ArticleCirculation. Cardiovascular quality and outcomes2024
Classification Algorithm to Distinguish Between Type 1 and Type 2 Myocardial Infarction in Administrative Claims Data.
Article in Circulation. Cardiovascular quality and outcomes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Improving classification of myocardial infarction with machine learning in a diverse population.American journal of epidemiology · 2026Article
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6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundType 2 myocardial infarction (T2MI) and type 1 myocardial infarction (T1MI) differ with respect to demographics, comorbidities, treatments, and clinical outcomes. Reliable quality and outcomes assessment depends on the ability to distinguish between T1MI and T2MI in administrative claims data. As such, we aimed to develop a classification algorithm to distinguish between T1MI and T2MI that could be applied to claims data.
methodsUsing data for beneficiaries in a Medicare accountable care organization contract in a large health care system in New England, we examined the distribution of MI diagnosis codes between 2018 to 2021 and the patterns of care and coding for beneficiaries with a hospital discharge diagnosis
resultsAfter application of inclusion and exclusion criteria, 7759 hospitalizations for myocardial infarction remained (46.5% T1MI and 53.5% T2MI; mean age, 79±10.3 years; 47% female). In the classification algorithm, female gender (odds ratio, 1.26 [95% CI, 1.11-1.44]), Black race relative to White race (odds ratio, 2.48 [95% CI, 1.76-3.48]), and diagnoses of COVID-19 (odds ratio, 1.74 [95% CI, 1.11-2.71]) or hypertensive emergency (odds ratio, 1.46 [95% CI, 1.00-2.14]) were associated with higher odds of the hospitalization being for T2MI versus T1MI. When applied to the testing sample, the C-statistic of the full model was 0.83. Comparison of classified T2MI and observed T2MI suggest the possibility of substantial misclassification both before and after the T2MI code.
conclusionsA simple classification algorithm appears to be able to differentiate between hospitalizations for T1MI and T2MI before and after the T2MI code was introduced. This could facilitate more accurate longitudinal assessments of acute myocardial infarction quality and outcomes.
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