RESUMO
(1) Background: The impact of armed conflicts on public health is undeniable, with psychological stress emerging as a significant risk factor for cardiovascular disease (CVD). Nevertheless, contemporary data regarding the influence of war on CVD, and especially on acute coronary syndrome (ACS), are scarce. Hence, the aim of the current study was to assess the repercussions of war on the admission and prognosis of patients admitted to a tertiary care center intensive cardiovascular care unit (ICCU). (2) Methods: All patients admitted to the ICCU during the first three months of the Israel-Hamas war (2023) were included and compared with all patients admitted during the same period in 2022. The primary outcome was in-hospital mortality. (3) Results: A total of 556 patients (184 females [33.1%]) with a median age of 70 (IQR 59-80) were included. Of them, 295 (53%) were admitted to the ICCU during the first three months of the war. Fewer Arab patients and more patients with ST-segment elevation myocardial infraction (STEMI) were admitted during the war period (21.8% vs. 13.2%, p < 0.001, and 31.9% vs. 24.1%, p = 0.04, respectively), whereas non-STEMI (NSTEMI) patients were admitted more frequently in the pre-war year (19.3% vs. 25.7%, p = 0.09). In-hospital mortality was similar in both groups (4.4% vs. 3.4%, p = 0.71; HR 1.42; 95% CI 0.6-3.32, p = 0.4). (4) Conclusions: During the first three months of the war, fewer Arab patients and more STEMI patients were admitted to the ICCU. Nevertheless, in-hospital mortality was similar in both groups.
RESUMO
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.