Decision Tree ACS Risk Estimation Using Serial Troponin
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Solution Overview
Problem
Current methods for diagnosing acute coronary syndrome (ACS) in emergency departments face high false positive and false negative rates, leading to adverse outcomes and increased healthcare costs due to inadequate risk stratification of patients, which is not individualized and relies heavily on non-specific troponin measurements.
Innovation Solution
Decision tree-based systems and methods that process cardiac troponin I or T concentrations, their rate of change, age, ECG values, and hematology parameters to estimate the risk of ACS or its comorbidity, using additive algorithms and a database of decision trees to provide personalized risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard troponin measurements and ECG are used for ACS diagnosis, then diagnostic procedures can be performed, but false positive and false negative rates remain high leading to inadequate risk stratification
Solution Approach 1:
The patent segments the diagnostic process into multiple components: ECG analysis, serial troponin measurements, and decision tree risk stratification. By dividing the diagnostic task into these separate analytical segments, each can be optimized independently, improving overall diagnostic accuracy while reducing false positives and negatives in risk stratification.
Solution Approach 2:
The patent adds temporal dimension by measuring troponin at multiple time points (serial measurements) rather than a single measurement. This temporal dimension allows the system to detect trends and rates of change, significantly improving risk stratification accuracy while maintaining high diagnostic precision.
2Measurement precision
If individualized risk assessment is implemented using multiple parameters, then diagnostic accuracy improves, but system complexity increases
Solution Approach 1:
The patent replaces complex manual risk assessment mechanisms with an automated decision tree algorithm system. The decision trees are pre-computed based on multiple parameters (ECG, age, gender, serial troponin measurements), and the system automatically navigates these trees to provide individualized risk assessments, reducing operational complexity while maintaining high accuracy.
Solution Approach 2:
The patent transforms the assessment approach by changing from single-point troponin measurements to multiple parameters including age, gender, ECG findings, and serial troponin rates of change. This parameter transformation enables more precise risk stratification while the decision tree structure manages the complexity of processing multiple parameters systematically.
3Reliability
If comprehensive patient data is collected and processed through decision trees, then false negatives are reduced, but processing time and computational resources increase
Solution Approach 1:
The decision trees are pre-computed and stored before patient assessment. The system navigates pre-structured decision paths that are ready for immediate execution, eliminating the need for real-time complex calculations. This preliminary preparation of risk stratification logic reduces processing time while maintaining comprehensive data analysis that minimizes false negatives.
Solution Approach 2:
The decision tree structure allows the system to skip unnecessary data processing steps by navigating through predetermined paths based on initial findings. If early measurements or ECG results indicate low risk, the system can skip further extensive testing or observation periods, reducing processing time while maintaining high reliability through the structured decision logic.
Data Source
AI summary
The invention provides decision tree based systems and methods for estimating the risk of acute coronary syndrome (ACS) in subjects suspect of having ACS. In particular, systems and methods are provided that employ additive decision tree based algorithms to process a subject's initial cardiac troponin I or T (cTnI or cTnT) concentration, a subject's cTnI or cTnT rate of change, and at least one of the following: the subject's age, the subject's gender, the subject's ECG value, the subject's hematology parameter value, to generate an estimate risk of ACS. Such risk stratification allows, for example, patients to be ruled in or rule out with regard to needing urgent treatment.