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

Abstract

BackgroundMany 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. ObjectivesTo systematically review and critically evaluate currently available prediction models for delirium after cardiac surgery. Data sourcesPubMed, EMBASE, and MEDLINE were systematically searched. This systematic review was registered in PROSPERO (Registration ID: CRD42021251226). Study selectionProspective or retrospective cohort studies were considered eligible if they developed or validated 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 extractionData extraction was independently performed by two authors using a standardized data extraction form based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist. Quality of the models was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Data synthesisOf 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 reported 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. ConclusionsThe 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 developing new models with rigorous methodology.