Machine-Learning Decision Support for Single-Test Myocardial Infarction Risk
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Solution Overview
Problem
Existing diagnostic pathways for myocardial infarction, such as those using high-sensitivity cardiac troponin assays, have limitations including fixed thresholds that do not account for age, sex, or comorbidities, require multiple measurements over time, and do not consider other important clinical factors, leading to delayed diagnosis and potential worsening outcomes.
Innovation Solution
A machine learning-based method using a single cardiac biomarker measurement, combined with clinical indicators like age, sex, and other factors, to calculate an individualized probability of myocardial infarction, utilizing an XGBoost model for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed cardiac troponin thresholds are used for all subjects, then the diagnostic pathway is simple and easy to implement, but it does not account for age, sex or comorbidities which are known to influence troponin concentrations
Solution Approach 1:
The patent changes the parameter from fixed thresholds to dynamic, individualized probability scores that adjust based on age, sex, and comorbidities. The model outputs a probability score between 0 and 100 that is calibrated for each subject's specific characteristics, allowing the same diagnostic pathway to achieve both simplicity and precision.
2Measurement precision
If serial cardiac troponin measurements are performed at specific time points, then the diagnosis accuracy is improved, but the time to diagnosis is delayed by hours and adherence is challenging in busy Emergency Departments
Solution Approach 1:
The patent performs preliminary action by calculating the probability score at the single initial troponin measurement time point, incorporating all necessary clinical information (age, sex, comorbidities, symptom duration, ECG findings) into the assessment upfront. This eliminates the need for waiting multiple time points and allows for immediate risk stratification and diagnosis.
3Ease of operation
If only age, sex and cardiac troponin concentration are used to calculate the MI3 score, then the implementation is facilitated due to simplicity, but the diagnostic performance is limited by not including other important subject factors
Solution Approach 1:
The patent achieves universality by creating a single diagnostic pathway that works across all subject subgroups (different ages, sexes, and comorbidity profiles) using one unified probability score calculation. The model incorporates multiple variables (age, sex, comorbidities, symptom duration, ECG findings) into a single integrated assessment that can be applied universally, improving diagnostic performance without complicating implementation.
4Productivity
If a single cardiac troponin measurement is used at presentation, then the speed of diagnosis is improved and duration of stay is shortened, but the accuracy is reduced due to not considering temporal changes in troponin levels
Solution Approach 1:
The patent introduces an intermediary - the probability score calculation - that mediates between the single troponin measurement and the final diagnostic accuracy. This intermediary integrates the single measurement with clinical context (age, sex, comorbidities, symptom duration, ECG) to produce an accurate probability assessment without requiring multiple time-point measurements, thus maintaining both speed and accuracy.
Data Source
AI summary
A computer implemented method of providing and an indication of the probability of myocardial infarction using cardiac biomarker measurements comprises combining measured with other clinical indicators in statistical model to compute the probability of a subject having suffered myocardial infarction, the statistical model using a machine learning algorithm. A decision tool and a system for implementing the method are disclosed.