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Published on: January 27, 2010
Do Risk Prediction Models for Postoperative Delirium Consider Patients' Preoperative Medication Use?
Gizat M Kassie1, Tuan A Nguyen2,3, Lisa M Kalisch Ellett2
1Quality Use of Medicines and Pharmacy Research Centre, School of Pharmacy and Medical Sciences, Sansom Institute for Health Research, University of South Australia, GPO Box 2471, Adelaide, SA, 5001, Australia. gizat_molla.kassie@mymail.unisa.edu.au.
Background:
Medicines are potentially modifiable risk factors for postoperative delirium. However, the extent to which preoperative medicines are included in risk prediction models (RPMs) is unknown.
Objective:
This systematic review aimed to assess the extent of inclusion of preoperative medications in RPMs for postoperative delirium.
Methods:
Articles were systematically searched from MEDLINE, EMBASE and CINAHL using Medical Subject Headings (MeSH) where possible and keywords for postoperative delirium and prediction model. Studies published until May 2017 with a primary outcome of postoperative delirium that developed an RPM containing preoperative patient information were considered. Where a study had two cohorts, a derivation and a validation cohort, findings from the derivation cohort were extracted and reported.
Results:
Eighteen prospective and one retrospective cohort studies were included for review. Of the 19 studies, only nine considered preoperative medication data, with medications appearing as predictor variables in five models. There was wide variability in the factors included in the final models, with the most frequent predictors being age and cognitive impairment, appearing in 13 (68%) and 11 (58%) RPMs, respectively.
Conclusion:
While medications are commonly cited risk factors for delirium, they are not adequately considered when developing RPMs. Future studies aiming to develop an RPM for postoperative delirium should include preoperative medication data as a potential predictor variable because of the modifiable nature of medication use and its impact on other factors commonly in models, such as cognition.
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