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Prognostic Implications of Machine Learning Algorithm-Supported Diagnostic Classification of Myocardial Injury Using
Kristina Lambrakis1, Ehsan Khan2, Zhibin Liao3
1College of Medicine and Public Health, Flinders University of South Australia, Adelaide, SA, Australia; Victorian Heart Institute, Monash University, Melbourne, Vic, Australia; MonashHeart, Monash Health, Melbourne, Vic, Australia.
Insights
High-sensitivity troponin assays identify more myocardial injury cases. Type 1 myocardial infarction (T1MI) carries the highest short-term risk for recurrent heart attacks, while other injuries increase long-term heart failure risk.
Area of Science:
- Cardiology
- Biomarkers
- Machine Learning in Healthcare
Background:
- High-sensitivity troponin assays increase identification of myocardial injury.
- Type 1 myocardial infarction (T1MI) is less common but has established evidence.
- Understanding temporal cardiovascular event risks across myocardial injury types is crucial.
Purpose of the Study:
- To assess the time course of cardiovascular events in different myocardial injury classifications.
- To compare risks of mortality, recurrent myocardial infarction, heart failure, and arrhythmia over three years.
Main Methods:
- Utilized machine learning algorithms to classify myocardial injury (T1MI, acute/type 2 myocardial infarction [T2MI], chronic injury, no injury) from hospital encounters.
- Analyzed temporal hazard for major adverse cardiovascular events over three years.
Main Results:
- Among 176,787 episodes, T1MI was 6.9%, acute/T2MI was 6.0%, and chronic injury was 26.7%.
- All injury types showed early mortality risk; T1MI had the highest short-term recurrent myocardial infarction risk.
- Acute/T2MI and chronic injury demonstrated persistent long-term heart failure risk, unlike T1MI.
Conclusions:
- Non-T1MI myocardial injury classifications are associated with substantial and persistent late cardiac events.
- There is a need for evidence-based therapeutic strategies for non-T1MI.
- Opportunities exist to improve long-term outcomes for all myocardial injury types with existing and novel therapies.
Background:
With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myocardial infarction (T1MI) being less common despite having the most well-established evidence base to inform care. This study assesses the temporal time course of cardiovascular events among various forms of myocardial injury.
Method:
Consecutive hospital encounters were identified. Using the first episode of care during the sampling period, myocardial injury classifications (i.e., T1MI, acute injury/type 2 myocardial infarction [T2MI], chronic injury, and no injury) were established via two machine learning algorithms. The temporal time course of increased hazard for mortality, recurrent myocardial infarction, heart failure, and arrhythmia over 3 years were explored.
Results:
There were 176,787 index episodes; 6.9% were classified as T1MI, 6.0% as acute injury/T2MI, and 26.7% as chronic injury. Although each classification was associated with an early increased risk of all-cause mortality compared with no injury (incidence rate ratio [IRR]<30 days: T1MI: 19.97 [95% confidence interval 12.50-32.69]; acute injury/T2MI: 26.51 [16.80-42.97]; chronic injury: 15.37 [10.22-23.95]), the instantaneous relative hazard for recurrent myocardial infarction was highest in those with initial T1MI (IRR<30 days: T1MI: 28.81 [22.75-36.76]; acute injury/T2MI: 10.23 [7.60-13.77]; chronic injury:5.54 [4.34-7.41]). In contrast, the instantaneous hazard for heart failure in those with initial acute injury/T2MI and chronic injury remained increased over long-term follow up unlike in T1MI (IRR1 3 yrs: T1MI: 5.52 [4.99-6.09]; acute injury/T2MI: 10.36 [9.51-11.30]; chronic injury:7.40 [6.90-7.94]).
Conclusions:
The substantial and persistent rate of late cardiac events highlights the need to establish an evidence base for the therapeutic management of "non-T1MI" diagnostic classifications and suggests opportunity to improve late outcomes using existing and emerging therapies.
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