The American journal of medicine
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Anemia (either pre-existing or hospital-acquired) is considered an independent predictor of mortality in acute coronary syndromes. However, it is still not clear whether anemia should be considered as a marker of worse health status or a therapeutic target. We sought to investigate the relationship between hospital-acquired anemia and clinical and laboratory findings and to assess the association with mortality and major cardiovascular events at long-term follow-up. ⋯ Hospital-acquired anemia affects one-third of patients hospitalized for acute coronary syndrome and is associated with age, frailty, and comorbidity burden, but was not found to be an independent predictor of long-term mortality.
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The ongoing emergence of novel severe acute respiratory syndrome coronavirus 2 strains such as the Omicron variant amplifies the need for precision in predicting severe COVID-19 outcomes. This study presents a machine learning model, tailored to the evolving COVID-19 landscape, emphasizing novel risk factors and refining the definition of severe outcomes to predict the risk of a patient experiencing severe disease more accurately. ⋯ We offer an improved machine learning model and risk score for predicting severe outcomes during changing COVID-19 strain eras. By emphasizing a more clinically precise definition of severe outcomes, the study provides insights for resource allocation and intervention strategies, aiming to better patient outcomes and reduce health care strain. The necessity for regular model updates is highlighted to maintain relevance amidst the rapidly evolving COVID-19 epidemic.