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.

中文翻译：

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行政索赔数据中区分 1 型和 2 型心肌梗死的分类算法

背景：2 型心肌梗死 (T2MI) 和 1 型心肌梗死 (T1MI) 在人口统计学、合并症、治疗和临床结果方面有所不同。可靠的质量和结果评估取决于区分行政索赔数据中的 T1MI 和 T2MI 的能力。因此，我们的目标是开发一种分类算法来区分可应用于索赔数据的 T1MI 和 T2MI。 方法：使用新英格兰大型医疗保健系统中 Medicare 责任医疗组织合同中受益人的数据，我们检查了2018 年至 2021 年 MI 诊断代码的分布，以及出院诊断受益人的护理模式和编码国际疾病分类， T2MI第十修订版代码，与 T1MI 代码相比。然后，我们评估了每次住院治疗为 T2MI 与 T1MI 的概率，并检查了 2017 年引入 T2MI 代码之前的护理情况。 结果：应用纳入和排除标准后，仍有 7759 例因心肌梗死住院（46.5% 为 T1MI，53.5% 为心肌梗死）。 % T2MI；平均年龄，79±10.3 岁；47% 女性）。在分类算法中，女性性别（优势比，1.26 [95% CI，1.11–1.44]）、黑人种族相对于白人种族（优势比，2.48 [95% CI，1.76–3.48]）以及新冠肺炎的诊断19（比值比，1.74 [95% CI，1.11–2.71]）或高血压急症（比值比，1.46 [95% CI，1.00–2.14]）与 T2MI 住院几率高于 T1MI 相关。当应用于测试样本时，完整模型的 C 统计量为 0.83。分类的 T2MI 和观察到的 T2MI 的比较表明，在 T2MI 代码之前和之后都可能存在严重错误分类。 结论：一种简单的分类算法似乎能够区分引入 T2MI 代码之前和之后因 T1MI 和 T2MI 住院的情况。这可以促进对急性心肌梗死的质量和结果进行更准确的纵向评估。