Prognostic factors of coronavirus disease 2019 (COVID-19) patients among European population are lacking. Our objective was to identify early prognostic factors upon admission to optimize the management of COVID-19 patients hospitalized in a medical ward. This French single-center prospective cohort study evaluated 152 patients with positive severe acute respiratory syndrome coronavirus 2 real-time reverse transcriptase-polymerase chain reaction assay, hospitalized in the Internal Medicine and Clinical Immunology Department, at Pitié-Salpêtrière's Hospital, in Paris, France, a tertiary care university hospital. Predictive factors of intensive care unit (ICU) transfer or death at day 14 (D14), of being discharge alive and severe status at D14 (remaining with ventilation, or death) were evaluated in multivariable logistic regression models; models' performances, including discrimination and calibration, were assessed (C-index, calibration curve, R 2 , Brier score). A validation was performed on an external sample of 132 patients hospitalized in a French hospital close to Paris, in Aulnay-sous-Bois, Île-de-France. The probability of ICU transfer or death was 32% (47/147) (95% CI 25-40). Older age (OR 2.61, 95% CI 0.96-7.10), poorer respiratory presentation (OR 4.04 per 1-point increment on World Health Organization (WHO) clinical scale, 95% CI 1.76-9.25), higher CRP-level (OR 1.63 per 100mg/L increment, 95% CI 0.98-2.71) and lower lymphocytes count (OR 0.36 per 1000/mm 3 increment, 95% CI 0.13-0.99) were associated with an increased risk of ICU requirement or death. A 9-point ordinal

BACKGROUND: Acute hypotensive episodes (AHE), defined as a drop in the mean arterial pressure (MAP) <65 mm Hg lasting at least 5 consecutive minutes, are among the most critical events in the intensive care unit (ICU). They are known to be associated with adverse outcome in critically ill patients. AHE prediction is of prime interest because it could allow for treatment adjustment to predict or shorten AHE. METHODS: The Super Learner (SL) algorithm is an ensemble machine-learning algorithm that we specifically trained to predict an AHE 10 minutes in advance. Potential predictors included age, sex, type of care unit, severity scores, and time-evolving characteristics such as mechanical ventilation, vasopressors, or sedation medication as well as features extracted from physiological signals: heart rate, pulse oximetry, and arterial blood pressure. The algorithm was trained on the Medical Information Mart for Intensive Care dataset (MIMIC II) database. Internal validation was based on the area under the receiver operating characteristic curve (AUROC) and the Brier score (BS). External validation was performed using an external dataset from Lariboisière hospital, Paris, France. RESULTS: Among 1151 patients included, 826 (72%) patients had at least 1 AHE during their ICU stay. Using 1 single random period per patient, the SL algorithm with Haar wavelets transform preprocessing was associated with an AUROC of 0.929 (95% confidence interval [CI], 0.899–0.958) and a BS of 0.08. Using all available periods for each patient, SL with Haar wavelets transform preprocessing was associated with an AUROC of 0.890 (95% CI, 0.886–0.895) and a BS of 0.11. In the external validation cohort, the AUROC reached 0.884 (95% CI, 0.775–0.993) with 1 random period per patient and 0.889 (0.768–1) with all available periods and BSs <0.1. CONCLUSIONS: The SL algorithm exhibits good performance for the prediction of an AHE 10 minutes ahead of time. It allows an efficient, robust, and rapid evaluation of the risk of hypotension that opens the way to routine use.