Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

Autor: J.M. Smit, J.H. Krijthe, H. Endeman, A.N. Tintu, Y.B. de Rijke, D.A.M.P.J. Gommers, O.L. Cremer, R.J. Bosman, S. Rigter, E.-J. Wils, T. Frenzel, D.A. Dongelmans, R. De Jong, M.A.A. Peters, M.J.A. Kamps, D. Ramnarain, R. Nowitzky, F.G.C.A. Nooteboom, W. De Ruijter, L.C. Urlings-Strop, E.G.M. Smit, D.J. Mehagnoul-Schipper, T. Dormans, C.P.C. De Jager, S.H.A. Hendriks, S. Achterberg, E. Oostdijk, A.C. Reidinga, B. Festen-Spanjer, G.B. Brunnekreef, A.D. Cornet, W. Van den Tempel, A.D. Boelens, P. Koetsier, J.A. Lens, H.J. Faber, A. karakus, R. Entjes, P. De Jong, T.C.D. Rettig, M.S. Arbous, R.C.A. Lalisang, M. Tonutti, D.P. De Bruin, P.W.G. Elbers, J. Van Bommel, M.J.T. Reinders
Přispěvatelé: Intensive care medicine, ACS - Diabetes & metabolism, General practice, Intensive Care, Clinical Chemistry, Intensive Care Medicine, AII - Infectious diseases, APH - Quality of Care, Graduate School, Plastic, Reconstructive and Hand Surgery, Medical Informatics, Paediatric Haematology, General Internal Medicine, Anesthesiology, Medical Biology, ACS - Heart failure & arrhythmias, Amsterdam Reproduction & Development, Biomedical Engineering and Physics, Urology, CCA - Cancer Treatment and Quality of Life, CCA - Imaging and biomarkers, ACS - Atherosclerosis & ischemic syndromes, Radiology and nuclear medicine, Psychiatry, Elderly care medicine, Pediatric surgery, AII - Cancer immunology, CCA - Cancer Treatment and quality of life, CCA - Cancer biology and immunology, Cardio-thoracic surgery, APH - Mental Health
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Intelligence-Based Medicine, 6
Intelligence-based medicine, 6:100071. Elsevier BV
Intelligence-Based Medicine, 6:100071. Elsevier
Smit, J M, Krijthe, J H, Endeman, H, Tintu, A N, de Rijke, Y B, Gommers, D A M P J, Cremer, O L, Bosman, R J, Rigter, S, Wils, E J, Frenzel, T, Dongelmans, D A, de Jong, R, Peters, M A A, Kamps, M J A, Ramnarain, D, Nowitzky, R, Nooteboom, F G C A, de Ruijter, W, Urlings-Strop, L C, Smit, E G M, Mehagnoul-Schipper, D J, Dormans, T, de Jager, C P C, Hendriks, S H A, Achterberg, S, Oostdijk, E, Reidinga, A C, Festen-Spanjer, B, Brunnekreef, G B, Cornet, A D, van den Tempel, W, Boelens, A D, Koetsier, P, Lens, J A, Faber, H J, karakus, A, Entjes, R, de Jong, P, Rettig, T C D, Arbous, M S, Lalisang, R C A, Tonutti, M, de Bruin, D P, Elbers, P W G, van Bommel, J & Reinders, M J T 2022, ' Dynamic prediction of mortality in COVID-19 patients in the intensive care unit : A retrospective multi-center cohort study ', Intelligence-based medicine, vol. 6, 100071 . https://doi.org/10.1016/j.ibmed.2022.100071
Intelligence-based medicine
ISSN: 2666-5212
Popis: Contains fulltext : 287659.pdf (Publisher’s version ) (Open Access) BACKGROUND: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. METHODS: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. RESULTS: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. DISCUSSION: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.
Databáze: OpenAIRE
Externí odkaz: