Adaptive MAP Threshold Detection for Acute Hypotension Prediction
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Solution Overview
Problem
Current monitoring systems in operating rooms and intensive care units rely on reactive treatments for hypotension events after low blood pressure values occur, failing to predict these events effectively due to the use of a single, hard blood pressure threshold that does not account for individual patient variability and compensatory mechanisms.
Innovation Solution
A method and apparatus that adaptively detect acute hypotension episodes by determining personalized mean arterial pressure (MAP) thresholds for each patient, using machine-learning algorithms to predict AHE events before they become clinically apparent, allowing for preemptive treatment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single hard blood pressure threshold value is used to detect AHE events, then the detection system is simple and easy to operate, but it fails to account for individual patient variability and compensatory mechanisms, reducing measurement precision
Solution Approach 1:
The patent transforms the static, fixed threshold approach into a dynamic, adaptive system that continuously learns and adjusts threshold values based on individual patient data. The machine learning model updates threshold parameters in real-time to reflect patient-specific physiology and compensatory mechanisms, resolving the contradiction between operational simplicity and measurement precision.
Solution Approach 2:
The invention changes the threshold parameter from a fixed constant to a variable that adapts to individual patient characteristics. By using machine learning to dynamically adjust threshold values based on patient-specific data, the system maintains ease of operation while significantly improving measurement precision through personalized parameter adaptation.
2Ease of manufacture
If reactive treatment is applied after low blood pressure values occur, then the treatment approach is simple and direct, but the duration and severity of AHE events increase, worsening patient outcomes
Solution Approach 1:
The patent implements preliminary action by predicting AHE events before they occur using machine learning algorithms that analyze trends in blood pressure data. This allows clinicians to intervene preemptively rather than reactively, reducing the duration and severity of hypotension episodes while maintaining treatment simplicity through automated prediction alerts.
3Measurement precision
If personalized MAP threshold values are determined using machine-learning algorithms, then the prediction accuracy of AHE events improves, but the device complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically learn and adapt to individual patient characteristics without requiring manual configuration or complex external interventions. The machine learning model autonomously processes patient data, identifies patterns, and generates personalized threshold values, improving prediction accuracy while managing complexity through automated self-adjustment.
4Device complexity
If a fixed MAP threshold is used for all patients, then the monitoring system is simple and consistent, but it cannot detect AHE events early enough for effective pre-emptive intervention
Solution Approach 1:
The patent enables preliminary action by using machine learning to predict AHE events before they occur, providing early warning signals that allow clinicians to intervene preemptively. This reduces the loss of time for effective intervention while managing system complexity through automated prediction algorithms that learn from patient-specific data patterns.
Data Source
AI summary
The present disclosure provides a method and an apparatus for adaptively detecting an acute hypotension episode (AHE) event of a patient, the method comprising: processing, by a processor, a signal relating to the patient to determine at least one mean arterial pressure (MAP) threshold value for the patient (101); and detecting, by the processor, an AHE event of the patient based on the at least one MAP threshold value (102).