The development of an automated ward independent delirium risk prediction model

Hugo A.J.M. de Wit*, Bjorn Winkens, Carlota Mestres Gonzalvo, Kim P.G.M. Hurkens, Wubbo J. Mulder, Rob Janknegt, Frans R. Verhey, Paul Hugo M. van der Kuy, Jos M.G.A. Schols

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

19 Citations (Scopus)

Abstract

Background A delirium is common in hospital settings resulting in increased mortality and costs. Prevention of a delirium is clearly preferred over treatment. A delirium risk prediction model can be helpful to identify patients at risk of a delirium, allowing the start of preventive treatment. Current risk prediction models rely on manual calculation of the individual patient risk. Objective The aim of this study was to develop an automated ward independent delirium riskprediction model. To show that such a model can be constructed exclusively from electronically available risk factors and thereby implemented into a clinical decision support system (CDSS) to optimally support the physician to initiate preventive treatment. Setting A Dutch teaching hospital. Methods A retrospective cohort study in which patients, 60 years or older, were selected when admitted to the hospital, with no delirium diagnosis when presenting, or during the first day of admission. We used logistic regression analysis to develop a delirium predictive model out of the electronically available predictive variables. Main outcome measure A delirium risk prediction model. Results A delirium risk prediction model was developed using predictive variables that were significant in the univariable regression analyses. The area under the receiver operating characteristics curve of the “medication model” model was 0.76 after internal validation. Conclusions CDSSs can be used to automatically predict the risk of a delirium in individual hospitalised patients’ by exclusively using electronically available predictive variables. To increase the use and improve the quality of predictive models, clinical risk factors should be documented ready for automated use.

Original languageEnglish
Pages (from-to)915-923
Number of pages9
JournalInternational Journal of Clinical Pharmacy
Volume38
Issue number4
DOIs
Publication statusPublished - Aug 2016
Externally publishedYes

Bibliographical note

Funding Information:
The SCREEN-study was supported by a grant from ZonMW, Grant Number 113101001.

Publisher Copyright:
© 2016, Springer International Publishing.

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