Classification Algorithm to Distinguish Between Type 1 and Type 2 Myocardial Infarction in Administrative Claims Data

• Published in: Circulation Cardiovascular quality and outcomes Vol. 17; no. 2; p. e009986
• Main Authors: Wasfy, Jason H., Price, Mary, Normand, Sharon-Lise T., Januzzi, James L., McCarthy, Cian P., Hsu, John
• Format: Journal Article
• Language: English
• Published: Hagerstown, MD Lippincott Williams & Wilkins 01.02.2024
• Subjects: Aged; Aged, 80 and over; Algorithms; Comorbidity; Female; Humans; Male; Medicare; Myocardial Infarction - diagnosis; Myocardial Infarction - epidemiology; Myocardial Infarction - therapy; New England; United States - epidemiology; United States; New England; epidemiology; hospitalization; Medicare; classification; myocardial infarction
• ISSN: 1941-7713; 1941-7705; 1941-7705
• DOI: 10.1161/CIRCOUTCOMES.123.009986

BACKGROUND: Type 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. METHODS: Using 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 International Classification of Diseases, Tenth Revision code for T2MI, compared with those for T1MI. We then assessed the probability that each hospitalization was for a T2MI versus T1MI and examined care occurring in 2017 before the introduction of the T2MI code. RESULTS: After 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. CONCLUSIONS: A 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.