Prediction models for postoperative delirium after cardiac surgery: Systematic review and critical appraisal

Background: Many studies have developed or validated prediction models to estimate the risk of delirium after cardiac surgery, but the quality of the model development and model applicability remain unknown.Objectives: To systematically review and critically evaluate currently available prediction models for delirium after cardiac surgery.Data sources: PubMed, EMBASE, and MEDLINE were systematically searched. This systematic review was regis-tered in PROSPERO (Registration ID: CRD42021251226).Study selection: Prospective or retrospective cohort studies were considered eligible if they developed or vali-dated prediction models or scoring systems for delirium in the ICU. We included studies involving adults (age >= 18 years) undergoing cardiac surgery and excluded studies that did not validate a prediction model.Data extraction: Data extraction was independently performed by two authors using a standardized data extrac-tion form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Stud-ies checklist. Quality of the models was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST).Data synthesis: Of 5469 screened studies, 13 studies described 10 prediction models. The postoperative delirium incidence varied from 11.3 % to 51.6 %. The most frequently used predictors were age and cognitive impairment. The reported areas under the curve or C-statistics were between of 0.74 and 0.91 in the derivation set. The re-ported AUCs in the external validation set were between 0.54 and 0.90. All the studies had a high risk of bias, mainly owing to poor reporting of the outcome domain and analysis domain; 10 studies were of high concern regarding applicability.Conclusions: The current models for predicting postoperative delirium in the ICU after cardiac surgery had a high risk of bias according to the PROBAST. Future studies should focus on improving current prediction models or de-veloping new models with rigorous methodology.(c) 2022 Published by Elsevier Ltd.