Journal of cardiac failure
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Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms. This study aimed to enhance the ICD algorithm for HF definition by incorporating comprehensive information from EMRs. ⋯ Applying natural language processing and machine learning on the discharge summaries of inpatient EMR data can improve the capture of cases of HF compared with the widely used ICD algorithm. The utility of the HFC algorithm is straightforward, making it easily applied for HF case identification.
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Coronavirus disease 2019 (COVID-19) is a pandemic that has affected more than 1.8 million people worldwide, overwhelmed health care systems owing to the high proportion of critical presentations, and resulted in more than 100,000 deaths. Since the first data analyses in China, elevated cardiac troponin has been noted in a substantial proportion of patients, implicating myocardial injury as a possible pathogenic mechanism contributing to severe illness and mortality. Accordingly, high troponin levels are associated with increased mortality in patients with COVID-19. This brief review explores the available evidence regarding the association between COVID-19 and myocardial injury.