Clinical outcomes of myocardial infarction with non-obstructive coronary arteries presenting with diabetic ketoacidosis: a propensity score-matched analysis.

INTRODUCTION AND OBJECTIVE: There is a paucity of data on patients with myocardial infarction with nonobstructive coronary arteries (MINOCA) and a decompensated diabetic state, diabetic ketoacidosis (DKA). Therefore, we aimed to investigate the outcomes of patients with MINOCA presenting with or without DKA. METHODS: We conducted this retrospective propensity score-matched analysis from January 1, 2015, to December 4, 2022. The patients with a principal admission diagnosis of ST-Elevation MI (STEMI) and discharge labeled as MINOCA (ICD-10-CM code 121.9) with DKA were analyzed. We performed a comparative analysis for MINOCA with and without DKA before and after propensity score matching for primary and secondary endpoints. RESULTS: Three thousand five hundred sixty-three patients were analyzed, and 1150 (32.27%) presented with DKA, while 2413 (67.72%) presented as non-DKA. The DKA cohort had over two-fold mortality (5.56% vs. 1.19%; p = 0.024), reinfarction (5.82% vs. 1.45%; p = 0.021), stroke (4.43% vs. 1.36%; p = 0.035), heart failure (6.89% vs. 2.11%; p = 0.033), and cardiogenic shock (6.43% vs. 1.78%; p = 0.025) in a propensity score-matched analysis. There was an increased graded risk of MINOCA with DM (RR (95% CI): 0.50 (0.36-0.86; p = 0.023), DKA (RR (95% CI): 0.46 (0.24-0.67; p = 0.001), and other cardiovascular (CV) risk factors. CONCLUSION: DKA complicates a portion of MINOCA and is associated with increased mortality and major adverse cardiovascular events (MACE).

Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach.

BACKGROUND: Our study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all-cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters. METHODS: The machine learning model was used as a statistical analyzer in recognizing the key risk factors and high-risk features with either outcome of cerebrovascular events or mortality. RESULTS: A total of 2184 patients with their chart data and imaging studies were included and the GBM analysis demonstrated mitral valve area (MVA), right ventricular systolic pressure, pulmonary artery pressure (PAP), left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and surgery as the most significant predictors of transient ischemic attack (TIA/stroke). MVA, PAP, LVEF, creatinine, hemoglobin, and diastolic blood pressure were predictors for all-cause mortality. CONCLUSION: The GBM model assimilates clinical data from all diagnostic modalities and significantly improves risk prediction performance and identification of key variables for the outcome of MS with AFL.