Predictive models of intensive care unit admission in patients with covid-19: systematic review - Castañeda-Sabogal, Alex (2022)
Background: It is essential to identify the epidemiological and clinical characteristics of patients infected with COVID-19 associated with disease progression leading to ICU admission. The objective was to systematically review the models or scores for predicting admission to the intensive care unit (ICU) available to date for patients with COVID-19. Methods: The study is a systematic review. PubMed, Scopus, Web of Science, Ovid-Medline, and Embase were searched until July 13, 2022. We included studies that have developed and validated a model or scoring system to predict ICU admission in patients with COVID-19. The primary outcome was ICU admission. Risk of bias assessment was performed using the PROBAST tool which is based on four domains: participants, predictors, outcome and analysis. Results: Two studies were included for data extraction and critical appraisal. Predictive models of ICU admission and performance were obtained as primary outcomes. Common predictors for both models were associated with pulmonary compromise (respiratory rate or pulmonary ventilation) and systemic inflammation (C-reactive protein). Conclusions: It is feasible to determine predictor variables for ICU admission in patients hospitalized for COVID-19. However, the studies do not determine a clearly defined score and present a high risk of bias, so it is not feasible to recommend the application of any of these models in clinical practice.