Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians’ policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. −0.051 95% CI −0.077 to −0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. −0.436 95% CI −0.474 to −0.402) cohorts. Our finding indicates that AID might support clinicians’ decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.

谵妄会导致不良结果，包括重症监护病房 （ICU） 收治患者的住院时间和死亡率增加。右美托咪定已用于预防这些患者的谵妄;然而，最佳剂量具有挑战性。提出了一种基于强化学习的谵妄预防人工智能模型 （AID） 以优化右美托咪定剂量。该模型是使用 2416 名患者 （2531 名 ICU 入院） 开发和内部验证的，并在 270 名患者 （274 名 ICU 入院） 上进行了外部验证。在推导 （0.390， 95% 置信区间 [CI] 0.361 至 0.420 vs. -0.051;95% CI -0.077 至 -0.025） 和外部验证 （0.186， 95% CI 0.139 至 0.236 vs. -0.436 95% CI -0.474 至 -0.402） 队列中，AID 政策的估计性能回报高于临床医生政策。我们的研究结果表明，AID 可能支持临床医生关于右美托咪定剂量的决策，以防止 ICU 患者出现谵妄，但需要进一步的非政策评估。