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.
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.
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.
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.
- Postoperative delirium is an under-recognised entity with detrimental impact on affected patients
- Artificial intelligence (AI) can utilise a patient’s electronic record to provide a low-effort screening mechanism
- Postoperative screening with an AI-based approach was helpful in identifying at-risk patients
- At the moment, both the traditional approaches to pre-operative risk stratification and AI-based were at best ‘marginal’ in diagnosing postoperative delirium.
- More comprehensive data availability will continue to improve AI-based solutions in the field of peri-operative care