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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.
Vagliano, Iacopo; Schut, Martijn C; Abu-Hanna, Ameen; Dongelmans, Dave A; de Lange, Dylan W; Gommers, Diederik; Cremer, Olaf L; Bosman, Rob J; Rigter, Sander; Wils, Evert-Jan; Frenzel, Tim; de Jong, Remko; Peters, Marco A A; Kamps, Marlijn J A; Ramnarain, Dharmanand; Nowitzky, Ralph; Nooteboom, Fleur G C A; de Ruijter, Wouter; Urlings-Strop, Louise C; Smit, Ellen G M; Mehagnoul-Schipper, D Jannet; Dormans, Tom; de Jager, Cornelis P C; Hendriks, Stefaan H A; Achterberg, Sefanja; Oostdijk, Evelien; Reidinga, Auke C; Festen-Spanjer, Barbara; Brunnekreef, Gert B; Cornet, Alexander D; van den Tempel, Walter; Boelens, Age D; Koetsier, Peter; Lens, Judith; Faber, Harald J; Karakus, A; Entjes, Robert; de Jong, Paul; Rettig, Thijs C D; Reuland, M C; Arbous, Sesmu; Fleuren, Lucas M; Dam, Tariq A; Thoral, Patrick J; Lalisang, Robbert C A; Tonutti, Michele; de Bruin, Daan P; Elbers, Paul W G; de Keizer, Nicolette F.
  • Vagliano I; Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. Electronic address: i.vagliano@amsterdamumc.nl.
  • Schut MC; Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
  • Abu-Hanna A; Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
  • Dongelmans DA; National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
  • de Lange DW; National Intensive Care Evaluation (NICE) foundation, Amsterdam, The Netherlands; Department of Intensive Care Medicine, University Medical Center Utrecht, University Utrecht, Utrecht, The Netherlands.
  • Gommers D; Department of Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands.
  • Cremer OL; Intensive Care, UMC Utrecht, Utrecht, The Netherlands.
  • Bosman RJ; ICU, OLVG, Amsterdam, The Netherlands.
  • Rigter S; Department of Anesthesiology and Intensive Care, St. Antonius Hospital, Nieuwegein, The Netherlands.
  • Wils EJ; Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, The Netherlands.
  • Frenzel T; Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
  • de Jong R; Intensive Care, Bovenij Ziekenhuis, Amsterdam, The Netherlands.
  • Peters MAA; Intensive Care, Canisius Wilhelmina Ziekenhuis, Nijmegen, The Netherlands.
  • Kamps MJA; Intensive Care, Catharina Ziekenhuis Eindhoven, Eindhoven, The Netherlands.
  • Ramnarain D; Department of Intensive Care, ETZ Tilburg, Tilburg, The Netherlands.
  • Nowitzky R; Intensive Care, Haga Ziekenhuis, Den Haag, The Netherlands.
  • Nooteboom FGCA; Intensive Care, Laurentius Ziekenhuis, Roermond, The Netherlands.
  • de Ruijter W; Department of Intensive Care Medicine, Northwest Clinics, Alkmaar, The Netherlands.
  • Urlings-Strop LC; Intensive Care, Reinier de Graaf Gasthuis, Delft, The Netherlands.
  • Smit EGM; Intensive Care, Spaarne Gasthuis, Haarlem en Hoofddorp, The Netherlands.
  • Mehagnoul-Schipper DJ; Intensive Care, VieCuri Medisch Centrum, Venlo, The Netherlands.
  • Dormans T; Intensive care, Zuyderland MC, Heerlen, The Netherlands.
  • de Jager CPC; Department of Intensive Care, Jeroen Bosch Ziekenhuis, Den Bosch, The Netherlands.
  • Hendriks SHA; Intensive Care, Albert Schweitzerziekenhuis, Dordrecht, The Netherlands.
  • Achterberg S; ICU, Haaglanden Medisch Centrum, Den Haag, The Netherlands.
  • Oostdijk E; ICU, Maasstad Ziekenhuis Rotterdam, Rotterdam, The Netherlands.
  • Reidinga AC; ICU, SEH, BWC, Martiniziekenhuis, Groningen, The Netherlands.
  • Festen-Spanjer B; Intensive Care, Ziekenhuis Gelderse Vallei, Ede, The Netherlands.
  • Brunnekreef GB; Department of Intensive Care, Ziekenhuisgroep Twente, Almelo, The Netherlands.
  • Cornet AD; Department of Intensive Care, Medisch Spectrum Twente, Enschede, The Netherlands.
  • van den Tempel W; Department of Intensive Care, Ikazia Ziekenhuis Rotterdam, Rotterdam, The Netherlands.
  • Boelens AD; Anesthesiology, Antonius Ziekenhuis Sneek, Sneek, The Netherlands.
  • Koetsier P; Intensive Care, Medisch Centrum Leeuwarden, Leeuwarden, The Netherlands.
  • Lens J; ICU, IJsselland Ziekenhuis, Capelle aan den IJssel, The Netherlands.
  • Faber HJ; ICU, WZA, Assen, The Netherlands.
  • Karakus A; Department of Intensive Care, Diakonessenhuis Hospital, Utrecht, The Netherlands.
  • Entjes R; Department of Intensive Care, Adrz, Goes, The Netherlands.
  • de Jong P; Department of Anesthesia and Intensive Care, Slingeland Ziekenhuis, Doetinchem, The Netherlands.
  • Rettig TCD; Department of Anesthesiology, Intensive Care and Pain Medicine, Amphia Ziekenhuis, Breda, The Netherlands.
  • Reuland MC; Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
  • Arbous S; Intensivist, LUMC, Leiden, The Netherlands.
  • Fleuren LM; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Dam TA; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Thoral PJ; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • Lalisang RCA; Pacmed, Amsterdam, The Netherlands.
  • Tonutti M; Pacmed, Amsterdam, The Netherlands.
  • de Bruin DP; Pacmed, Amsterdam, The Netherlands.
  • Elbers PWG; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
  • de Keizer NF; 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.
Int J Med Inform ; 167: 104863, 2022 11.
Article in English | MEDLINE | ID: covidwho-2041812
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article