Intravascular Heart Pump Prediction for Patient Survival Tracking
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Solution Overview
Problem
Clinicians face challenges in reliably tracking patient health status, particularly for patients with cardiovascular conditions, due to inconsistent and time-consuming qualitative judgments, which hinder timely and informed healthcare decisions.
Innovation Solution
The use of predictive modeling with heart pump systems to determine a heart health index based on continuous measurements and clinical data, enabling quantitative assessment of patient survival probability and adjusting cardiac support levels accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If clinicians use qualitative judgments and indirect estimates to predict patient health status, then the process is simple to perform, but the reliability and consistency of predictions deteriorate
Solution Approach 1:
The patent replaces the mechanical/cognitive system of qualitative clinical judgment with an automated computational system that processes physiological data through predictive models. The system automatically collects data from sensors, applies machine learning algorithms, and generates quantitative health status predictions, eliminating the need for manual qualitative assessment while improving both reliability and consistency of predictions
Solution Approach 2:
The patent introduces an intermediary computational system that acts as a bridge between raw physiological data and clinical decision-making. This intermediary system processes complex data streams through predictive models and presents simplified, actionable insights to clinicians, thereby improving prediction reliability without requiring clinicians to directly analyze complex raw data
2Measurement precision
If clinicians analyze all measurements associated with patient cardiac function, then the accuracy of health status determination improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by automatically collecting, organizing, and pre-processing physiological measurements before they reach the clinician. The system continuously monitors multiple parameters, pre-calculates relevant metrics, and prepares comprehensive analysis results in advance, so that when a clinician needs health status information, it is already processed and ready for immediate review
Solution Approach 2:
The system performs self-service by automatically analyzing all cardiac function measurements without requiring manual clinician intervention. The predictive models autonomously process the data streams, identify patterns, and generate health status predictions, freeing clinicians from time-consuming analysis while maintaining high accuracy through comprehensive data evaluation
3Reliability
If heart pump systems provide continuous data on cardiac function, then the ability to monitor patient condition improves, but the complexity of data processing increases
Solution Approach 1:
The patent applies segmentation by dividing the complex data processing task into distinct functional modules: data collection from sensors, data transmission to processing systems, predictive model analysis, and result presentation. Each module handles a specific aspect of the data flow, making the overall complex system manageable and maintainable while enabling continuous reliable monitoring of patient health status
Data Source
AI summary
Systems and methods are provided herein for treating a patient in cardiogenic shock. An intravascular heart pump system is inserted into vasculature of the patient. The heart pump system has a cannula, pump outlet, pump inlet, and rotor. The heart pump system is positioned within the patient such that the cannula extends across the patient's aortic valve, the pump inlet is located within the patient's left ventricle, and the pump outlet is located within the patient's aorta. Data related to time-varying parameters of the heart pump system is acquired from the heart pump system. A plurality of features are extracted from the data. A probability of survival of the patient is determined based on the plurality of features and using a prediction model. The heart pump system is operated to treat the patient.