Objective The timely initiation of renal replacement therapy (RRT) for acute kidney injury (AKI) requires sequential decision-making tailored to individuals’ evolving characteristics. To learn and validate optimal strategies for RRT initiation, we used reinforcement learning on clinical data from routine care and randomized controlled trials. Materials and methods We used the MIMIC-III database for development and AKIKI trials for validation. Participants were adult ICU patients with severe AKI receiving mechanical ventilation or catecholamine infusion. We used a doubly robust estimator to learn when to start RRT after the occurrence of severe AKI for three days in a row. We developed a “crude strategy” maximizing the population-level hospital-free days at day 60 (HFD60) and a “stringent strategy” recommending RRT when there is significant evidence of benefit for an individual. For validation, we evaluated the causal effects of implementing our learned strategies versus following current best practices on HFD60. Results We included 3748 patients in the development set and 1068 in the validation set. Through external validation, the crude and stringent strategies yielded an average difference of 13.7 [95% CI −5.3 to 35.7] and 14.9 [95% CI −3.2 to 39.2] HFD60, respectively, compared to current best practices. The stringent strategy led to initiating RRT within 3 days in 14% of patients versus 38% under best practices. Discussion Implementing our strategies could improve the average number of days that ICU patients spend alive and outside the hospital while sparing RRT for many. Conclusion We developed and validated a practical and interpretable dynamic decision support system for RRT initiation in the ICU.

中文翻译：

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在重症监护病房开始个性化肾脏替代治疗：基于强化学习的策略，并在 AKIKI 随机对照试验中进行外部验证

目的 及时启动针对急性肾损伤（AKI）的肾脏替代治疗（RRT）需要根据个体不断变化的特征做出连续决策。为了学习和验证 RRT 启动的最佳策略，我们对来自常规护理和随机对照试验的临床数据使用强化学习。材料和方法 我们使用 MIMIC-III 数据库进行开发，并使用 AKIKI 试验进行验证。参与者是接受机械通气或儿茶酚胺输注的严重 AKI 的成年 ICU 患者。我们使用双稳健估计器来了解连续三天发生严重 AKI 后何时开始 RRT。我们制定了一项“粗略策略”，最大限度地提高第 60 天时人口水平的非住院天数 (HFD60)，并制定了一项“严格策略”，当有明显证据表明对个人有益时，建议 RRT。为了进行验证，我们评估了实施我们学到的策略与遵循 HFD60 当前最佳实践的因果效应。结果 我们在开发组中纳入了 3748 名患者，在验证组中纳入了 1068 名患者。通过外部验证，与当前最佳实践相比，粗略策略和严格策略产生的平均差异分别为 13.7 [95% CI -5.3 至 35.7] 和 14.9 [95% CI -3.2 至 39.2] HFD60。严格的策略使得 14% 的患者在 3 天内开始 RRT，而最佳实践中这一比例为 38%。讨论 实施我们的策略可以改善 ICU 患者存活和出院的平均天数，同时为许多人节省 RRT。结论 我们开发并验证了一个实用且可解释的动态决策支持系统，用于 ICU 启动 RRT。