Predictive Model for Cardiorespiratory Deterioration
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Solution Overview
Problem
Current methods for predicting cardiorespiratory deterioration in patients with single ventricle lesions, such as hypoplastic left heart syndrome, are limited by inaccuracy, speed, and efficiency, particularly in identifying subtle changes in cardiorespiratory dynamics that precede life-threatening events.
Innovation Solution
A predictive model using continuous physiological measurements from patients with parallel systemic and pulmonary circulations, incorporating a risk index derived from multivariate regression analysis, to detect imminent deterioration by comparing current physiology to pre-deterioration states, enabling early intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vital sign monitoring methods are used, then the monitoring process is simple, but the accuracy and speed of predicting cardiorespiratory deterioration are insufficient
Solution Approach 1:
The patent segments the prediction task into multiple components: extracting features from different physiological signals (ECG, respiratory rate, oxygen saturation), computing separate statistical metrics for each signal type, and combining them into a composite risk score. This segmentation allows complex prediction accuracy to be achieved through modular processing of individual signal components.
Solution Approach 2:
The patent introduces an intermediary computational layer that processes raw physiological signals through statistical analysis and feature extraction. This intermediary layer transforms complex raw data into meaningful predictive features, enabling accurate deterioration prediction without requiring direct complex monitoring of all underlying physiological processes.
2Speed
If traditional monitoring methods are used, then the system is easy to operate, but the speed of detecting deterioration is too slow
Solution Approach 1:
The patent performs preliminary computational actions by continuously calculating statistical features (mean, standard deviation, trends) from physiological signals in real-time. This preliminary processing of data enables rapid detection of deterioration patterns without requiring complex real-time analysis when deterioration actually occurs, thus improving speed while maintaining operational simplicity.
3Measurement precision
If detailed physiological analysis is performed to improve prediction accuracy, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The patent changes the parameters of analysis by focusing on specific statistical features (mean, standard deviation, trends) of physiological signals rather than analyzing all possible signal characteristics. This parameter selection achieves high prediction accuracy by concentrating computational resources on the most discriminative features, thereby reducing overall computational complexity.
Data Source
AI summary
A system generates and displays a clinical metric based on continuously collected patient physiological data, wherein the clinical metric provides a predictive measure of the likelihood of the onset of a cardiorespiratory deterioration event in the patient in a predetermined time period in the future. If the clinical metric has a configured relationship with a predetermined threshold value, embodiments may generate an alarm in addition to or instead of displaying the clinical metric. The clinical metric thus allows clinical staff to take medically indicated actions to prevent or reduce the effects of the predicted deterioration.