Marcel J. H. Aries, Auke C Reidinga, Lucas A. Ramos, Kim C. E. Sigaloff, Wouter V. Potters, Suat Simsek, Iwan C. C. van der Horst, Peter G. Noordzij, Tom Dormans, Caroline E. Wyers, Henk A. Marquering, Maarten C. Ottenhoff, Ella H C van den Hout, Michiel Schinkel, Dan Piña-Fuentes, Guido van Wingen, Deborah Hubers, Lianne de Haan, Roger J M W Rennenberg, Rajat M. Thomas, Daisy Rusch, W. Joost Wiersinga, Christian Herff, Marcus L.F. Janssen, Shi Hu, Renée A. Douma, Niels C. Gritters van den Oever, Egill Axfjord Fridgeirsson, Martijn D. de Kruif, Max Welling, Joop P. W. van den Bergh, Martijn Beudel, Paul W. G. Elbers, Pieter L. Kubben, Lucas M Fleuren, David T B Buis, RS: MHeNs - R3 - Neuroscience, Neurochirurgie, MUMC+: HZC Med Staf Spec Klinische Neurofys (9), Klinische Neurowetenschappen, Intensive Care, MUMC+: MA Intensive Care (3), MUMC+: MA Medische Staf IC (9), RS: Carim - V04 Surgical intervention, MUMC+: MA Med Staf Spec Neurochirurgie (9), Interne Geneeskunde, MUMC+: MA Alg Interne Geneeskunde (9), RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Neurology, Amsterdam Neuroscience - Systems & Network Neuroscience, Intensive care medicine, Internal medicine, APH - Quality of Care, ACS - Diabetes & metabolism, Amsterdam Machine Learning lab (IVI, FNWI), IvI Research (FNWI), Video & Image Sense Lab (IvI, FNWI), Biomedical Engineering and Physics, Adult Psychiatry, ACS - Atherosclerosis & ischemic syndromes, APH - Methodology, ANS - Brain Imaging, ANS - Cellular & Molecular Mechanisms, ANS - Compulsivity, Impulsivity & Attention, ANS - Neurovascular Disorders, AMS - Amsterdam Movement Sciences, Graduate School, Radiology and Nuclear Medicine, Center of Experimental and Molecular Medicine, Infectious diseases, AII - Infectious diseases, APH - Mental Health, ANS - Complex Trait Genetics, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, and ANS - Neurodegeneration
ObjectiveDevelop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.DesignRetrospective cohort study.SettingA multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.ParticipantsSARS-CoV-2 positive patients (age ≥18) admitted to the hospital.Main outcome measures21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).ConclusionBoth models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.