Revista espanola de cardiologia最新文献

Introduction and objectives

Hypercoagulability and thromboembolism are processes that arise from severe acute respiratory syndrome coronavirus 2 infection and are responsible for a high degree of coronavirus disease 2019 (COVID-19)-related morbidity and mortality. This study sought to assess the effect of antiplatelet drugs on COVID-19 severity (risk of hospitalization and mortality), susceptibility to severe acute respiratory syndrome coronavirus 2 infection, and progression to severe COVID-19.

Methods

We conducted a population-based case-control study in a northwestern region of Spain in 2020. The study involved 3060 participants with a positive polymerase chain reaction test who were hospitalized, 26 757 participants with a positive polymerase chain reaction test who were not hospitalized, and 56 785 healthy controls.

Results

Triflusal seemed to be associated with a significant increase in risk of hospitalization (aOR, 1.97; 95%CI, 1.27-3.04) and susceptibility to infection (OR, 1.45; 95%CI, 1.07-1.96). It also appeared to lead to a nonsignificant increase in the risk of mortality (OR, 2.23; 95%CI, 0.89-5.55) and/or progression to more severe disease stages (OR, 1.42; 95%CI, 0.8-2.51). Aspirin seemed to be associated with a statistically significant decrease in susceptibility to severe acute respiratory syndrome coronavirus 2 infection (OR, 0.92; 95%CI, 0.86-0.98).

Conclusions

Triflusal use appears to increase the risk of susceptibility to COVID-19 infection and an even higher risk of hospitalization, whereas the other antiplatelets could be associated with a reduction in the risk of the various outcomes or have no effect on risk. These findings could support reconsideration of triflusal prescription in COVID-19 pandemic situations.

Introduction and objectives

The present study sought to establish the diagnostic yield of cardiovascular magnetic resonance (CMR) in a large cohort of patients admitted with myocardial infarction (MI) with nonobstructive coronary artery disease (MINOCA) based on the timing of referral to CMR.

Methods

Consecutive patients referred to CMR from January 2009 to February 2022 with a working diagnosis of MINOCA were retrospectively evaluated. Cine, T₂-weighted, early, and late gadolinium-enhanced images were acquired and analyzed. The frequency of the underlying diagnosis and the association between timing of CMR and relative frequency of each diagnosis were assessed.

Results

We included 207 patients (median age 50 years, 60% men). Final diagnosis after CMR was achieved in 91% of the patients (myocarditis in 45%, MI in 20%, tako-tsubo cardiomyopathy in 19%, and other cardiomyopathies in 7%). The performance of CMR within 7 days of admission with MINOCA (median, 5 days; 117 patients) allowed a higher diagnostic yield compared with CMR performed later (median, 10 days; 88 patients) (96% vs 86%, P = .02). Although myocarditis was the most frequent diagnosis in both groups according to time to CMR, its frequency was higher among patients with a CMR performed within the first 7 days (53% vs 35%, P = .02). The frequency of other underlying diagnoses was not influenced by CMR timing.

Conclusions

CMR led to an underlying diagnosis of MINOCA in 91% of patients and its diagnostic yield increased to 96% when CMR was performed within 7 days of admission. The most frequent diagnosis was myocarditis.

Introduction and objectives

Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predictive model for delirium for patients in the CICU.

Methods

This study included consecutive patients admitted to the CICU of the Samsung Medical Center. To assess the candidate variables for the model: we applied the following machine learning methods: random forest, extreme gradient boosting, partial least squares, and Plmnet-elastic.net. After selecting relevant variables, we performed a logistic regression analysis to derive the model formula. Internal validation was conducted using 100-repeated hold-out validation.

Results

We analyzed 2774 patients, 677 (24.4%) of whom developed delirium in the CICU. Machine learning-based models showed good predictive performance. Clinically significant and frequently important predictors were selected to construct a delirium prediction scoring model for CICU patients. The model included albumin level, international normalized ratio, blood urea nitrogen, white blood cell count, C-reactive protein level, age, heart rate, and mechanical ventilation. The model had an area under the receiver operating characteristics curve (AUROC) of 0.861 (95%CI, 0.843-0.879). Similar results were obtained in internal validation with 100-repeated cross-validation (AUROC, 0.854; 95%CI, 0.826-0.883).

Conclusions

Using variables frequently ranked as highly important in four machine learning methods, we created a novel delirium prediction model. This model could serve as a useful and simple tool for risk stratification for the occurrence of delirium at the patient's bedside in the CICU.

Introduction and objectives

Methods

Results

Conclusions