Abstract
BACKGROUND: Postoperative delirium is a highly relevant complication of cardiac surgery. It is associated with worse outcomes and considerably increased costs of care. A novel approach of monitoring patients with machine learning enabled prediction software could trigger pre-emptive implementation of mitigation strategies as well as timely intervention. OBJECTIVE: This study evaluates the predictive accuracy of an artificial intelligence (AI) model for anticipating postoperative delirium by comparing it to established standards and measures of risk and vulnerability. DESIGN: Retrospective predictive accuracy study. SETTING: Records were gathered from a database for anaesthesia quality assurance at a specialised heart surgery centre in Germany. PATIENTS: Between January and July 2021, 131 patients had been enrolled into the database and had data available for AI prediction modelling. After exclusion of incomplete follow-ups, a subset of 114 was included in the statistical analysis. MAIN OUTCOME MEASURES: Delirium was diagnosed with the Confusion Assessment Method for the ICU (CAM-ICU) over three days postoperatively with specific follow-up visits. AI predictions were also compared with risk assessment through a frailty screening, a Shulman Clock Drawing Test, and using a checklist of predisposing factors including comorbidity, reduced mobility, and substance abuse. RESULTS: Postoperative delirium was diagnosed in 23.7% of patients. Postoperative AI screening exhibited reasonable performance with an area under the receiver operating curve (AUROC) of 0.79, 95% confidence interval (CI), 0.69-0.87. But pre-operative prediction was weak for all methods (AUROC range from 0.55 to 0.66). There were significant associations with postoperative delirium: open heart surgery versus endovascular valve replacement (33.3% vs. 10.4%, P < 0.01), postinterventional hospitalisation (12.8 vs. 8.6 days, P < 0.01), and length of ICU stay (1.7 vs. 0.3 days, P < 0.01) were all significantly associated with postoperative delirium. CONCLUSION: AI is a promising approach with considerable potential and delivered noninferior results compared with the usual approach of structured evaluation of risk factors and questionnaires. Since these established methods do not provide the desired confidence level, improved AI may soon deliver a better performance. TRIAL REGISTRATION: None.