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<p>Renal replacement therapy (RRT) is a critical intervention for patients with acute kidney injury (AKI). However, clinical decision-making regarding the timing of initiation, modality selection, optimal ultrafiltration rate, and weaning criteria remains highly complex and exhibits significant practice variation. Current RCTs fail to guide adaptive strategies, leaving dynamic decision-making heavily reliant on empirical experience. Consequently, a substantial gap persists in dynamically adjusting treatment in response to patient-specific clinical progression. We conducted a retrospective multi-center study to develop <b>H</b>ierarchical <b>R</b>einforcement Learning for <b>R</b>enal <b>R</b>eplacement <b>T</b>herapy (HRRT), a holistic clinical decision support system (CDSS) that covers the full decision-making process in RRT. 2467 Intensive Care Unit (ICU) stays of 1439 patients from a US hospital were used for training and internal testing. The model’s performance was evaluated on two external validation sets, where we selected 1085 ICU stays of 1085 patients from Netherlands and 1230 stays of 845 patients from China. The estimated mortality rate decreased by 6.1 percentage points from 47.7% (95% CI: 45.2–50.0) to 41.6% (95% CI: 35.6–47.2) compared to clinician-led outcomes. These findings imply that an AI-driven, holistic approach to RRT can reduce inappropriate practice variation and lower ICU mortality by recommending personalized treatment adjustments.</p>

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HRRT: hierarchical reinforcement learning for renal replacement therapy decision support

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月21日

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  • Qianyi Xu,
  • Feng Wu,
  • Zi Yi Christopher Thong,
  • Mark Sen Liang Goh,
  • Pengpeng Chen,
  • Jie Yang,
  • Chen Huang,
  • Zhongheng Zhang,
  • Yucai Hong,
  • Kay Choong See,
  • Mengling Feng

摘要

Renal replacement therapy (RRT) is a critical intervention for patients with acute kidney injury (AKI). However, clinical decision-making regarding the timing of initiation, modality selection, optimal ultrafiltration rate, and weaning criteria remains highly complex and exhibits significant practice variation. Current RCTs fail to guide adaptive strategies, leaving dynamic decision-making heavily reliant on empirical experience. Consequently, a substantial gap persists in dynamically adjusting treatment in response to patient-specific clinical progression. We conducted a retrospective multi-center study to develop Hierarchical Reinforcement Learning for Renal Replacement Therapy (HRRT), a holistic clinical decision support system (CDSS) that covers the full decision-making process in RRT. 2467 Intensive Care Unit (ICU) stays of 1439 patients from a US hospital were used for training and internal testing. The model’s performance was evaluated on two external validation sets, where we selected 1085 ICU stays of 1085 patients from Netherlands and 1230 stays of 845 patients from China. The estimated mortality rate decreased by 6.1 percentage points from 47.7% (95% CI: 45.2–50.0) to 41.6% (95% CI: 35.6–47.2) compared to clinician-led outcomes. These findings imply that an AI-driven, holistic approach to RRT can reduce inappropriate practice variation and lower ICU mortality by recommending personalized treatment adjustments.