Critical care : the official journal of the Critical Care Forum
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The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. ⋯ An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
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Selective digestive decontamination (SDD) regimens, variously constituted with topical antibiotic prophylaxis (TAP) and protocolized parenteral antibiotic prophylaxis (PPAP), appear highly effective for preventing ICU-acquired infections but only within randomized concurrent control trials (RCCT's). Confusingly, SDD is also a concept which, if true, implies population benefit. The SDD concept can finally be reified in humans using the broad accumulated evidence base, including studies of TAP and PPAP that used non-concurrent controls (NCC), as a natural experiment. However, this test implicates overall population harm with higher event rates associated with SDD use within the ICU context.