Systems and methods for pacing rate programming

Abstract

Disclosed are methods and systems for outputting a baseline pacing rate, including: receiving a heart rate and a blood pressure of a patient; determining a first pacing rate to modify the heart rate based on the heart rate and the blood pressure; outputting the first pacing rate; receiving a subjective input subsequent to outputting the first pacing rate; determining a second pacing rate based on the subjective input; and outputting the second pacing rate; receiving a baseline pacing program subsequent to the heart rate matching the second pacing rate; determining a third pacing rate based on the baseline pacing program, and outputting the third pacing rate.

Description

SYSTEMS AND METHODS FOR PACING RATE PROGRAMMINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of US Provisional Patent Application No. 63 / 665,683 filed on June 28, 2024. The entirety of this application is hereby incorporated herein by reference.TECHNICAL FIELD

[0002] Various examples of the present disclosure relate generally to cardiac pacing based on at least one input, and more particularly, to systems and methods for outputting cardiac pacing rates based on one or more objective and / or subjective inputs.BACKGROUND

[0003] Hypertension (HTN) is a major contributor to cardiovascular mortality. Many patients with drugresistant hypertension (DRH) also require permanent pacing (PP). Hypertension treatment with a dualchamber pacemaker appears safe and effective at intermediate and long-term follow-up.

[0004] Generally, a pacing rate for a pacemaker may be determined by an internal algorithm of the pacemaker. The base rate of the pacemaker, or the lowest heart rate allowed, may be set by a cardiologist taking into account the output of rhythm studies and other tests or by a machine learning algorithm. Current approaches to treatment rely on a patient’s presence at a provider’s facility to allow for pacing rate adjustment.SUMMARY

[0005] A system for adaptive cardiac pacing. The system may include a physiological input measuring device configured to receive physiological data from a patient, the physiological data including a blood pressure, a pacing system including one or more defibrillators configured to deliver cardiac pacing to the patient, a memory configured to store one or more of a lookup table and a machine learning model, and a processor operatively coupled to the physiological input measuring device, the pacing system, and the memory.

[0006] The system may determine that the blood pressure is below a threshold baseline physiologic value and initiate a baseline pacing program. The baseline pacing program may include a baseline rate of cardiac pacing determined by one or more of manual input, the lookup table and the machine learning model. The system may determine that one or more of the blood pressure, in response to the baseline pacing program, is less than the threshold baseline physiologic value during a predetermined period of time and the baseline rate of cardiac pacing is below a baseline pacing threshold during the predetermined period of time. The system may determine that the blood pressure, in response to the baseline pacing program, is above a threshold upper physiologic value and terminate the baseline pacing program.

[0007] A method for adaptive cardiac pacing may include receiving, by a physiological input measuring device, physiological data from a patient, the physiological data including a blood pressure. A processormay determine that the blood pressure is below a threshold baseline physiologic value. A pacing system including one or more defibrillators configured to deliver cardiac pacing to the patient, may initiate a baseline pacing program. The baseline pacing program may include a baseline rate of cardiac pacing determined by one or more of manual input, a lookup table stored in a memory, and a machine learning model stored in the memory. The processor may determinine that one or more of the blood pressure, in response to the baseline pacing program, is less than the threshold baseline physiologic value during a predetermined period of time and the baseline rate of cardiac pacing is below a baseline pacing threshold during the predetermined period of time. The processor may determine that the blood pressure, in response to the baseline pacing program, is above a threshold upper physiologic value. The pacing system may terminate the baseline pacing program.

[0008] A system for adaptive cardiac pacing. The system may include a physiological input measuring device configured to receive physiological data from a patient, the physiological data including a blood pressure, a user device, a pacing system including one or more defibrillators configured to deliver cardiac pacing to the patient, a memory configured to store one or more of a lookup table and a machine learning model, and a processor operatively coupled to the physiological input measuring device, the user device, the pacing system, and the memory.

[0009] The system may determine that the blood pressure is below a threshold physiologic value and initiate a baseline pacing program. The baseline pacing program may include a first rate of cardiac pacing determined by one or more of manual input, the lookup table and the machine learning model. The system may determine that the blood pressure, in response to the baseline pacing program, is less than the threshold physiologic value after a first predetermined period of time. The system determine that the blood pressure remains below the threshold physiologic value for a predetermined number of consecutive checks. The system may adjust the first rate of cardiac pacing to a second rate of cardiac pacing. The system may receive a first user input via the user device after a second predetermined period of time. The user input may include a first indication of wellbeing in response to the second rate of cardiac pacing. The system may adjust the second rate of cardiac pacing to a third rate of cardiac pacing in response to the user input. The system may receive one or more additional user inputs via the user device after a third predetermined period of time, the one or more additional user inputs comprising one or more additional indications of wellbeing in response to the third rate of cardiac pacing. The system may determine that a predetermined number of consecutive additional user inputs of the one or more additional user inputs are negative indications of wellbeing. The system may terminate the baseline pacing program and initiate a default pacing program comprising a default rate of cardiac pacing.

[0010] A method for adaptive cardiac pacing. The method may include receiving, by a physiological input measuring device, physiological data from a patient. The physiological data may include a blood pressure. A processor may determine that the blood pressure is below a threshold physiologic value. A pacing system including one or more defibrillators configured to deliver cardiac pacing to the patient,may initiate a baseline pacing program. The baseline pacing program may include a first rate of cardiac pacing determined by one or more of manual input, the lookup table and the machine learning model. The processor may determine that the blood pressure, in response to the baseline pacing program, is less than the threshold physiologic value after a first predetermined period of time. The processor may determine that the blood pressure remains below the threshold physiologic value for a predetermined number of consecutive checks. The pacing system may adjust the first rate of cardiac pacing to a second rate of cardiac pacing. A first user input may be recieved by a user device after a second predetermined period of time. The user input may include a first indication of wellbeing in response to the second rate of cardiac pacing. The pacing system may adjust the second rate of cardiac pacing to a third rate of cardiac pacing in response to the user input. One or more additional user inputs may be received by the user device after a third predetermined period of time. The one or more additional user inputs may include one or more additional indications of wellbeing in response to the third rate of cardiac pacing. The processor may determine that a predetermined number of consecutive additional user inputs of the one or more additional user inputs are negative indications of wellbeing. The pacing system may terminatethe baseline pacing program and initiate a default pacing program comprising a default rate of cardiac pacing.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various examples and, together with the description, serve to explain the principles of the disclosed examples.

[0012] Aspects of the disclosure may be implemented in connection with examples illustrated in the attached drawings. These drawings show different aspects of the present disclosure and, where appropriate, reference numerals illustrating like structures, components, materials, and / or elements in different figures are labeled similarly. It is understood that various combinations of the structures, components, and / or elements, other than those specifically shown, are contemplated and are within the scope of the present disclosure. Moreover, there are many examples described and illustrated herein.

[0013] FIG. 1 depicts an exemplary environment for determining a pacing rate, according to one or more examples.

[0014] FIG. 2A depicts a flow diagram of an exemplary method for determining a pacing rate, according to one or more examples.

[0015] FIG. 2B depicts a flow diagram of an exemplary method for determining a baseline pacing rate, according to one or more examples.

[0016] FIG. 2C depicts a flow diagram of another exemplary method for determining a baseline pacing rate, according to one or more examples.

[0017] FIG. 2D depicts a lookup table, according to one or more examples.

[0018] FIG. 2E depicts a flow diagram of another exemplary method for determining a pacing rate, according to one or more examples.

[0019] FIG. 2F depicts a lookup table, according to one or more examples.

[0020] FIG. 3A depicts a flowchart of an exemplary method for determining a pacing rate, according to one or more examples.

[0021] FIG. 3B depicts a flowchart of an exemplary method for determining a pacing rate, according to one or more examples.

[0022] FIG. 4 depicts a further flow diagram of an exemplary method for determining a pacing rate via a pseudo-subjective machine learning model, according to one or more examples.

[0023] FIG. 5 depicts an example of training a machine learning model, according to one or more examples.

[0024] FIG. 6 depicts an example of a computing device, according to one or more examples.

[0025] Notably, for simplicity and clarity of illustration, certain aspects of the figures depict the general structure and / or manner of construction of the various examples. Descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring other features. Elements in the figures are not necessarily drawn to scale; the dimensions of some features may be exaggerated relative to other elements to improve understanding of the examples. For example, one of ordinary skill in the art appreciates that the side views are not drawn to scale and should not be viewed as representing proportional relationships between different components. The side views are provided to help illustrate the various components of the depicted assembly, and to show their relative positioning to one another.DETAILED DESCRIPTION

[0026] Various examples of the present disclosure relate generally to methods and systems for cardiac pacing rate programming.

[0027] As will be discussed in more detail below, in various examples, systems and methods are described for using machine learning to determine cardiac pacing rate programming. By training a machine-learning model, e.g., via supervised or semi-supervised learning, to learn associations between training data (e.g., physiological data) and ground truth data (e.g., cardiac pacing programs), the trained machine-learning model may be usable to output a pacing program for a patient.

[0028] Reference will now be made in detail to examples of the present disclosure. The present disclosure is not limited to any single aspect or example thereof, nor is it limited to any combinations and / or permutations of such aspects and / or examples. Moreover, each of the aspects of the present disclosure, and / or examples thereof, may be employed alone or in combination with one or more of the other aspects of the present disclosure and / or examples thereof. For the sake of brevity, certain permutations and combinations are not discussed and / or illustrated separately herein.

[0029] Notably, an example or implementation described herein as “exemplary” is not to be construed as preferred or advantageous, for example, over other examples or implementations. The term “exemplary” is used in the sense of “example” rather than “ideal.”

[0030] Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. In the discussion that follows, relative terms such as “about,” “substantially,” “approximately,” etc. are used to indicate a possible variation of ±10% in a stated numeric value.

[0031] In this disclosure, the term “based on” means “based at least in part on.” The singular forms “a,” “an,” and “the” include plural referents unless the context dictates otherwise.

[0032] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0033] The term “or” is used disjunctively, such that “at least one of A or B” includes, (A), (B), (A and A), (A and B), etc. Relative terms, such as, “substantially,” “approximately,” and “generally,” are used to indicate a possible variation of ±10% of a stated or understood value.

[0034] It will also be understood that, although the terms first, second, third, etc. are, in some instances, used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first contact could be termed a second contact, and, similarly, a second contact could be termed a first contact, without departing from the scope of the various described examples. The first contact and the second contact are both contacts, but they are not the same contact. In addition, the terms “first,” “second,” and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish an element or a structure from another. Moreover, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of one or more of the referenced items.

[0035] As used herein, the term “if’ is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.

[0036] Terms like “provider,” “medical provider,” or the like generally encompass an entity, person, or organization that may seek information, resolution of an issue, or engage in any other type of interaction with a user, e.g., to provide medical care, medical intervention or advice, or the like. Terms like “user,” “patient,” or the like generally encompass any person (e.g., an individual, a medical provider, etc.) or entity who is using a device, programming a device, obtaining information, seeking resolution of an issue, or the like.

[0037] Terms like “pacemaker” or the like generally encompass a device that may help control a user’s heartbeat, for example, to prevent the user’s heart from beating too fast or too slow. A pacemaker may include one or more sensors and / or one or more defibrillators, which may determine heart rate and provide electrical impulses, respectively. Terms like “pacing rate” or the like generally encompass the electrical pulses generated by the pacemaker and provided to one or more chambers of the heart to maintain an adequate heart rate. Terms like “base rate” or the like generally encompass the lowest heart rate allowed by a pacemaker. Terms like “lookup table” or the like generally encompass data that may determine the pacing rate and / or the base rate. A lookup table may be stored, e.g., in a database, in the form of a data table.

[0038] According to implementations of the disclosed subject matter, changes to a pacing rate and / or a base rate may be based on a lookup table. A lookup table may be a table with the patient attributes (e.g., height, weight, gender, medical condition, physiologic input, current state, etc.), objective inputs, subjective inputs, and / or the like and may be referenced to determine one or more pacing rates. Such a lookup table may be accessed by a pacemaker or a processing device in communication with a pacemaker.

[0039] According to implementations of the disclosed subject matter, cardiac pacing may be determined based on physiological inputs such as, but not limited to, blood pressure and / or heart rate, as further discussed herein. Such physiological input based cardiac pacing may be used to treat conditions such as, but not limited to, drug resistant hypertension (DRH), DRH with diastolic congestive heart failure (DCHF), heart failure with preserved ejection fraction (HFpEF), etc.

[0040] Blood pressure may be detected using a blood pressure measuring device (a “device” or a “blood pressure device”). A blood pressure may be a sensed value, a blood pressure, a sensed value converted into one or more other formats (e.g., by a processor), or the like. A blood pressure may indicate how much pressure a user’s blood exerts against the user’s artery walls when the user’s heart beats (e.g., a systolic blood pressure). A blood pressure may indicate how much pressure a user’s blood exerts against the user’s artery walls when the user’s heart is resting between beats (e.g., diastolic blood pressure).

[0041] A blood pressure measuring device may include any type of blood pressure monitor or cuff such as, for example, a pneumatic cuff relying on mechanical compression of a peripheral artery cuff (e.g., to be attached to brachial artery, ankle, wrist, etc.), a non-pneumatic cuff (e.g., which analyzes an arterial waveform and function anywhere on the body where the arterial pulse contour can be sensed such as at a wrist), or an implantable sensor within a blood vessel or heart chamber. A blood pressure measuring device may be a light-based device such as a photoplethysmography (PPG) device. A blood pressure measuring device may output blood pressure in a first format which may be converted to a second format such that a processing component receiving blood pressure information may be configured to utilize such information in the second format and may not be configured to utilize such information in the first format.

[0042] Physiological inputs, as discussed herein, include, but are not limited to, a blood-pressure, heart rate, biomarker level (e.g., cortisol, atrial natriuretic peptide (ANP), B-type natriuretic peptide (BNP), N-terminal pro b-type natriuretic peptide (NT-proBNP), etc.), blood oxygen level, glucose level, blood electrolytes level, an accelerometer value, respiratory rate sensor value (e.g., via diaphragmatic movement), thoracic impedance, impedance (e.g., as a correlate of right ventricular function), environmental parameter, ambient oxygen concentration (e.g., SP02), humidity, portions of cardiac rate such as atrial rate, ventricular rate, atrioventricular conduction, the presence of rhythm irregularities, autonomic nervous system (ANS) function, glucose, skin electrolytes, galvanic skin response, PPG values, Electroencephalogram (EEG) wave, urination parameters, etc. Such physiological inputs may be provided by one or more sensors, devices, or the like. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) may be sensed by one or more blood pressure sensing devices.

[0043] According to implementations of the disclosed subject matter, cardiac pacing may be determined based on environmental parameters. Such environmental parameters include one or more of the patient’s diet, a time of day, an ambient temperature, the patient’s location, an ambient oxygen concentration, and / or a humidity.

[0044] Additionally, cardiac pacing may be determined based on subjective input from the patient. This subjective input can include emotional parameters, such as the patient’s (or a patient’s provider’s) reporting of a state of emotional well-being, physical well-being, comfort level, etc. Alternatively, cardiac pacing may be determined by a medical provider or by a machine learning algorithm trained to begin, maintain, modify, and / or end cardiac pacing. In instances where cardiac pacing is determined by a machine learning algorithm, a medical provider or other professional may manually indicate the use of the machine learning algorithm for pacing, or the pacing by the machine learning algorithm may be initiated, performed, or terminated automatically.

[0045] As discussed herein, cardiac pacing may be based on physiological parameters, environmental parameters, and / or subjective / emotional parameters. It will be understood that such parameters include changes to such parameters. For example, cardiac pacing may be based one or more of a change such as a change in clinical status, a change in medication, a change in other physiologic parameters, a change in other diagnostic testing such as in vitro diagnostics (e.g., blood tests and the like), changes based on procedures during and / or after surgery, endoscopy, cardiac ablation, renal denervation, etc.

[0046] As used herein, a “machine-learning model” generally encompasses instructions, data, or a model configured to receive input, and apply one or more of a weight, bias, classification, or analysis on the input to generate an output. Accordingly, techniques disclosed herein may be implemented to determine or modify pacing (e.g., by a pacing device such as a pacemaker) based on physiological, environmental, and / or subjective inputs. A pacing output may be modified in accordance with an algorithm or machine learning output. The output may include, for example, a classification of the input, an analysis based on the input, a design, process, prediction, or recommendation associated with theinput, or any other suitable type of output. A machine-learning model is generally trained using training data, e.g., experiential data or samples of input data, which are fed into the model in order to establish, tune, or modify one or more aspects of the model, e.g., the weights, biases, criteria for forming classifications or clusters, or the like. Aspects of a machine-learning model may operate on an input linearly, in parallel, via a network (e.g., a neural network), or via any suitable configuration. By virtue of such training, a machine-learning model is converted from an un-trained and un-specific model to a model that is unique to and specifically configured for the particular purpose for which it is trained. In an example, training of a machine-learning model is analogous to a method of production in which the article produced is the trained model having unique characteristics by virtue of its particular training. Moreover, the result of training a machine-learning model using particular training data and for a particular purpose results in a technical solution to an inherently technical problem.

[0047] The execution of the machine-learning model may include deployment of one or more machine learning techniques, such as linear regression, logistical regression, random forest, gradient boosted machine (GBM), deep learning, or a deep neural network. Supervised or unsupervised training may be employed. For example, supervised learning may include providing training data and labels corresponding to the training data, e.g., as ground truth. Unsupervised approaches may include clustering, classification or the like. K-means clustering or K-Nearest Neighbors may also be used, which may be supervised or unsupervised. Combinations of K-Nearest Neighbors and an unsupervised cluster technique may also be used. Any suitable type of training may be used, e.g., stochastic, gradient boosted, random seeded, recursive, epoch or batch-based, etc.

[0048] For example, pacing may be modified to improve a blood pressure related condition by increasing or decreasing blood pressure, via determined cardiac pacing outputs, based on observed biomarker levels. The modification may result an increase in a cardiac pacing rate or amplitude, a decrease in cardiac pacing rate or amplitude, an acceleration of a cardiac pacing rate, a deceleration of a cardiac pacing rate, and / or the like. Such modified pacing may, at least in part, improve a given medical conditions for a patient (e.g., a blood pressure condition). Such conditions may include, for example, hypertension, hypotension, DRH, DRH with diastolic congestive heart failure (DCHF), HFpEF, and / or the like.

[0049] Presented below are various aspects of machine learning techniques that may be adapted to determine cardiac pacing instructions. As will be discussed in more detail below, machine learning techniques may be adapted to output a cardiac pacing rate or program, may include one or more aspects according to this disclosure, e.g., a particular selection of training data, a particular training process for the machine-learning model, operation of a particular device suitable for use with the trained machinelearning model, operation of the machine-learning model in conjunction with particular data, modification of such particular data by the machine-learning model, etc., or other aspects that may be apparent to one of ordinary skill in the art based on this disclosure.

[0050] FIG. 1 shows an exemplary environment 100 for a closed- loop system for determining an adjusted physiologic pacing rate, according to one or more examples. In some examples, the systems and devices of the environment 100 may communicate in any arrangement. As will be discussed herein, systems or devices of the environment 100 may communicate in order to one or more of generate, train, or use a machine-learning model to determine a physiologic pacing rate, among other activities.

[0051] Such a pacing rate may be determined, for example, upon detection of a medical condition such as Hypertensive Heart Disease and / or may be determined independent of a medical condition (e.g., based on one or more physiological inputs, subjective inputs, environmental inputs, and / or the like). Physiological pacing rates may include any applicable properties for cardiac pacing such as, but not limited to, frequency of pacing, amplitude of pacing, duration of pacing, acceleration of pacing, deceleration of pacing, etc.

[0052] Environment 100 of FIG. 1 depicts at least one physiological input measuring device 110, a component 120 to receive the input (e.g., an electronic device, a patient device, a network device, a cloud device, etc.), and pacing system 130. Some or all of these components may be connected via a network 140.

[0053] Physiological input measuring device 110 may include one or more sensors such as, but not limited to, a blood-pressure sensors, heart rate sensors, biomarker level sensors, blood oxygen level sensors, glucose level sensors, blood electrolytes level sensors, an accelerometer, motion sensors, position sensors, respiratory rate sensors, impedance sensors, environmental sensors, ambient oxygen condition sensors, humidity sensors, ANS sensors, glucose sensors, skin electrolytes sensors, galvanic skin sensors, PPG sensors, EEG sensors, EKG sensors, fluid sensors, volume sensors, light sensors, cameras, and / or the like.

[0054] One or more of the components of environment 100 of FIG. 1 may communicate with each other and / or other systems, e.g., via network 140. In some examples, network 140 may connect one or more components of environment 100 via a wired connection. In some examples, network 140 may connect one or more aspects of environment 100 via an electronic network connection, for example a wide area network (WAN), a local area network (LAN), personal area network (PAN), or the like. In some examples, the electronic network connection includes the internet, and information and data provided between various systems occurs online. “Online” may mean connecting to or accessing source data or information from a location remote from other devices or networks coupled to the Internet. Alternatively, “online” may refer to connecting or accessing an electronic network (wired or wireless) via a mobile communications network or device. The Internet is a worldwide system of computer networks — a network of networks in which a party at one computer or other device connected to the network may obtain information from any other computer and communicate with parties of other computers or devices. The most widely used part of the Internet is the World Wide Web (often- abbreviated “WWW” or called “the Web”). A “website page,” a “portal,” or the like generallyencompasses a location, data store, or the like that is, for example, hosted and / or operated by a computer system so as to be accessible online, and that may include data configured to cause a program such as a web browser to perform operations such as send, receive, or process data, generate a visual display and / or an interactive interface, or the like. In any case, the connections within the environment 100 may be network, wired, any other suitable connection, or any combination thereof.

[0055] In an exemplary example, pacing system 130 may be used to generate or train a machine-learning model. For example, such a system may include instructions for generating the machine-learning model, the training data and ground truth, or instructions for training the machine-learning model. A resulting trained-machine-leaming model may then be provided to the pacing system 130.

[0056] Generally, a machine -learning model includes a set of variables, e.g., nodes, neurons, filters, etc., that are tuned, e.g., weighted or biased, to different values via the application of training data. In supervised learning, e.g., where a ground truth is known for the training data provided, training may proceed by feeding a sample of training data into a model with variables set at initialized values, e.g., at random, based on Gaussian noise, a pre-trained model, or the like. The output may be compared with the ground truth to determine an error, which may then be back-propagated through the model to adjust the values of the variable. In unsupervised learning, patterns, correlations, or clusters of input samples may be used to determine one or more metrics or features of the samples usable to differentiate between related subsets of the samples. In semi-supervised learning, unsupervised and supervised approaches may be combined.

[0057] Training may be conducted in any suitable manner, e.g., in batches, and may include any suitable training methodology, e.g., stochastic or non-stochastic gradient descent, gradient boosting, random forest, etc. In some examples, a portion of the training data may be withheld during training or used to validate the trained machine-learning model, e.g., compare the output of the trained model with the ground truth for that portion of the training data to evaluate an accuracy of the trained model. The training of the machine-learning model may be configured to cause the machine-learning model to learn associations between [training] data and [ground truth] data, such that the trained machine-learning model is configured to determine an output [claim] in response to the input [claim] data based on the learned associations. Particular selection or application of training data, such as discussed in various examples of this disclosure, may inhibit or reduce impact of concerns such as biasing (e.g., via selection, truncation, or the like), overfitting, under-fitting, etc.

[0058] In some instances, training using one set or type of data may be used or adapted to another set of data. For example, a modal initially trained on one data set may require less samples or time to train on a second data set. In another example, initial training may result in a base model that may be tuned with an additional data set so as to form a particularized model specific to circumstances of the additional data set.

[0059] In various examples, the variables of a machine-learning model may be interrelated in any suitable arrangement in order to generate the output. For example, the machine-learning model may include one or more convolutional neural network (“CNN”) configured to identify features in the physiological data, and may include further architecture, e.g., a connected layer, neural network, etc., configured to determine a relationship between the identified features in order to determine a location in the physiological data.

[0060] In some instances, different samples of training data or input data may not be independent. For example, training data from different testing, trials, programs, and the like may include samples of training data obtained by different entities and for different purposes. Thus, in some examples, the machine-learning model may be configured to account for or determine relationships between multiple samples.

[0061] For example, in some examples, the machine-learning model of the pacing system 130 may include a Recurrent Neural Network (“RNN”). Generally, RNNs are a class of feed-forward neural networks that may be well adapted to processing a sequence of inputs. In some examples, the machinelearning model may include a Long Shor Term Memory (“LSTM”) model or Sequence to Sequence (“Seq2Seq”) model. An LSTM model may be configured to generate an output from a sample that takes at least some previous samples or outputs into account. A Seq2Seq model may be configured to, for example, receive a sequence of non-optical in vivo images as input, and generate a sequence of locations, e.g., a path, in the medical imaging data as output.

[0062] In a reinforcement learning model, environmental data (e.g., data describing a current state of a system) is evaluated using a policy in order to determine a next action of an agent. A scoring function or metric is usable to objectively quantify a state of the environment, e.g., to evaluate whether the action of the agent was desirable or not. The policy may include, for example, one or more tunable metrics or any suitable machine learning architecture such as a neural network in which the output nodes correspond to possible actions of the agent. In some instances, the policy includes a recurrent network structure or the like that obtains or retains data on previous actions of the agent or states of the environment. Training the reinforcement model may include, for example, a random forest of policy perturbations (e.g., to control for a final score outcome), weighting of policy parameters via back propagation based on a score for a state of the environment after performance of a particular action, etc.

[0063] In an adversarial network, a network may be trained using the output of a different model, rather than or in addition to using ground truth. For example, output of a mathematical (formula-based) model or a different machine learning model may be compared with output from a model to be trained. In an example, the difference in the results may be back propagated. In another example, the comparison may include a scoring, and instances in which the model to be trained outperformed the adversary may cause reinforcement of the model.

[0064] Any suitable type of machine learning model or combination of machine learning models may be used. Operations conducted by one model in some examples may be distributed amongst a plurality of models in other examples, or vice versa.

[0065] Although depicted as separate components in FIG. 1, it should be understood that a component or portion of a component in the environment 100 may, in some examples, be integrated with or incorporated into one or more other components. For example, a portion of the physiological input measuring device 110 may be integrated into the component 120 or the like. In another example, the pacing system 130 may be integrated with a data storage system (not shown). In some examples, operations or aspects of one or more of the components discussed above may be distributed amongst one or more other components. Any suitable arrangement or integration of the various systems and devices of the environment 100 may be used.

[0066] According to implementations of the disclosed subject matter, one or more systems or methods disclosed herein may be utilized for pacing rate programming. FIG. 2A depicts a flow diagram 200 determine (e.g., program) one or more cardiac pacing rates. It will be understood that the steps described in reference to FIG. 2A are an example only. Steps shown in FIG. 2A may be performed in the order described herein or in any applicable order. Further, for simplicity, flow diagram 200 is provided in reference to blood pressure data. However, it will be understood that any physiologic input (e.g., as discussed herein) may be substituted for blood pressure in accordance with the techniques discussed in reference to flow diagram 200.

[0067] As shown in FIG. 2A, the process of flow diagram 200 may start at step 202. Physiologic property data (e.g., blood pressure data) of a subject may be received at step 204. For example, blood pressure data may be received via physiological input measuring device 110 based on one or more sensor inputs detected at physiological input measuring device 110. Physiological property data, at step 204, may be received at, for example, component 120 via network 140. As discussed herein, physiological property data received at step 204 may be determined in a first format associated with physiological input measuring device 110 and may be converted into a second format to be utilized by component 120. Physiological property data received at step 204 may be converted from the first format to the second format via physiological input measuring device 110 and / or via component 120. According to an implementation, component 120 may be part of, associated with, or in communication with pacing system 130.

[0068] At step 206, subject information of a given subject may be received. Such subject information may include, for example, patient attributes (e.g., demographic attributes, height, weight, ethnicity, medical conditions, medication information, etc.). Subject information may be provided via physiological input measuring device 110 (which may be the same as or different than the physiological input measuring device that provides blood pressure information at step 204). Alternatively, or in addition, subject information may be provided via component 120 (e.g., via user input or storageassociated with component 120) and / or may be provided via a separate component (e.g., a remote component, database, server, electronic medical record program, etc.).

[0069] At step 212, a determination may be made regarding whether a physiologic property (e.g., SBP) associated with the input received at step 204 meets or exceeds a threshold physiologic value (e.g., if SBP is greater than approximately 130 mmHg). The threshold physiologic value may be an upper bound of an acceptable physiologic value range (e.g., an acceptable SBP range). If the physiologic property does not meet or exceed the threshold physiologic value, then step 210 may be performed. At step 210, a determination may be made whether the physiologic property is below a second threshold physiologic value (e.g., if SBP is less than approximately 110 mmHg). The second threshold physiologic value may be a lower bound of an acceptable physiologic value range (e.g., an acceptable SBP range). If the physiologic property is not below the second threshold physiologic value, then step 208 may be performed and the process of flow diagram 200 may be terminated. Accordingly, step 208 may be performed to terminate the process if the physiologic property (e.g., blood pressure) received at step 204 is within an acceptable physiologic value range. For example, the physiologic property data being in such an acceptable range may not require an adjusted pacing rate.

[0070] Returning to step 210, if the physiologic property is below the second threshold physiologic value (e.g., lower than the acceptable physiologic value range), step 214 may be performed. For example, as shown in flow diagram 200, if systolic blood pressure is less than approximately 110 mmHg, an existing pacing rate may be adjusted to be a predetermined or dynamically determined amount (e.g., approximately 5% of a patient heart rate or physiologic property such as blood pressure) lower. The pacing rate may be adjusted to be lowered by the amount in comparison to the existing pacing rate and / or may be adjusted to be lowered in comparison to a heart rate (e.g., a heart rate detected by an ECG sensor).

[0071] According to an example, the pacing rate may be adjusted based on an output of a pacing machine learning model. The pacing machine learning model may be trained in accordance with the techniques disclosed herein. For example, the pacing machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and / or the like. The pacing machine learning model may receive, as inputs, one or more of the physiologic input value received at step 204, the subject information received at step 206, the acceptable physiologic value range applied at step 212 and step 210 (e.g. , as output by a threshold machine learning model), and / or the like. The pacing machine learning model may output a pacing rate (e.g., an actual rate, a percentage change, a ratio, etc.) based on the inputs.

[0072] Next, at step 218A, a subjective input may be received from the subject. For example, a patient may be provided a prompt (e.g., via a graphical user interface) requesting the patient to provide an assessment of his or her own wellbeing. According to an implementation of the disclosed subject matter,the subject may be provided a prompt, via a graphical interface, comprising graphical components generated based on one or more of the physiologic property received or determined based on the input received at step 204, based on the amount of deviation of the physiological property from the second threshold physiologic value, and / or the like. For example, the prompt may include larger icons for receiving the subjective input if the amount of deviation of the physiological property from the second threshold physiologic value is above a threshold amount. An order of the requested input may be determined based on the one or more of the physiologic property received or determined based on the input received at step 204, based on the amount of deviation of the physiological property from the second threshold physiologic value, and / or the like. For example, a graphical component corresponding to the subject feeling ill may be ordered above a graphical component corresponding to the subject feeling healthy if the amount of deviation of the physiological property from the second threshold physiologic value is above a threshold amount.

[0073] If, at step 218A, a positive indication is received indicating that the subject is feeling the same or better than prior to the pacing rate adjustment at step 214, a waiting period of a predetermined or dynamically determined time period (e.g., 3 minutes) may be implemented at step 220A. Upon expiration of the time period, another subjective input may be received from the subject at step 218B. According to implementations of the disclosed subject matter, a dynamically determined time period may be determined using an algorithm or time-based machine learning model. The time-based machine learning model may be trained in accordance with the techniques disclosed herein. For example, the time-based machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, and / or the like. The time-based machine learning model may receive, as inputs, one or more of the physiologic input value received at step 204, the subject information received at step 206, the acceptable physiologic value range applied at step 212 and step 210, and / or the like. The time-based machine learning model may output a dynamically determined time period such that, for example, the dynamically determined time period provides sufficient time for the subject to notice an effect of the pacing rate change implemented at step 214. Further, the output dynamically determined time period may not exceed a time period such that the likelihood of the subject being effected by external factors other than the pacing rate change is below an acceptable threshold likelihood. Accordingly, the time-based machine learning model may, at least in part, determine a likelihood of the subject being effected by external factors.

[0074] If, at step 218B, a positive indication is received indicating that the subject is feeling better or the same as prior to step 220A, the process may be considered successful and may terminate at step 224. It will be understood that indications (e.g., via subjective inputs received at steps 218A, 218B, 218C, 218D, 218E, and / or 218F) may be provided via an interface in a manner similar to that discussed in reference to step 218A.

[0075] If at step 218 A, a negative indication is received indicating that the subject is feeling worse, then, at step 226A, the pacing rate will revert to the same pacing rate at step 202, and the appointment will end 208A. Alternatively, at step 226A, the pacing rate may be adjusted to an intermediate rate (e.g., a rate higher than the pacing rate at step 202 but lower than the pacing rate at step 222).

[0076] Similarly, if at step 218B a negative indication is received indicating that the subject is feeling worse, then the pacing rate will revert to the same pacing rate at step 202, and the appointment will end 208A. Alternatively, at step 226B, the pacing rate may be adjusted to an intermediate rate (e.g., a rate higher than the pacing rate at step 202 but lower than the pacing rate at step 222

[0077] Still referring to FIG. 2A, if the physiological property value (e.g., systolic blood pressure) at step 212 is greater than the threshold amount (e.g., approximately 130 mmHg), the pacing rate may be adjusted to be raised by a predetermined or dynamically determined amount (e.g., 10%) at step 216. The pacing rate may be adjusted by the amount in comparison to the existing pacing rate and / or may be adjusted to be lowered in comparison to a heart rate (e.g., a heart rate detected by an ECG sensor).

[0078] Next, at step 218C, a subjective input may be received from the subject. If, at step 218C, a positive indication is received indicating that the subject is feeling the same or better than prior to the pacing rate adjustment at step 216, a waiting period of a predetermined or dynamically determined time period (e.g., approximately 3 minutes) may be implemented at step 220B. Upon expiration of the time period, another subjective input may be received from the subject at step 218D.

[0079] If, at step 218D, a positive indication is received indicating that the subject is feeling better or the same as prior to step 220B, the process may be considered successful and may terminate at step 224. It will be understood that indications (e.g., via subjective inputs received at steps 218A, 218B, 218C, 218D, 218E, and / or 218F) may be provided via an interface in a manner similar to that discussed in reference to step 218A.

[0080] If at step 218C, a negative indication is received indicating that the subject is feeling worse, then, the pacing rate may be adjusted to be raised by a predetermined or dynamically determined amount (e.g., 5%) at step 222, as discussed herein. Similarly, if at step 218D a negative indication is received indicating that the subject is feeling worse, then the pacing rate may be adjusted to be raised by a predetermined or dynamically determined amount (e.g., 5%) at step 222, as discussed herein.

[0081] Next, at step 218E, a subjective input may be received from the subject. If, at step 218E, a positive indication is received indicating that the subject is feeling the same or better than prior to the pacing rate adjustment at step 222, a waiting period of a predetermined or dynamically determined time period (e.g., approximately 3 minutes) may be implemented at step 220C. Upon expiration of the time period, another subjective input may be received from the subject at step 218F. If, at step 218F, a positive indication is received indicating that the subject is feeling better or the same as prior to step 222, the process may be considered successful and may terminate at step 224.

[0082] If at step 218E, a negative indication is received indicating that the subject is feeling worse, then, at step 226B, the pacing rate will revert to the same pacing rate at step 202, and the process may terminate at step 208B. Alternatively, at step 226B, the pacing rate may be adjusted to be higher than the pacing rate at step 202 but lower than the pacing rate at step 222. Similarly, if at step 218F a negative indication is received indicating that the subject is feeling worse, then the pacing rate will revert to the same pacing rate at step 202, and the process may terminate at step 208B. Alternatively, at step 226B, the pacing rate may be adjusted to be higher than the pacing rate at step 202 but lower than the pacing rate at step 222.

[0083] As discussed herein, a dose-response relationship to modifying a pacing rate (e.g., an atrial pacing rate) is provided herein. Accordingly, a pacing rate output to a pacing system may be based on a current or prior pacing rate. The output pacing rate may be a percentage or ratio of the current or prior pacing rate. The percentage or ratio may be predetermined (e.g., approximately 5%, approximately 10%, etc.) and / or may be determined based on or more factors discussed herein (e.g., via an algorithm or a pacing machine learning algorithm). Accordingly, for a first patient, the output pacing rate may be a percentage or ratio that is different for a second patient. As another example, for a first patient, the output pacing rate may be a percentage or ratio at a first time that is different than the output pacing rate for the first patient at a second time (e.g., based on a change in the patient information, physiological factors, etc.) In some examples, the adjustment of the base pacing rate or the atrial pacing rate may be adjusted based on a patient’s tolerance and / or may not exceed the pacing maximum allowed by physiologic or safety guardrails (e.g., approximately 88 bpm). Such physiologic or safety guardrails may be determined based on patient information, device characteristics, safety regulations, and / or the like.

[0084] According to examples of the disclosed subject matter, the threshold and / or the second threshold (e.g., as described in reference to step 212 and / or step 210) may be output by a threshold machine learning model. The threshold machine learning model may be trained in accordance with the techniques disclosed herein. For example, the threshold machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, changes in physiological values, and / or the like. The threshold machine learning model may receive, as inputs, one or more of the physiologic input value received at step 204, the subject information received at step 206, and / or the like. The threshold machine learning model may output the threshold and / or second threshold, either of which may be patient specific. According to an example, an output of the threshold machine learning model may be provided as an input into the time-based machine learning model which may provide an output, as discussed herein, at least in part of the threshold machine learning model output.

[0085] According to an example of the disclosed subject matter, a complex lookup table may be accessed to determine a pacing rate. A primary lookup variable for the complex lookup table may beone or more physiological parameter values (e.g., blood pressure values) which may include reference systolic / diastolic numbers associated with respective blood pressure readings. Each pair of systolic / diastolic numbers may correspond to an applicable pacing rate. Hence, in response to an input physiological parameter value (e.g., blood pressure reading), the device and / or algorithm implementing the change of pacing rate may perform a look up of the new pacing rate to be programmed. An implementation of the complex lookup table (lookup table) may include a function that retrieves data from a pre-programmed and / or pre-populated array of data. For example, such pre-programmed and / or pre-populated array of data may be populated using the threshold machine learning model discussed herein. According to this example, the threshold machine learning model may be provided physiological parameter values as an input, and may generate corresponding pacing rates to populate the lookup table. For example, blood pressure (BP) may be looked up and a pacing rate may be determined using a device, based on a populated lookup table. An exemplary lookup table, such as the one shown in FIG. 2D as Table 250, will be discussed in greater detail below.

[0086] FIG. 2B and FIG. 2C include flow diagrams related to two different modes for baseline pacing. Either mode, FIG. 2B or FIG. 2C, may be selected manually by a provider or may be activated automatically such as via an output of a machine learning model (e.g., such as one or more machine learning models discussed herein).

[0087] As applied herein, baseline pacing refers to a reduced pacing mode that may be triggered based on one or more factors discussed herein. Baseline pacing may provide a lower pacing rate than the one or more pacing rates discussed in reference to FIG. 2A. For example, a baseline pacing rate may be between approximately 1% and approximately 5% of a patient heart rate or physiological value such as blood pressure. Baseline pacing may provide a lower level of cardiac pacing that causes a patient physiological value to reach a target range over a period of time. In comparison to the pacing discussed in reference to Fig. 2A, baseline pacing may be a guarded level of pacing to cause the patient physiological value to reach the target range over a period of time.

[0088] As an example, some patients that are hypertensive may spontaneously have a drop in systolic and / or diastolic blood pressure. Such a drop may be experienced, for example, during a placebo phase of a treatment. Such a drop may be due to any number of factors, such as the patient being observed, taking medication at a higher frequency or dose, or becoming more comfortable with closer observation by trained medical staff.

[0089] Baseline pacing is a subroutine to the pacing algorithms discussed herein. As an example, baseline pacing may be implemented for subjects who have HFpEF and have an SBP less than a lower threshold (e.g., 135 mmHg) so that such patients are also provided a pacing output, using the baseline pacing subroutine instead of the pacing routine discussed in reference to FIG. 2A.

[0090] As an illustrative example, if the SBP for a given patient remains below 135 mmHg for a threshold duration (e.g., approximately 3 days) or the degree of pacing remains below a threshold level(e.g., below approximately 5%) for a threshold duration (e.g., approximately 3 days), the system will continually pace the subject at a baseline pacing rate n% (e.g., approximately 5%) until the blood pressure rises above approximately 130 mmHg to approximately 135 mmHg systolic. According to an implementation, no pacing may be provided when the blood pressure reaches an acceptable threshold (e.g., 130 mmHg) as that blood pressure may be considered optimal. In such an implementation, 0% pacing may be implemented while the patient is within the optimal blood pressure range.

[0091] As another example, if a given patient’s SBP is less than a lower threshold (e.g., approximately 135 mmHg) for a threshold number of consecutive time blocks (e.g., three consecutive twelve hour blood pressure checks), the system may enter baseline pacing and a given percent (e.g., 5% increase in right atrial pacing). A subjective or pseudo subjective input (e.g., via a prompt or automated technique as discussed herein) may be received. For example, the given patient may be asked “How do you feel?” If the subjective input or pseudo subjective input is a positive indication, the process may be repeated for a duration of time (e.g., 12 hours). If the subjective input or pseudo subjective input is a negative indication, the system may lower the pacing rate to a lower pacing rate (e.g., to 2.5%) and repeat the process. If a negative indication is received based on the lowered pacing, the system may maintain the lower pacing rate and repeat the process after a duration of time (e.g., in 12 hours). After the process is repeated a threshold number of times (e.g., three times) after provision of one or more lowered pacing rates (e.g., between 0% and 5%), a default pacemaker programming may be triggered for a given amount of time or until a certain blood pressure is reached, before the pacing algorithms discussed herein are activated.

[0092] According to examples of the disclosed subject matter, one or more thresholds discussed herein may be determined, adjusted, or otherwise modified by a user (e.g., a medical clinician) and / or based on an output form a machine learning model such as one or models discussed herein. For example, a user may adjust a physiological value threshold (e.g., 130 mmHg blood pressure threshold lowered to 120 mmHg) based on, for example, a patient’s response to cardiac pacing.

[0093] As shown in FIG. 2B, the baseline pacing process of flow diagram 230 may start at step 231, and may be a continuation after the completion of step 224 from FIG. 2A and / or may be triggered during one or more steps of FIG. 2A. At step 231, a determination may be made regarding whether a physiologic property (e.g., SBP) associated with an input, such as the physiologic property (e.g., SBP) received at step 204 of FIG. 2A, falls below a threshold baseline physiologic value (e.g., if SBP is less than approximately 130 mmHg). The threshold physiologic value may be determined by a machine learning model or be manually provided by the provider. The threshold baseline physiological value may be a lower bound of an acceptable physiologic value range (e.g., an acceptable SBP range).

[0094] According to an implementation, a threshold baseline physiological value may be determined by a baseline value machine learning model. The baseline value machine learning model may be trained by modifying one or more layers, weights, biases, synapses, nodes and / or the like. The baseline valuemachine learning model may be trained using training data that include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, pseudo subjective inputs and / or the like. The training data may be tagged such that the baseline value machine learning model may correlate components of the training data. A trained baseline value machine learning model may receive inputs such as one or more of physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and pseudo subjective inputs of a given patient, and may output a threshold baseline physiological value for that patient based on such inputs. For example, a first healthy patient below a given age may have a first threshold baseline blood pressure value which may be different relative to a second threshold baseline blood pressure value of a second unhealthy patient above the given age.

[0095] Continuing with FIG. 2A, if the physiologic property falls below the threshold physiologic value, then the process may continue to step 232. Alternatively, if the lower bound of an acceptable physiologic value continues be met (e.g., if the SBP is greater than 130 mmHg and below an upper acceptable SBP value), the process continues to monitor the physiologic property until the physiologic property falls below the threshold physiologic value.

[0096] According to an example, the baseline pacing program may include adjusting the pacing rate based on an output of a pacing machine learning model. The pacing machine learning model may be trained in accordance with the techniques disclosed herein. For example, the pacing machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and / or the like. The pacing machine learning model may receive, as inputs, one or more of the physiologic input values, subject information, acceptable physiologic value ranges (e.g., as output by a threshold machine learning model), and / or the like. The baseline pacing machine learning model may output a pacing rate (e.g., an actual rate, a percentage change, a ratio, etc.) based on the inputs.

[0097] Step 232 may be performed which includes starting a baseline pacing program. The baseline pacing program may be percentage based (e.g., a percentage based on a physiologic property). Physiologic property data (e.g., blood pressure data) of a subject may be received at step 233. For example, blood pressure data may be received via physiological input measuring device 110 based on one or more sensor inputs detected at physiological input measuring device 110. Physiological property data, at step 233, may be received at, for example, component 120 via network 140. As discussed herein, physiological property data received at step 233 may be determined in a first format associated with physiological input measuring device 110 and may be converted into a second format to be utilized by component 120. Physiological property data received at step 233 may be converted from the first format to the second format via physiological input measuring device 110 and / or via component 120. Accordingto an implementation, component 120 may be part of, associated with, or in communication with pacing system 130.

[0098] At step 234, a determination may be made regarding whether a physiologic property (e.g., SBP) associated with the input received at step 233 falls below a threshold baseline physiologic value (e.g., if SBP is less than approximately 130 mmHg) during a first time duration (e.g., 3 days). The threshold baseline physiologic value may be, for example, a lower bound of an acceptable physiologic value range (e.g., an acceptable SBP range). The first time duration may be a pre-determined duration of time (e.g., a measurable length of time). If the physiologic property falls below the threshold physiologic value during the pre-determined duration of time, then step 235 may be performed.

[0099] At step 235, a determination may be made as to whether the baseline pacing percentage is less than a baseline pacing threshold (e.g., if the pacing percentage is 5% or less) for the pre -determined duration of time (e.g., for the duration of 3 days). The baseline pacing percentage may be an upper bound of an acceptable baseline pacing percentage (e.g., an acceptable pacing amount). If the baseline pacing percentage has been lower than the baseline pacing threshold for the pre-determined duration of time, then step 236 may be performed which includes continuing with the baseline pacing program. Continuing the baseline pacing program may include pacing at the baseline pacing threshold or may include lowering the baseline pacing value as discussed herein.

[0100] Alternatively, at step 234, it may be determined that the physiologic property (e.g., SBP) has remained below the threshold baseline physiologic value (e.g., less than approximately 130 mmHg) during the first time duration (e.g., 3 days). In this instance, step 237 is performed.

[0101] In another example, it may be determined that at step 234 the physiologic property (e.g., SBP) has not remained below the threshold physiologic value (e.g., less than approximately 130 mmHg) during the first time duration (e.g., 3 days), but at step 235 it is determined that the baseline pacing percentage has been less than the baseline pacing threshold for the pre-determined duration of time. In this example, step 237 is performed.

[0102] Step 237 may include determining whether a physiologic property (e.g., SBP) associated with the input received at step 233 is above a threshold physiologic value (e.g., if SBP is greater than approximately 130 mmHg). The threshold physiologic value may an upper bound of an acceptable physiologic value range (e.g., an acceptable SBP range). If the physiologic property is not above the threshold physiologic value step 238 may be performed which includes continuing with the baseline pacing program. This loop may continue until, at step 237, the physiologic property is determined to be above the threshold physiologic value. If it is determined at step 237 that the physiologic property is above the threshold physiologic value, step 239 may be performed which includes ending the baseline pacing program.

[0103] According to an example, as shown in FIG. 2C, the process of flow diagram 240 may start at step 241a, following the completion of step 224 from FIG. 2A and / or may be triggered during one ormore steps of FIG. 2A. At step 241a, a determination may be made regarding whether a physiologic property (e.g., SBP) associated with an input, such as the physiologic property (e.g., SBP) received at step 204 of FIG. 2A, falls below a threshold physiologic value (e.g., if SBP is less than approximately 130 mmHg). The threshold physiologic value may be a lower bound of an acceptable physiologic value range (e.g., an acceptable SBP range). If the physiologic property falls below the threshold physiologic value, then the process may continue to step 242. Alternatively, if the lower bound of an acceptable physiologic value continues be met (e.g., if the SBP is greater than 130 mmHg), the process continues to monitor the physiologic property until the physiologic property falls below the threshold physiologic value.

[0104] Step 242 may be performed which includes starting a baseline pacing program. The baseline pacing program may be percentage based (e.g., a percentage based on a physiologic property). The baseline pacing program may include continually receiving physiologic property data (e.g., blood pressure data) of a subject. For example, blood pressure data may be received via physiological input measuring device 110 based on one or more sensor inputs detected at physiological input measuring device 110. Physiological property data, may be received at, for example, component 120 via network 140. As discussed herein, physiological property data may be determined in a first format associated with physiological input measuring device 110 and may be converted into a second format to be utilized by component 120. Physiological property data may be converted from the first format to the second format via physiological input measuring device 110 and / or via component 120. According to an implementation, component 120 may be part of, associated with, or in communication with pacing system 130.

[0105] According to an example, the baseline pacing program may include adjusting the pacing rate based on an output of a pacing machine learning model. The pacing machine learning model may be trained in accordance with the techniques disclosed herein. For example, the pacing machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and / or the like. The pacing machine learning model may receive, as inputs, one or more of the physiologic input values, subject information, acceptable physiologic value ranges (e.g., as output by a threshold machine learning model), and / or the like. The baseline pacing machine learning model may output a pacing rate (e.g., an actual rate, a percentage change, a ratio, etc.) based on the inputs.

[0106] After beginning the baseline pacing program, step 243 may be performed. Step 243 may include determining a status of the treatment. Determining a status of the treatment may include determining physiological values after a certain duration of time (e.g., determining the SBP after 12 hours of performing the baseline pacing program).

[0107] At step 241b, a determination may be made regarding whether a physiologic property (e.g., SBP) falls below a threshold physiologic value (e.g., if SBP is less than approximately 130 mmHg). The threshold physiologic value may be a lower bound of an acceptable physiologic value range (e.g., an acceptable SBP range). If the physiologic property falls below the threshold physiologic value, then step 241c may be performed. Alternatively, if the physiologic property does not fall below the threshold physiologic value, then step 243 may be performed and a status update may be determined after another set duration of time (e.g., after another 12 hours).

[0108] At step 241c, a determination may be made regarding whether a physiologic property (e.g., SBP) is below a threshold physiologic value (e.g., if SBP is less than approximately 130 mmHg) for a predetermined number of checks. For example, step 241c may include determining whether the SBP has been below 130 mmHg for the last 3 checks. If the physiologic property has not been less than the threshold physiologic value for the pre-determined number of checks, step 243 may be performed and a status update may be determined in another pre-determined amount of time. Alternatively, if the physiologic property has been less than the threshold physiologic value for the pre-determined number of checks, step 244 may be performed.

[0109] At step 244, if the physiologic property is below the threshold physiologic value (e.g., lower than the acceptable physiologic value range) for the pre-determined number of checks (e.g., three checks every twelve hours), an existing pacing rate may be adjusted to be a predetermined or dynamically determined amount (e.g., approximately 5% lower). The pacing rate may be adjusted to be lowered by the amount in comparison to the existing pacing rate and / or may be adjusted to be lowered in comparison to a heart rate (e.g., a heart rate detected by an ECG sensor).

[0110] At step 245, a status update may be determined at another pre -determined amount of time. Step 24 Id may include receiving a subjective indication from the patient related to how the patient is feeling based on the implementation of the current baseline pacing rate. In one exemplary example, receiving a status update may include a subjective input received from the subject. For example, a patient may be provided a prompt (e.g., via a graphical user interface) requesting the patient to provide an assessment of his or her own wellbeing. According to an implementation of the disclosed subject matter, the subject may be provided a prompt, via a graphical interface, comprising graphical components that are generated and displayed based on the pre-determined status update schedule. For example, a prompt may provide a selectable graphical component corresponding to the subject feeling ill and selectable graphical component corresponding to the subject feeling well after the patient’s baseline pacing rate was set to 5%.[oni] If, at step 24 Id, a positive indication is received indicating that the subject is feeling the same or better than prior to the pacing rate adjustment at step 244, a waiting period of a predetermined or dynamically determined time period (e.g., 12 hours) may be implemented at before returning back to step 245. Upon expiration of the time period, another subjective input may be received at step 24 Id.According to implementations of the disclosed subject matter, a dynamically determined time period may be determined using an algorithm or time-based machine learning model. The time-based machine learning model may be trained in accordance with the techniques disclosed herein. For example, the time-based machine learning model may be trained to adjust one or more weights, layers, biases, synapses, nodes, and / or the like based on training data that may include one or more of historical or simulated physiologic input values, subject information, acceptable physiologic value ranges, and / or the like. The time-based machine learning model may receive, as inputs, one or more of the physiologic input value received, acceptable physiologic value ranges applied, and / or the like. The time-based machine learning model may output a dynamically determined time period such that, for example, the dynamically determined time period provides sufficient time for the subject to notice an effect of a pacing rate change. Further, the output dynamically determined time period may not exceed a time period such that the likelihood of the subject being effected by external factors other than the pacing rate change is below an acceptable threshold likelihood. Accordingly, the time-based machine learning model may, at least in part, determine a likelihood of the subject being effected by external factors.

[0112] In the alternative, if at step 24 Id a negative indication is received indicating that the subject is feeling worse, then, at step 246, the pacing rate will adjust the pacing rate. For example, the pacing rate may be adjusted to an intermediate rate (e.g., a rate less than the pacing rate at step 244, but greater than 0%). The pacing rate may also be adjusted by an amount in comparison to the existing pacing rate and / or may be adjusted to be lowered in comparison to a heart rate (e.g., a heart rate detected by an ECG sensor).

[0113] At step 247, a status update may be determined at another pre -determined amount of time. Step 24 le may include receiving a subjective indication from the patient related to how the patient is feeling based on the implementation of the current baseline pacing rate. In one exemplary example, receiving a status update may include a subjective input received from the subject. For example, a patient may be provided a prompt (e.g., via a graphical user interface) requesting the patient to provide an assessment of his or her own wellbeing. According to an implementation of the disclosed subject matter, the subject may be provided a prompt, via a graphical interface, comprising graphical components that are generated and displayed based on the pre-determined status update schedule. For example, a prompt may provide a selectable graphical component corresponding to the subject feeling ill and selectable graphical component corresponding to the subject feeling well after the patient’s baseline pacing rate was set to 2.5%.

[0114] If, at step 24 le, a positive indication is received indicating that the subject is feeling the same or better than prior to the pacing rate adjustment at step 246, a waiting period of a predetermined or dynamically determined time period (e.g., 12 hours) may be implemented at before returning back to step 247. Upon expiration of the time period, another subjective input may be received at step 241e.

[0115] If at step 24 le, a negative indication is received indicating that the subject is feeling worse, then at step 241f, the number of negative indications is compared to a pre -determined threshold (e.g., wasthis the 3rdconsecutive “worse” status update). If the threshold is not met, a waiting period of a predetermined or dynamically determined time period (e.g., 12 hours) may be implemented at before returning back to step 247. Upon expiration of the time period, another subjective input may be received at step 24 le. However, if the threshold is met at step 24 If, step 248 includes terminating the baseline pacing program and returning to default programing.

[0116] The exemplary lookup table, as shown in FIG. 2D as Table 250, may be populated based on a patient’s profile (e.g., using the threshold machine learning model and / or pacing machine learning model). Table 250 may include a heart rate 252, physiological parameter thresholds 254 (e.g., SBP) which may be output by a threshold machine learning model, a first pacing rate 256 which may be output by a pacing machine learning model, a first subjective input 258, a second pacing rate 260 output by a pacing machine learning model, a second subjective input 262, a third pacing rate 264 output by a pacing machine learning model, a third subjective input 266, a fourth pacing rate 268 output by a pacing machine learning model, and / or the like. It will be understood that the columns of Table 250 may corresponds to the steps described in reference to FIG. 2A, 2B, or 2C, which may be iterated multiple times. Such iterations are not limited by the columns of Table 250 shown in FIG. 2D.

[0117] In some examples, the lookup table may be populated based on and / or may include a patient’s height, weight, demographics, disease quantification (e.g. biomarkers such as one or more QT interval and / or one or more QTc interval), etc. The lookup table can be generated, for example, either using an empirical series of measurements in the clinic and / or a series of past records of the patient. Accordingly, a lookup table may be generated, for example, empirically or based on past historical information.

[0118] In some examples, the lookup table may not be static. In accordance with the current disclosure, a feedback system may be maintained wherein, for each new pacing rate that is programmed, the resultant physiological parameter values may be measured and / or monitored. If the desired control is achieved, changes to the look up table may not be needed. If the desired control is not achieved, a new target pacing rate may be tried / tested (e.g., in accordance with flow diagram 200 of FIG. 2A). If this rate results in the desired control, this experience may be recorded. After a certain number of such experiences, which may be a parameterized number, the lookup table entry may be modified to reflect the experiences. As such, a lookup table may be developed for each patient. Accordingly, a lookup table may be personalized (e.g., over time) based on feedback and learning, as discussed herein.

[0119] In certain clinical situations, it may be necessary to review modifications to the lookup table, even if such changes are algorithmically implemented autonomously. Hence, implementations of the current disclosure may include a built-in alert and / or communication mechanism that may be configured to transmit the modifications to a provider and may affect the changes, once approved. A user (e.g., a physician) or automated system may select, for example, either a review-gated mode or autonomous mode for the lookup table modifications or may set a threshold beyond which a change needs to bereviewed. Accordingly, a review (e.g., a manual review) may be implemented and may be triggered based on one or more thresholds.

[0120] At a macro level, correlating the personalized tables over similar patients (e.g., similar in height, weight, demographics, disease conditions, etc.) may yield improved starting points for the lookup table of each patient and / or also lead to the modifications of the lookup tables across a cohort. Such cross communication and group-evolution of lookup tables may be accomplished across multiple patient devices through a set of cloud services, anonymously. Such cross communication may be done at any desired or determined periodic frequency. Accordingly, cohort level data may be improved by aggregating personal tables and / or personal tables may be improved using cohort level data.

[0121] In some instances, modifications to a lookup table may inadvertently result in the patient feeling worse. In this case, a previous lookup table may be restored. Accordingly, previous versions of lookup tables may be stored and any may be restored, e.g., as a roll back feature. Accordingly, previous versions of a lookup table may be restored or rolled back.

[0122] In some instances, the lookup table may be a substitute implementation used in compute- constrained and / or battery-constrained platforms. For such a platform, the outputs of an algorithm may be pre-computed and stored, e.g., as a cache. This configuration may reduce the need for computation, reduce latency, and / or improve both the thermal management as well as battery life of certain implementations. Accordingly, a complex algorithm (e.g., running in the background) may be used to process one or more scenarios (e.g., a series of scenarios) and corresponding outputs bay be pre-recorded.

[0123] While the disclosed methods, devices, and systems are described with exemplary reference to pacing rate programming via a pacemaker, it should be appreciated that the disclosed examples may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainment system, a home entertainment system, etc. Also, the disclosed examples may be applicable to any type of Internet protocol.

[0124] According to an example, as shown in FIG. 2E, the process of flow diagram 270 describes an exemplary example of a pacing algorithm. The example may be an improvement to pacing programs by serving as an adaptive mechanism to calculate a desired right atrial pacing rate based on multiple variables (e.g., an existing pacing rate, current blood pressure, one or more physiological parameters, current heart-rate, patient’s current activity level, etc.). According to this example, the algorithm may use a three step approach to calculate an optimal right atrial pacing rate and make calculations more efficient by using patient specific values, from the configuration selected by a clinician, or derived from a machine learning algorithm that has “learned” an individual patient’s physiologic (e.g., blood pressure) dynamics and other correlated data that includes other parameters of interest, such as age, medications, etc. For simplicity, blood pressure is used as an example physiologic value hereafter. However, it will be understood that blood pressure may be replaced with any applicable physiological value discussed herein. Similarly, for simplicity, example blood pressure values are provided herein. It will beunderstood that values corresponding to one or more other physiologic attributes may be substituted for the example blood pressure values provided herein. This example may provide a personalized, patientspecific real-time closed loop adjustment to right atrial pacing for treating a variety of cardiovascular conditions including, but not limited to, hypertension and heart failure.

[0125] This example may be an extension of the examples disclosed herein, and may be based on extending the benefits of prior examples to patients with relatively low Systolic Blood Pressure (SBP) (e.g., ranging from approximately 115 mmHg -135 mmHg). For example, prior pacing programs may be designed for patients with an SBP of greater than 135 mmHg. This example may extend the range of SBP and treatment range for diastolic blood pressure, mean arterial pressure, and any of the above parameters, in combination with physiologic and / or environmental variables for which pacing may be applied. This example may include an algorithm that is configurable and capable of being enhanced by Al methods, such as ML. Thus, this example may make pacing programs even more effective as a realtime closed-loop system for a wide range of disease states, beginning with hypertension and heart failure.

[0126] As shown in FIG. 2E, this example extends and updates the pacing range, in comparison to examples disclosed herein. Multiple stages of pacing programs may be defined. As shown in Table 271, multiple stages may include a Stage-I pacing (Stage-I Pacing where SBP is, for example, between approximately 115 mmHg to approximately 135 mmHg) and a Stage-II pacing (Stage-II Pacing where SBP is greater than 135 mmHg). There may also be a no pacing stage (no Pacing where SBP is less than 115 mmHg). Each stage may refer to a different level of therapy.

[0127] In this example, Stage-I patients may receive lower doses of pacing therapy and the automatic or configurable increments of treatment may also be smaller. For Stage-II patients, the dosage and increments of pacing therapy may be higher than Stage-I, making Stage-II pacing may be calculated to determine more aggressive pacing values. For example, Stage-I pacing may be configurable to reduce both the duration and magnitude of right atrial pacing changes, which may result in Stage-I dosing that is less intense (lower duration, and / or lower rate increase or decrease) than Stage-II.

[0128] In this example, flow diagram 270 illustrates a high level decision making flowchart. Each blood pressure value may be assessed against the 3-bands approach from table 271 (No Pacing, Stage-I, or Stage-II). A decision may be made to classify a given blood pressure value into one of the three bands. If the value falls into the “No Pacing” band, no therapy will be delivered.

[0129] If the value is classified into the Stage-I band, the Stage-I lookup table may be used to calculate the applicable pacing value to be sent as a programming command to the pacemaker. This lookup table may be similar to table 280 from FIG. 2F. While the values from table 280 represent actual values for this example, it is to be appreciated that table 280 may include other values and is not limited to the values included herewith. As disclosed herein, a lookup table may be output by a machine learning model based on one more inputs and / or applicable training data (e.g., supervised or unsupervised training data).

[0130] According to an example, a patient physiologic value may be detected and / or received (e.g., a blood pressure value detected using a blood pressure device). The patient physiologic value may be analyzed and a determination may be made whether the physiologic value corresponds to Stage-I, Stage- II, or Stage-Ill. Based on the determination of the applicable stage, a corresponding stage specific program may be activated. Similarly, based on the determination of the applicable stage, a corresponding look-up table may be generated, received, and / or loaded. By activating a given stage specific program, the pacing based technology may be improved as a single program may be selected from a plurality of programs such that targeted pacing may be provided to a patient.

[0131] In this example, a preliminary calculation may be based on table 280 and / or a pacing algorithm may be designed to incorporate various configurable parameters. These parameters may be divided into categories such patient historical values, patient physiological parameters, and patient environmental and geographical parameters. A non-exhaustive list may include the following:

[0132] Patient historical values may include (but are not limited to): Which Pacing value resulted in better quality of life; Success of a pacing program; Individual successful treatment dynamics developed for each patient; Recent food / drink intake; Amount of sleep last night; Physical activities (e.g., recent or ongoing); Any emotional events; Any ongoing sickness; Current medications; etc.

[0133] Patient physiological parameters may include (but are not limited to): Demographics (e.g., male / female, age, body morphology, race); Medications; Pulse oximetry; Hemodynamics (e.g., cardiac output and systemic vascular resistance); Height; Weight; Systolic Blood Pressure; Diastolic Blood Pressure; Mean Arterial Pressure; Heart-rate; Oxygen saturation; Body temperature; Fluid status; Bioimpedance; Systemic vascular resistance; etc.

[0134] Patient environmental and geographical parameters may include (but are not limited to): Date & time of the day; Location (e.g., latitude & longitude and altitude); Outside temperature; Humidity; Motion sensor input; etc.

[0135] Some of these values may be fed through a patient feedback module, which may be an optional module for the entire algorithm. Other variables may be derived through sensors and other automation or calculations. Consideration of these configurable values may be through traditional mathematics, a machine learning algorithm, or a hybrid approach of the two. Machine learning scenarios may calculate patient specific recommendations by learning from a patient’s history and may also consider cohortbased learnings, available from a server, to apply towards calculating an optimal or recommended pacing value for a given dosage delivery.

[0136] Flow diagram 270 may start at step 272. Step 273 may include checking a parameter, such as blood pressure. Based on the parameter, a determination is made at step 274, to determine if a patient falls within a Stage-I pacing program. If the patient is determined to be on a Stage-I pacing program (e.g., an SBP between 115 mmHg and 135 mmHg), step 274a may include activating a Stage-I program 1using a Stage-I pacing algorithm to determine and adjust a pacing parameter for a patient. After a period of time (e.g., 30 minutes, 3 hours, or 3 days) step 274b may include receiving a patient’s feedback.

[0137] Receiving a patient’s feedback may include receiving a subjective indication from the patient related to how the patient is feeling based on the implementation of the current pacing rate. In one example, receiving a status update may include a subjective input received from the subject. For example, a patient may be provided a prompt (e.g., via a graphical user interface) requesting the patient to provide an assessment of his or her own wellbeing. According to an implementation of the disclosed subject matter, the subject may be provided a prompt, via a graphical interface, comprising graphical components that are generated and displayed based on the pre-determined status update schedule. For example, a prompt may provide a selectable graphical component corresponding to the subject feeling ill and selectable graphical component corresponding to the subject feeling well after the patient’s baseline pacing rate was set to 5%. Alternatively, or in addition, receiving patient feedback may include receiving a pseudo-subjective input, as discussed herein.

[0138] The patient feedback (or pseudo-subjective input) may be combined, in step 276, with configurable, patent specific parameters (physiological and historical). This information may then be included, at step 274a, and utilized as part of Stage-I pacing algorithm to determine an updated pacing parameter. The loop may continue in such a manner where after each adjustment, patient feedback is received, and the new information may be used to update the pacing parameter determined using the Stage-I pacing algorithm.

[0139] Alternatively, if it is determined at step 274 that the patient is not in a Stage-I pacing (e.g., the SBP of the patient is greater than 135 mmHg) a determination at step 275 will determine if the patient falls within a Stage-II pacing program. If the patient is determined to fall into a Stage-II pacing program, step 275 a may include activating a Stage-II program using a Stage-II pacing algorithm to determine a pacing parameter for a patient. After the pacing parameter is adjusted, step 275b may include receiving a patient’s feedback, such as by using methods described above. The patient feedback may be combined in step 276 with configurable, patient specific parameters (physiological and historical). This information may then be included again at step 275 a and used as part of Stage-II pacing algorithm to determine an updated pacing parameter.

[0140] Generally, a Stage-II pacing algorithm may follow a similar strategy as a Stage-I processing. In the example, when comparing a Stage-I dosage with a Stage-II dosage, the Stage-II pacing program will likely have a higher dosage, larger increments, and more steps. After each adjustment, a patient’s feedback is received, and that new information may be used to update pacing parameters using the Stage- II pacing algorithm.

[0141] If it is determined at step 275 that the patient is not in a Stage-II pacing, the process will revert back to step 273 where patient parameters (such as blood pressure) may be monitored.

[0142] Step 274a and step 275a of Stage-I and Stage-II respectively, may be configurable to include therapeutic parameters such as: Duration between two consecutive Pacing therapies (e.g., settling time or refractory period); Priority level with respect to other pacemaker’s built-in algorithms; Number of increments (stair steps model) and different % for each increments; Criteria for step back and different % for each step back values; Total number of steps before max out or reaching minimum; etc.

[0143] Additionally, step 274a and 275a may include a pacing algorithm that allows clinicians to configure various rules based on which patient can be remotely monitored better. For example, such rules may include (but are not limited to) the following: If the patient is not feeling well after 3 consecutive attempts of pacing therapy, alert the clinician; If the patient remains at a max possible pacing value for 5 days, alert the physician; If a pacemaker or ICD rejects a prescribed pacing value 3 consecutive times, alert the physician; If a patient is jogging, but a sensory input is at a level where it needs additional confirmation, alert the patient through smartphone app notification, and ask for confirmation, etc

[0144] Overall, this example may be designed to have a maximum configurability to customize pain therapies for every patient, prescribe a pain therapy that considers various external parameters, leverage machine learning to determine appropriate pacing programs and parameters, incorporate advanced monitoring and alerting mechanisms made available to a patient, caretaker, and clinician, and harvest relevant data available in a cloud for machine learning purposes.

[0145] The exemplary lookup table, as shown in FIG. 2F as Table 280, may be populated based on a patient’s profile (e.g., using the threshold machine learning model and / or pacing machine learning model). Table 280 may include a heart rate 281, physiological parameter thresholds 282 (e.g., SBP) which may be output by a threshold machine learning model, a first pacing rate 283 which may be output by a pacing machine learning model, a first subjective input 284, a second pacing rate 285 output by a pacing machine learning model, a second subjective input 286, a third pacing rate 287 output by a pacing machine learning model, a third subjective input 288, a fourth pacing rate 289 output by a pacing machine learning model, and / or the like. It will be understood that the columns of Table 280 may correspond to the steps described in reference to FIG. 2E, which may be iterated multiple times. Such iterations are not limited by the columns of Table 280 shown in FIG. 2F.

[0146] In some examples, the lookup table may be populated based on and / or may include a patient’s height, weight, demographics, disease quantification (e.g. biomarkers such as one or more QT interval and / or one or more QTc interval), etc. The lookup table can be generated, for example, either using an empirical series of measurements in the clinic and / or a series of past records of the patient. Accordingly, a lookup table may be generated, for example, empirically or based on past historical information.

[0147] In some examples, the lookup table may not be static. In accordance with the current disclosure, a feedback system may be maintained wherein, for each new pacing rate that is programmed, the resultant physiological parameter values may be measured and / or monitored. If the desired control isachieved, changes to the look up table may not be needed. If the desired control is not achieved, a new target pacing rate may be tried / tested (e.g., in accordance with flow diagram 270 of FIG. 2E). If this rate results in the desired control, this experience may be recorded. After a certain number of such experiences, which may be a parameterized number, the lookup table entry may be modified to reflect the experiences. As such, a lookup table may be developed for each patient. Accordingly, a lookup table may be personalized (e.g., over time) based on feedback and learning, as discussed herein.

[0148] In certain clinical situations, it may be necessary to review modifications to the lookup table, even if such changes are algorithmically implemented autonomously. Hence, implementations of the current disclosure may include a built-in alert and / or communication mechanism that may be configured to transmit the modifications to a provider and may affect the changes, once approved. A user (e.g., a physician) or automated system may select, for example, either a review-gated mode or autonomous mode for the lookup table modifications or may set a threshold beyond which a change needs to be reviewed. Accordingly, a review (e.g., a manual review) may be implemented and may be triggered based on one or more thresholds.

[0149] At a macro level, correlating the personalized tables over similar patients (e.g., similar in height, weight, demographics, disease conditions, etc.) may yield improved starting points for the lookup table of each patient and / or also lead to the modifications of the lookup tables across a cohort. Such cross communication and group-evolution of lookup tables may be accomplished across multiple patient devices through a set of cloud services, anonymously. Such cross communication may be done at any desired or determined periodic frequency. Accordingly, cohort level data may be improved by aggregating personal tables and / or personal tables may be improved using cohort level data.

[0150] In some instances, modifications to a lookup table may inadvertently result in the patient feeling worse. In this case, a previous lookup table may be restored. Accordingly, previous versions of lookup tables may be stored and any may be restored, e.g., as a roll back feature. Accordingly, previous versions of a lookup table may be restored or rolled back.

[0151] In some instances, the lookup table may be a substitute implementation used in compute- constrained and / or battery-constrained platforms. For such a platform, the outputs of an algorithm may be pre-computed and stored, e.g., as a cache. This configuration may reduce the need for computation, reduce latency, and / or improve both the thermal management as well as battery life of certain implementations. Accordingly, a complex algorithm (e.g., running in the background) may be used to process one or more scenarios (e.g., a series of scenarios) and corresponding outputs may be prerecorded.

[0152] While the disclosed methods, devices, and systems are described with exemplary reference to pacing rate programming via a pacemaker, it should be appreciated that the disclosed examples may be applicable to any environment, such as a desktop or laptop computer, an automobile entertainmentsystem, a home entertainment system, etc. Also, the disclosed examples may be applicable to any type of Internet protocol.

[0153] FIG. 3A shows a flowchart 300 for determining pacing rate, according to one or more examples. For simplicity, flowchart 300 is provided in reference to blood pressure. However, it will be understood that any applicable physiological parameter value, such as those discussed herein, may be used in reference to flowchart 300. At step 302, the beginning of a session, a patient’s blood pressure and heart rate may be received. The blood pressure may be detected using a blood pressure cuff or a continuous blood pressure device. The blood pressure data may be provided to a processor that is local to a pacing device or a remote component external to the pacing device (e.g., a user device, a cloud component, an external processor, etc.), such as component 120 of FIG. 1.

[0154] At step 304, a first pacing rate may be determined to modify the heart rate received at step 302, based on the blood pressure input received at step 302. For example, if a patient’s initial blood pressure was 145 mmHg and heart rate was 60 bpm, the first pacing rate may be set at 66 bpm. In another example, if a patient’s initial blood pressure was 105 mmHg, and heart rate was 60 bpm, the first pacing rate may be set at 58 bpm. The first pacing rate may be determined based on the techniques disclosed herein in reference to FIG. 2A. For example, the first pacing rate may be determined based on a threshold value and / or second threshold value output by a threshold machine learning model and / or an output generated by a pacing machine learning model, as discussed herein. Alternatively, or in addition, the first pacing rate may be determined in accordance with the complex lookup table discussed in reference to FIG. 2D. As discussed herein, the complex lookup table may be populated using outputs output by a threshold machine learning model and / or pacing machine learning model.

[0155] According to examples disclosed herein, a pacing rate may be determined on-demand using the threshold machine learning model and / or pacing machine learning machine learning model. For example, component 120 may receive the blood pressure and / or heart rate at step 302 and provide the same to the threshold machine learning model and / or pacing machine learning machine to receive a pacing rate. Alternatively, pacing system 130 may be provided a complex lookup table (e.g., table 250 of FIG. 2D) which may be populated based on the threshold machine learning model and / or pacing machine learning machine. Pacing system 130 may maintain a static copy of the complex lookup table which may be updated from time to time. According to this example, battery efficiency may be optimized at the pacing system 130 by using the static complex lookup table to receive, determine, or otherwise identify pacing rates.

[0156] At step 306, the first pacing rate may be output and the output may be received at a cardiac pacing device (e.g., pacing system 130). The cardiac pacing device may be configured to pace based on the first pacing rate. For example, the first pacing rate may be output by a pacing machine learning algorithm and / or by a complex lookup table populated at least in part based on a pacing machine learning model.

[0157] After a defined period of time (e.g., as determined based on a time-based machine learning model), a patient may be provided an interface to rate their wellbeing (e.g., as discussed in reference to steps 218A, 218B, 218C, 218D, 218E, and / or 218E of FIG. 2A). The interface may be populated in accordance with the techniques discussed herein.

[0158] At step 308, a patient’s input regarding their wellbeing may be received following the output of the first pacing rate at step 306. For example, a patient may indicate that they are feeling better, worse, or the same. If the patient is feeling better or same (e.g., based on a positive indication provided via the provided interface), the session may terminate as there may be no need for further adjustment of the pacing rate. However, if the patient indicates that they are feeling worse (e.g., a negative indication), the session may continue to step 310.

[0159] At step 310, a second pacing rate is determined based on input received from the patient at step 308. For example, if the first pacing rate was set at 66 bpm, the second pacing rate may be set at 63 bpm, based on input received from the patient at step 308. In another example, if the first pacing rate was set at 58 bpm, and the patient input a negative indication at step 308, the second pacing rate may be adjusted in accordance with the techniques disclosed herein (e.g., the second pacing rate may be set to the initial rate at the beginning of the session, such as 60 bpm). At step 312, the second pacing may be output and the output may be received at a cardiac pacing device. The cardiac pacing device may be configured to pace based on the second pacing rate.

[0160] FIG. 3B shows a flowchart 320 for determining pacing rate, according to one or more examples. For simplicity, flowchart 320 is provided in reference to blood pressure. However, it will be understood that any applicable physiological parameter value, such as those discussed herein, may be used in reference to flowchart 320. At step 322, the beginning of a baseline pacing session, a patient’s baseline pacing program may be received subsequent to the heart rate matching the second pacing rate from FIG. 3 A at step 312. The baseline pacing program may be provided by a provider or determined by a machine learning model as discussed herein.

[0161] At step 304, a third pacing rate may be determined to modify the heart rate, based on the baseline pacing program received at step 322. For example, if a patient’s initial blood pressure was 105 mmHg, and heart rate was 60 bpm, the first pacing rate may be set at 58 bpm. The pacing rate may be determined based on the techniques disclosed herein in reference to FIG. 2B or FIG. 2C. For example, the first pacing rate may be determined based on a threshold value and / or second threshold value output by a threshold machine learning model and / or an output generated by a pacing machine learning model, as discussed herein. Alternatively, or in addition, the pacing rate may be determined in accordance with the complex lookup table discussed in reference to FIG. 2D. As discussed herein, the complex lookup table may be populated using outputs output by a threshold machine learning model and / or pacing machine learning model.

[0162] According to examples disclosed herein, a pacing rate may be determined on-demand using the threshold machine learning model and / or pacing machine learning machine learning model. For example, component 120 may receive the blood pressure and / or heart rate at step 324 and provide the same to the threshold machine learning model and / or pacing machine learning machine to receive a pacing rate. Alternatively, pacing system 130 may be provided a complex lookup table (e.g., table 250 of FIG. 2D) which may be populated based on the threshold machine learning model and / or pacing machine learning machine. Pacing system 130 may maintain a static copy of the complex lookup table which may be updated from time to time. According to this example, battery efficiency may be optimized at the pacing system 130 by using the static complex lookup table to receive, determine, or otherwise identify pacing rates.

[0163] At step 326, the pacing rate may be output and the output may be received at a cardiac pacing device (e.g., pacing system 130). The cardiac pacing device may be configured to pace based on the first pacing rate. For example, the first pacing rate may be output by a pacing machine learning algorithm and / or by a complex lookup table populated at least in part based on a pacing machine learning model.

[0164] According to an example, if a given physiological value targeted to improve using baseline pacing does not improve or reach a given optimal state after a threshold amount of time and / or level of baseline pacing, the baseline pacing may be suspended. Further, according to an example, if a given physiological value reaches a lower baseline bound (e.g., approximately 100 mmHg), then baseline pacing may be suspended.

[0165] The disclosed subject matter provides a closed-loop system for determining cardiac pacing rates (e.g., based on the patient’s blood pressure, heart rate, and / or other physiological, environmental, subjective and / or pseudo-subjective inputs) and stimulating a heart based on the determined pacing rates (e.g., by stimulating atrial tissue).

[0166] FIG. 4 depicts a flow diagram 400 of an exemplary method for determining a pacing rate via a pseudo-subjective machine learning model, according to one or more examples. As shown in FIG. 4, the pseudo-subjective input 408 may be output by a pseudo-subjective machine learning model 406. The pseudo-subjective machine learning model 406 may be trained to output a pseudo-subjective input 408 based on one or more objective inputs. The pseudo-subjective machine learning model 406 may be trained by modifying one or more layers, weights, biases, synapsis, and / or the like of the model based on the training data. The machine learning model may be trained to output a pseudo-subjective input 408 based on one or more sensor inputs which may correspond to physiological inputs (e.g., blood pressure, heart-rate, temperature, sympathetic nerve activity (SNA), etc.). These sensor inputs may be sensed by one or more sensors, such as those shown in FIG. 4 as sensor input A 402 and / or sensor input B 404.

[0167] The pseudo-subjective machine learning model 406 may be trained using historical patient information 410, such as, patient subjective inputs, patient medications (e.g., patient provided, receivedby a system or component, medication compliance information, etc.), health history, and / or the like. For example, pseudo-subjective machine learning model 406 may be trained using historical subjective inputs provided by a patient (e.g., in accordance with flow diagram 200 of FIG. 2A). The pseudo- subjective machine learning model 406 may correlate or otherwise associate such subjective inputs with corresponding physiological inputs captured at or about the time the subjective inputs are provided. Accordingly, the pseudo-subjective machine learning model 406 may be trained to output pseudo- subjective inputs that correspond to the subjective inputs a patient is likely to provide, based on one or more current physiological states the patient is experiencing. As a simplified example, a given patient may typically provide a negative indication as a subjective input when the patient’s heart rate is above a given amount. Accordingly, the pseudo-subjective machine learning model 406 may generate a negative indication pseudo-subjective input when sensor input A 402 and / or sensor input B 404 senses a heart rate above the given heart rate. It will be understood that although this simplified example is based on a single objective input (e.g., heart rate), the pseudo-subjective machine learning model 406 may be trained to output a pseudo-subjective input based on one or more objective inputs, trends related to such objective inputs, changes in such objective inputs, and / or the like.

[0168] Alternatively, or in addition, cohort population information 412 may be used to train pseudo- subjective machine learning model 406. For example, pseudo-subjective machine learning model 406 may be trained using historical or simulated subjective inputs provided by a cohort of patients (e.g., in accordance with flow diagram 200 of FIG. 2A). The pseudo-subjective machine learning model 406 may correlate or otherwise associate such cohort based subjective inputs with corresponding cohort physiological inputs captured at or about the time the subjective inputs are provided. Accordingly, the pseudo-subjective machine learning model 406 may be trained to output pseudo-subjective inputs that correspond to the subjective inputs a patient is likely to provide, based on one or more current physiological states the patient is experiencing. As a simplified example, a cohort of patients may typically provide a negative indication as a subjective input when the patients’ temperature is above a given amount. Accordingly, the pseudo-subjective machine learning model 406 may generate a negative indication pseudo-subjective input when sensor input A 402 and / or sensor input B 404 senses that a given patient’s temperature is above the given temperature.

[0169] The following is an example that corresponds with the subject matter discussed herein. Drug resistant hypertension (DRH) is defined as blood pressure (BP) that remains above goal despite concomitant use of >3 different classes of antihypertensive drugs, administered at maximally tolerated doses, including a diuretic. Patients with DRH are at high risk for having major cardiovascular events. The prevalence and incidence of DRH is expected to increase as the global population continues to age, with an overall increase in the number of affected individuals as the general population grows.

[0170] Recent efforts to address the problem of DRH have included the development and investigation of device-based therapies. Because many patients have high resting sympathetic activity, one focus inthe past decade has been the development of several implantable devices, including those intended to target the autonomic nervous system, regulate left ventricular preload or alter mechanical arterial properties. Non-pharmacological neuromodulation devices that modulate sympathetic activity using electrical activation of the carotid baroreflex, catheter-based renal nerve ablation, and new algorithms for permanent ventricular pacing are supported by experimental studies and early clinical trials. However, the relationship between hypertension (HTN) and conventional clinical permanent cardiac pacing is not well established. Many older patients requiring permanent pacing (PP) also have persistent HTN with systolic blood pressures (SBP) above recommended levels. A significant reduction in SBP and diastolic (DBP) has been observed among such patients.

[0171] In this example, permanent cardiac pacing, particularly with elderly patients having DRH, may result in pacing-mediated BP lowering.

[0172] One or more implementations disclosed herein may be applied by using a machine learning model, another Al system such as a neural network, or a non-AI rules-based system. For example, a machine learning model may be used to determine a state machine and / or a next state. As shown in flow diagram 500 of FIG. 5, training data 512 may include one or more of stage inputs 514 and known outcomes 518 related to a machine learning model to be trained. The stage inputs 514 may be from any applicable source including an input or system discussed herein (e.g., an output from a step or aspect of FIGs. 1-4). The known outcomes 518 may be included for machine learning models generated based on supervised or semi-supervised training. An unsupervised machine learning model might not be trained using known outcomes 518. Known outcomes 518 may include known or desired outputs for future inputs similar to or in the same category as stage inputs 514 that do not have corresponding known outputs.

[0173] The training data 512 and a training algorithm 520 may be provided to a training component 530 that may apply the training data 512 to the training algorithm 520 to generate a machine learning model. According to an implementation, the training component 530 may be provided comparison results 516 that compare a previous output of the corresponding machine learning model to apply the previous result to re-train the machine learning model. The comparison results 516 may be used by the training component 530 to update the corresponding machine learning model. The training algorithm 520 may utilize machine learning networks and / or models including, but not limited to a deep learning network such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN) and Recurrent Neural Networks (RCN), probabilistic models such as Bayesian Networks and Graphical Models, and / or discriminative models such as Decision Forests and maximum margin methods, or the like.

[0174] In general, any process or operation discussed in this disclosure that is understood to be computer-implementable, such as the flows and / or process discussed herein (e.g., in FIGS. 1-5), etc., may be performed by one or more processors of a computer system, such any systems or devices usedto implement the techniques disclosed herein. A process or process step performed by one or more processors may also be referred to as an operation. The one or more processors may be configured to perform such processes by having access to instructions (e.g., software or computer-readable code) that, when executed by the one or more processors, cause the one or more processors to perform the processes. The instructions may be stored in a memory of the computer system. A processor may be a central processing unit (CPU), a graphics processing unit (GPU), or any suitable types of processing unit.

[0175] FIG. 6 depicts an example system 600 that may execute techniques presented herein. FIG. 6 is a simplified functional block diagram of a computer that may be configured to execute techniques described herein, according to examples of the present disclosure. Specifically, the computer (or “platform” as it may not be a single physical computer infrastructure) may include a data communication interface 660 for packet data communication. The platform may also include a central processing unit 620 (“CPU”), in the form of one or more processors, for executing program instructions. The platform may include an internal communication bus 610, and the platform may also include a program storage and / or a data storage for various data files to be processed and / or communicated by the platform such as ROM 630 and RAM 640, although the system 600 may receive programming and data via network communications. The system 600 also may include input and output ports 650 to connect with input and output devices such as keyboards, mice, touchscreens, monitors, displays, etc. Of course, the various system functions may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load. Alternatively, the systems may be implemented by appropriate programming of one computer hardware platform.

[0176] The general discussion of this disclosure provides a brief, general description of a suitable computing environment in which the present disclosure may be implemented. In one example, any of the disclosed systems, methods, and / or graphical user interfaces may be executed by or implemented by a computing system consistent with or similar to that depicted and / or explained in this disclosure. Although not required, aspects of the present disclosure are described in the context of computerexecutable instructions, such as routines executed by a data processing device, e.g., a server computer, wireless device, and / or personal computer. Those skilled in the relevant art will appreciate that aspects of the present disclosure can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including personal digital assistants (“PDAs”)), wearable computers, all manner of cellular or mobile phones (including Voice over IP (“VoIP”) phones), dumb terminals, media players, gaming devices, virtual reality devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, mini-computers, mainframe computers, and the like. Indeed, the terms “computer,” “server,” and the like, are generally used interchangeably herein, and refer to any of the above devices and systems, as well as any data processor.

[0177] Aspects of the present disclosure may be embodied in a special purpose computer and / or data processor that is specifically programmed, configured, and / or constructed to perform one or more of the computer-executable instructions explained in detail herein. While aspects of the present disclosure, such as certain functions, are described as being performed exclusively on a single device, the present disclosure may also be practiced in distributed environments where functions or modules are shared among disparate processing devices, which are linked through a communications network, such as a Local Area Network (“LAN”), Wide Area Network (“WAN”), and / or the Internet. Similarly, techniques presented herein as involving multiple devices may be implemented in a single device. In a distributed computing environment, program modules may be located in both local and / or remote memory storage devices.

[0178] As discussed herein, a memory may include a device or system that is used to store information for immediate use in a computer or related computer hardware and digital electronic devices. Contents of memory can be transferred to storage (e.g., via virtual memory). Memory may be implemented as semiconductor memory, where data is stored within memory cells built from MOS transistors on an integrated circuit. Semiconductor memory may include volatile and / or non-volatile memory. Examples of non-volatile memory include flash memory and read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) memory, and the like. Examples of volatile memory include primary memory such as dynamic random-access memory (DRAM) and fast CPU cache memory such as static random-access memory (SRAM).

[0179] Aspects of the present disclosure may be stored and / or distributed on non-transitory computer- readable media, including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, biological memory, or other data storage media. Alternatively, computer implemented instructions, data structures, screen displays, and other data under aspects of the present disclosure may be distributed over the Internet and / or over other networks (including wireless networks), on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave, etc.) over a period of time, and / or they may be provided on any analog or digital network (packet switched, circuit switched, or other scheme).

[0180] Program aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of executable code and / or associated data that is carried on or embodied in a type of machine-readable medium. “Storage” type media include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, mayenable loading of the software from one computer or processor into another, for example, from a management server or host computer of the mobile communication network into the computer platform of a server and / or from a server to the mobile device. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links, or the like, also may be considered as media bearing the software. As used herein, unless restricted to non- transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0181] The terminology used above may be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific examples of the present disclosure. Indeed, certain terms may even be emphasized above; however, any terminology intended to be interpreted in any restricted manner will be overtly and specifically defined as such in this Detailed Description section. Both the foregoing general description and the detailed description are exemplary and explanatory only and are not restrictive of the features, as claimed.

[0182] Other examples of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.

[0183] It should be understood that examples in this disclosure are exemplary only, and that other examples may include various combinations of features from other examples, as well as additional or fewer features. It should be appreciated that in the above description of exemplary examples of the invention, various features of the invention are sometimes grouped together in a single example, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed example. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate example of this invention.

[0184] Furthermore, while some examples described herein include some but not other features included in other examples, combinations of features of different examples are meant to be within the scope of the invention, and form different examples, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed examples can be used in any combination.

[0185] Thus, while certain examples have been described, those skilled in the art will recognize that other and further modifications may be made thereto without departing from the spirit of the invention,and it is intended to claim all such changes and modifications as falling within the scope of the invention. For example, functionality may be added or deleted from the block diagrams and operations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present invention.

[0186] The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other implementations, which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. While various implementations of the disclosure have been described, it will be apparent to those of ordinary skill in the art that many more implementations are possible within the scope of the disclosure. Accordingly, the disclosure is not to be restricted except in light of the attached claims and their equivalents.

Claims

CLAIMSWhat is claimed is:

1. A system for adaptive cardiac pacing comprising: a physiological input measuring device configured to receive physiological data from a patient, the physiological data comprising a blood pressure; a pacing system comprising one or more defibrillators configured to deliver cardiac pacing to the patient; a memory configured to store one or more of a lookup table and a machine learning model; and a processor operatively coupled to the physiological input measuring device, the pacing system, and the memory, the processor configured to: determine that the blood pressure is below a threshold baseline physiologic value, initiate a baseline pacing program, the baseline pacing program comprising a baseline rate of cardiac pacing determined by one or more of manual input, the lookup table and the machine learning model, determine that one or more of the blood pressure, in response to the baseline pacing program, is less than the threshold baseline physiologic value during a predetermined period of time and the baseline rate of cardiac pacing is below a baseline pacing threshold during the predetermined period of time, determine that the blood pressure, in response to the baseline pacing program, is above a threshold upper physiologic value, and terminate the baseline pacing program.

2. The system of claim 1, wherein the physiological data further comprises one or more of a heart rate, biomarker levels, blood oxygen levels, glucose levels, blood electrolytes levels, accelerometer values, respiratory rate, thoracic impedance, atrial rate, ventricular rate, and atrioventricular conduction.

3. The system of claim 1, wherein the blood pressure is a systolic blood pressure.

4. The system of claim 1, wherein the threshold baseline physiologic value is a lower bound of a physiologic value range determined for the patient and threshold upper physiologic value is an upper bound of a physiologic value range determined for the patient.

5. The system of claim 1, wherein one of the threshold baseline physiologic value and the threshold upper physiologic value comprises 130 mmHg.

6. The system of claim 1, wherein the predetermined period of time comprises 3 days.

7. The system of claim 1, wherein the lookup table comprises pacing rate values corresponding to different combinations of heart rate and blood pressure measurements and is personalized for the patient based on the patient's physiological responses to previous pacing adjustments.

8. The system of claim 1, wherein the machine learning model is trained using one or more of one or more of historical physiologic input values, simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and pseudo subjective inputs.

9. The system of claim 1, wherein the processor is further configured to: determine that the blood pressure, in response to the baseline pacing program, is above the threshold baseline physiologic value during the predetermined period of time; determine that the baseline rate of cardiac pacing is above a baseline pacing threshold during the predetermined period of time; and continue the baseline pacing program.

10. The system of claim 1, wherein the processor is further configured to: determine that the blood pressure, in response to the baseline pacing program, is below the threshold upper physiologic value; and continue the baseline pacing program.

11. A method for adaptive cardiac pacing comprising: receiving, by a physiological input measuring device, physiological data from a patient, the physiological data comprising a blood pressure; determining, by a processor, that the blood pressure is below a threshold baseline physiologic value; initiating, by a pacing system comprising one or more defibrillators configured to deliver cardiac pacing to the patient, a baseline pacing program, the baseline pacing program comprising a baseline rate of cardiac pacing determined by one or more of manual input, a lookup table stored in a memory, and a machine learning model stored in the memory; determining, by the processor, that one or more of the blood pressure, in response to the baseline pacing program, is less than the threshold baseline physiologic value during a predetermined period of time and the baseline rate of cardiac pacing is below a baseline pacing threshold during the predetermined period of time,determining, by the processor, that the blood pressure, in response to the baseline pacing program, is above a threshold upper physiologic value, and terminating, by the pacing system, the baseline pacing program.

12. The method of claim 11, wherein the physiological data further comprises one or more of a heart rate, biomarker levels, blood oxygen levels, glucose levels, blood electrolytes levels, accelerometer values, respiratory rate, thoracic impedance, atrial rate, ventricular rate, and atrioventricular conduction.

13. The method of claim 11, wherein the blood pressure is a systolic blood pressure.

14. The method of claim 11, wherein the threshold baseline physiologic value is a lower bound of a physiologic value range determined for the patient and the threshold upper physiologic value is an upper bound of a physiologic value range determined for the patient.

15. The method of claim 11, wherein one of the threshold baseline physiologic value and the threshold upper physiologic value comprises 130 mmHg.

16. The method of claim 11, wherein the predetermined period of time comprises 3 days.

17. The method of claim 11, wherein the lookup table comprises pacing rate values corresponding to different combinations of heart rate and blood pressure measurements and is personalized for the patient based on the patient's physiological responses to previous pacing adjustments.

18. The method of claim 11, wherein the machine learning model is trained using one or more of one or more of historical physiologic input values, simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and pseudo subjective inputs.

19. The method of claim 11, further comprising: determining, by the processor, that the blood pressure, in response to the baseline pacing program, is above the threshold baseline physiologic value during the predetermined period of time; determining, by the processor, that the baseline rate of cardiac pacing is above a baseline pacing threshold during the predetermined period of time; and continuing, by the pacing system, the baseline pacing program.

20. The method of claim 11, further comprising: determining, by the processor, that the blood pressure, in response to the baseline pacing program, is below the threshold upper physiologic value; and continuing, by the pacing system, the baseline pacing program.

21. A system for adaptive cardiac pacing comprising: a physiological input measuring device configured to receive physiological data from a patient, the physiological data comprising a blood pressure; a user device; a pacing system comprising one or more defibrillators configured to deliver cardiac pacing to the patient; a memory configured to store one or more of a lookup table and a machine learning model; and a processor operatively coupled to the physiological input measuring device, the user device, the pacing system, and the memory, the processor configured to: determine that the blood pressure is below a threshold physiologic value, initiate a baseline pacing program, the baseline pacing program comprising a first rate of cardiac pacing determined by one or more of manual input, the lookup table and the machine learning model, determine that the blood pressure, in response to the baseline pacing program, is less than the threshold physiologic value after a first predetermined period of time, determine that the blood pressure remains below the threshold physiologic value for a predetermined number of consecutive checks, adjust the first rate of cardiac pacing to a second rate of cardiac pacing, receive a first user input via the user device after a second predetermined period of time, the user input comprising a first indication of wellbeing in response to the second rate of cardiac pacing, adjust the second rate of cardiac pacing to a third rate of cardiac pacing in response to the user input, receive one or more additional user inputs via the user device after a third predetermined period of time, the one or more additional user inputs comprising one or more additional indications of wellbeing in response to the third rate of cardiac pacing, determine that a predetermined number of consecutive additional user inputs of the one or more additional user inputs are negative indications of wellbeing, terminating the baseline pacing program, and initiating a default pacing program comprising a default rate of cardiac pacing.

22. The system of claim 21, wherein the physiological data further comprises one or more of a heart rate, biomarker levels, blood oxygen levels, glucose levels, blood electrolytes levels, accelerometer values, respiratory rate, thoracic impedance, atrial rate, ventricular rate, and atrioventricular conduction.

23. The system of claim 21, wherein the blood pressure is a systolic blood pressure.

24. The system of claim 21, wherein the threshold physiologic value is a lower bound of a physiologic value range determined for the patient.

25. The system of claim 21, wherein the threshold physiologic value comprises 130 mmHg.

26. The system of claim 21, wherein one or more of the first predetermined period of time, the second predetermined period of time, and the third predetermined period of time comprises 12 hours.

27. The system of claim 21, wherein the lookup table comprises pacing rate values corresponding to different combinations of heart rate and blood pressure measurements and is personalized for the patient based on the patient's physiological responses to previous pacing adjustments.

28. The system of claim 21, wherein the machine learning model is trained using one or more of one or more of historical physiologic input values, simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and pseudo subjective inputs.

29. The system of claim 21, wherein the predetermined number of consecutive checks comprises 3.

30. The system of claim 21, wherein the second rate of cardiac pacing is lower than the first rate of cardiac pacing.

31. The system of claim 21, wherein the third rate of cardiac pacing is lower than the second rate of cardiac pacing.

32. The system of claim 21, wherein the predetermined number of consecutive additional user inputs is 3.

33. A method for adaptive cardiac pacing comprising: receiving, by a physiological input measuring device, physiological data from a patient, the physiological data comprising a blood pressure; determining, by a processor, that the blood pressure is below a threshold physiologic value;initiating, by a pacing system comprising one or more defibrillators configured to deliver cardiac pacing to the patient, a baseline pacing program, the baseline pacing program comprising a first rate of cardiac pacing determined by one or more of manual input, a lookup table stored in a memory, and a machine learning model stored in the memory; determining, by the processor, that the blood pressure, in response to the baseline pacing program, is less than the threshold physiologic value after a first predetermined period of time; determining, by the processor, that the blood pressure remains below the threshold physiologic value for a predetermined number of consecutive checks; adjusting, by the pacing system, the first rate of cardiac pacing to a second rate of cardiac pacing; receiving, by a user device, a first user input after a second predetermined period of time, the user input comprising a first indication of wellbeing in response to the second rate of cardiac pacing; adjusting, by the pacing system, the second rate of cardiac pacing to a third rate of cardiac pacing in response to the user input; receiving, by the user device, one or more additional user inputs after a third predetermined period of time, the one or more additional user inputs comprising one or more additional indications of wellbeing in response to the third rate of cardiac pacing; determining, by the processor, that a predetermined number of consecutive additional user inputs of the one or more additional user inputs are negative indications of wellbeing; terminating, by the pacing system, the baseline pacing program; and initiating, by the pacing system, a default pacing program comprising a default rate of cardiac pacing.

34. The method of claim 33, wherein the physiological data further comprises one or more of a heart rate, biomarker levels, blood oxygen levels, glucose levels, blood electrolytes levels, accelerometer values, respiratory rate, thoracic impedance, atrial rate, ventricular rate, and atrioventricular conduction.

35. The method of claim 33, wherein the blood pressure is a systolic blood pressure.

36. The method of claim 33, wherein the threshold physiologic value is a lower bound of a physiologic value range determined for the patient.

37. The method of claim 33, wherein the threshold physiologic value comprises 130 mmHg.

38. The method of claim 33, wherein one or more of the first predetermined period of time, the second predetermined period of time, and the third predetermined period of time comprises 12 hours.

39. The method of claim 33, wherein the lookup table comprises pacing rate values corresponding to different combinations of heart rate and blood pressure measurements and is personalized for the patient based on the patient's physiological responses to previous pacing adjustments.

40. The method of claim 33, wherein the machine learning model is trained using one or more of one or more of historical physiologic input values, simulated physiologic input values, subject information, acceptable physiologic value ranges, pacing rates, pacing rate changes, subjective inputs, and pseudo subjective inputs.

41. The method of claim 33, wherein the predetermined number of consecutive checks comprises 3.

42. The method of claim 33, wherein the second rate of cardiac pacing is lower than the first rate of cardiac pacing.

43. The method of claim 33, wherein the third rate of cardiac pacing is lower than the second rate of cardiac pacing.

44. The method of claim 33, wherein the predetermined number of consecutive additional user inputs is 3.

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