Do Risk Prediction Models for Postoperative Delirium Consider Patients’ Preoperative Medication Use?
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.
This systematic review aimed to assess the extent of inclusion of preoperative medications in RPMs for postoperative delirium.
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.
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.
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.
We thank the health librarians of the University of South Australia who assisted us in searching the literature.
GMK is supported by an Australian Government Research Training Program Scholarship. LMKE and TAN are both supported by National Health and Medical Research Council (NHMRC)–Australian Research Council (ARC) Dementia Research Development Fellowships (LMKE grant identification number APP1101788, TAN grant identification number APP1103860). EER is supported by an NHMRC Senior Principal Research Fellowship (grant identification number APP1110139). The contents of the published material are solely the responsibility of the individual authors and do not reflect the views of NHMRC.
Compliance with Ethical Standards
Conflict of interest
Gizat M. Kassie, Tuan A. Nguyen, Lisa M. Kalisch Ellett, Nicole L. Pratt and Elizabeth E. Roughead declare that they have no conflicts of interest relevant to the content of this review.
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