Denervation Response Prediction Using Baseline Patient Parameters
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Solution Overview
Problem
Existing renal denervation therapies for hypertension have variable effectiveness among patients, with some patients not responding well due to differing physiological characteristics, making it difficult for clinicians and patients to assess potential benefits and risks.
Innovation Solution
A computing device uses a computational model to predict the change in blood pressure of a patient before denervation therapy, incorporating patient parameters and weighting coefficients derived from a large patient population, providing a non-invasive and accurate prediction of therapy response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If percutaneous renal denervation procedure is performed to treat hypertension, then blood pressure reduction may be achieved in some patients, but not all patients respond well and some are non-responders
Solution Approach 1:
The patent applies preliminary action by measuring baseline sympathetic nerve activity before denervation therapy to predict patient responsiveness. This pre-assessment allows clinicians to identify likely responders versus non-responders before committing to the invasive procedure, thereby improving therapy reliability by selecting appropriate candidates in advance.
Solution Approach 2:
The patent implements feedback by using baseline sympathetic nerve activity measurements to inform treatment decisions. The measured baseline activity serves as feedback about patient characteristics that predict treatment outcome, allowing the treatment approach to be adapted based on individual patient physiology rather than applying a one-size-fits-all approach.
2Measurement precision
If denervation therapy is delivered to reduce sympathetic nerve activity, then blood pressure reduction may occur, but the extent of reduction varies substantially among patients
Solution Approach 1:
The patent replaces complex clinical judgment and trial-and-error approaches with an automated computational model that processes baseline measurements to predict blood pressure reduction. This substitution of mechanical/computational systems for human decision-making improves measurement precision in predicting outcomes while managing the complexity through algorithmic processing rather than manual assessment.
Solution Approach 2:
The patent utilizes parameter changes by measuring baseline sympathetic nerve activity as a key parameter that correlates with treatment response. By identifying and measuring this specific physiological parameter before treatment, the model can predict the magnitude of blood pressure reduction, transforming a variable outcome into a predictable result based on measurable parameters.
3Productivity
If invasive denervation procedure is performed without predicting responsiveness, then treatment may be delivered, but patient discomfort and healthcare resources are wasted on ineffective procedures
Solution Approach 1:
The patent applies preliminary action by performing non-invasive baseline sympathetic nerve activity measurements before the invasive denervation procedure. This pre-screening identifies patients likely to benefit from treatment, preventing unnecessary invasive procedures on non-responders and thereby eliminating patient discomfort and resource waste associated with ineffective treatments.
Solution Approach 2:
The patent introduces an intermediary predictive model that sits between the decision to treat and the actual invasive procedure. This intermediary assessment tool evaluates baseline parameters and predicts treatment outcomes, serving as a mediator that filters out unlikely responders before they undergo invasive procedures, thus improving healthcare efficiency and reducing patient suffering.
Data Source
AI summary
An example computing system includes a memory and one or more processors coupled to the memory. The one or more processors obtain values for a plurality of patient parameters of a patient with hypertension that relate to a physiological condition of the patient. Before delivery of denervation therapy to the patient, the one or more processors determine, using a computational model, a predicted change in blood pressure of the patient if the denervation therapy is delivered based on the values of the plurality of patient parameters based on the values of the plurality of patient parameters. The one or more processors output, responsive to determining the predicted change in blood pressure of the patient, an indication associated with the predicted change in blood pressure of the patient.