Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery

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.

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.

KEY POINTS