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Vagliano I, Schut MC, Abu-Hanna A, Dongelmans DA, de Lange DW, Gommers D, Cremer OL, Bosman RJ, Rigter S, Wils EJ, Frenzel T, de Jong R, Peters MAA, Kamps MJA, Ramnarain D, Nowitzky R, Nooteboom FGCA, de Ruijter W, Urlings-Strop LC, Smit EGM, Mehagnoul-Schipper DJ, Dormans T, de Jager CPC, Hendriks SHA, Achterberg S, Oostdijk E, Reidinga AC, Festen-Spanjer B, Brunnekreef GB, Cornet AD, van den Tempel W, Boelens AD, Koetsier P, Lens J, Faber HJ, Karakus A, Entjes R, de Jong P, Rettig TCD, Reuland MC, Arbous S, Fleuren LM, Dam TA, Thoral PJ, Lalisang RCA, Tonutti M, de Bruin DP, Elbers PWG, de Keizer NF. Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records. Int J Med Inform 2022; 167:104863. [PMID: 36162166 PMCID: PMC9492397 DOI: 10.1016/j.ijmedinf.2022.104863] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/19/2022] [Accepted: 09/03/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data. METHODS Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors. RESULTS A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors. CONCLUSION In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.
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Affiliation(s)
- Iacopo Vagliano
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - Martijn C Schut
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Dave A Dongelmans
- National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Dylan W de Lange
- National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands
| | - Diederik Gommers
- Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Olaf L Cremer
- Intensive Care, UMC Utrecht, Utrecht, The Netherlands
| | | | - Sander Rigter
- Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Remko de Jong
- Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands
| | - Marco A A Peters
- Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands
| | - Marlijn J A Kamps
- Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, The Netherlands
| | | | - Ralph Nowitzky
- Intensive Care, Haga Ziekenhuis, Den Haag, The Netherlands
| | | | - Wouter de Ruijter
- Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands
| | | | - Ellen G M Smit
- Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands
| | | | - Tom Dormans
- Intensive care, Zuyderland MC, Heerlen, The Netherlands
| | | | | | | | | | - Auke C Reidinga
- ICU, SEH, BWC, Martiniziekenhuis, Groningen, The Netherlands
| | | | - Gert B Brunnekreef
- Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Alexander D Cornet
- Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Walter van den Tempel
- Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands
| | - Age D Boelens
- Anesthesiology, Antonius Ziekenhuis Sneek, Sneek, The Netherlands
| | - Peter Koetsier
- Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands
| | - Judith Lens
- ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, The Netherlands
| | | | - A Karakus
- Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands
| | - Robert Entjes
- Department of Intensive Care, Adrz, Goes, The Netherlands
| | - Paul de Jong
- Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands
| | - Thijs C D Rettig
- Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands
| | - M C Reuland
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | | | - Lucas M Fleuren
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Tariq A Dam
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick J Thoral
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | | | | | | | - Paul W G Elbers
- Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands
| | - Nicolette F de Keizer
- Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands
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