ABSTRACT
Introduction: Despite ongoing efforts to minimize sex bias in diagnosis and treatment of acute coronary syndrome (ACS), data still shows outcomes differences between sexes including higher risk of all-cause mortality rate among females. Hence, the aim of the current study was to examine sex differences in ACS in-hospital mortality, and to implement artificial intelligence (AI) models for prediction of in-hospital mortality among females with ACS. Methods: All ACS patients admitted to a tertiary care center intensive cardiac care unit (ICCU) between July 2019 and July 2023 were prospectively enrolled. The primary outcome was in-hospital mortality. Three prediction algorithms, including gradient boosting classifier (GBC) random forest classifier (RFC), and logistic regression (LR) were used to develop and validate prediction models for in-hospital mortality among females with ACS, using only available features at presentation. Results: A total of 2,346 ACS patients with a median age of 64 (IQR: 56-74) were included. Of them, 453 (19.3%) were female. Female patients had higher prevalence of NSTEMI (49.2% vs. 39.8%, p < 0.001), less urgent PCI (<2â h) rates (40.2% vs. 50.6%, p < 0.001), and more complications during admission (17.7% vs. 12.3%, p = 0.01). In-hospital mortality occurred in 58 (2.5%) patients [21/453 (5%) females vs. 37/1,893 (2%) males, HR = 2.28, 95% CI: 1.33-3.91, p = 0.003]. GBC algorithm outscored the RFC and LR models, with area under receiver operating characteristic curve (AUROC) of 0.91 with proposed working point of 83.3% sensitivity and 82.4% specificity, and area under precision recall curve (AUPRC) of 0.92. Analysis of feature importance indicated that older age, STEMI, and inflammatory markers were the most important contributing variables. Conclusions: Mortality and complications rates among females with ACS are significantly higher than in males. Machine learning algorithms for prediction of ACS outcomes among females can be used to help mitigate sex bias.
ABSTRACT
BACKGROUND: Hemoglobin A1C (HbA1c) is a form of glycated hemoglobin used to estimate glycemic control in diabetic patients. Data regarding the prognostic significance of HbA1c levels in contemporary intensive cardiac care unit (ICCU) patients is limited. METHODS: All patients admitted to the ICCU at a tertiary care medical center between January 1, 2020, and June 30, 2021, with documented admission HbA1c levels were included in the study. Patients were divided into 3 groups according to their HbA1c levels: < 5.7 g% [no diabetes mellitus (DM)], 5.7-6.4 g% (pre-DM), ≥ 6.5 g% (DM). RESULTS: A total of 1412 patients were included. Of them, 974 (69%) were male with a mean age of 67(± 15.7) years old. HbA1c level < 5.7 g% was found in 550 (39%) patients, 5.7-6.4 g% in 458 (32.4%) patients and ≥ 6.5 g% in 404 (28.6%) patients. Among patients who did not know they had DM, 81 (9.3%) patients had high HbA1c levels (≥ 6.5 g%) on admission. The crude mortality rate at follow-up (up to 1.5 years) was almost twice as high among patients with pre-DM and DM than in patients with no DM (10.6% vs. 5.4%, respectively, p = 0.01). Interestingly, although not statistically significant, the trend was that pre-DM patients had the strongest association with mortality rate [HR 1.83, (95% CI 0.936-3.588); p = 0.077]. CONCLUSIONS: Although an HbA1c level of ≥ 5.7 g% (pre-DM & DM) is associated with a worse prognosis in patients admitted to ICCU, pre-DM patients, paradoxically, have the highest risk for short and long-term mortality rates.