利用机器压力波形对接受CRRT的AKI危重患者进行滚动日间死亡率预测

中文摘要 AI 辅助

接受连续性肾脏替代治疗（CRRT）的急性肾损伤（AKI）危重患者面临高死亡率，然而当前的风险评估主要依赖于电子健康记录（EHR）中的临床参数，忽略了CRRT机器产生的分钟级回路压力波形，这些波形追踪了体外回路与患者之间的相互作用。因此，临床医生无法在病情恶化发生时及时察觉。风险仅在抽取实验室检查时才被重新评估，而这一连续记录因受到共享设备记录、非生理分钟和传感器伪影的污染而被丢弃。为使该数据流可用，我们将机器记录与图表化的治疗区间对齐以防止跨患者泄漏，移除了预充和停机分钟，在合成尖峰注入基准上调整了去噪处理，并对未观察区间进行掩蔽而非插补。在此清洁数据流上，我们定义了一个滚动日间任务和一个基于Transformer的堆叠集成模型，该模型将窗口缩减序列Transformer与使用回路不稳定特征和临床EHR变量的经典模型进行晚期融合。在多中心CRRTnet队列（976名患者，4,585个治疗日）的防泄漏基准测试中，仅使用机器数据的模型具有最低的独立预后价值（AUROC 0.625），其次是仅使用EHR的模型（0.717）。整合EHR和机器数据流后，一天死亡率AUROC达到0.766。SHAP归因分析显示，回路不稳定描述符将组合模型前15个特征中机器数据的占比从3个提升至7个（从20.0%提升至46.7%），突显了滤器压力、跨膜压（TMP）和访问端至回流端差值（ARD）的重要性。据我们所知，这是首个纳入CRRT机器数据的患者水平死亡率预测，将原本被丢弃的床旁数据流转化为连续的风险信号。

英文摘要

Critically ill patients with acute kidney injury (AKI) on continuous renal replacement therapy (CRRT) face high mortality, yet current risk assessment relies primarily on clinical parameters from electronic health records (EHR) and ignores minute-level circuit pressure waveforms generated by CRRT machines that track the extracorporeal circuit's interaction with the patient. Clinicians therefore cannot see deterioration as it develops. Risk is reassessed only when labs are drawn, while this continuous record is discarded because it is contaminated by shared-device records, non-physiological minutes, and sensor artifacts. To make the stream usable, we aligned machine records to charted therapy intervals to prevent cross-patient leakage, removed priming and downtime minutes, tuned denoising on a synthetic spike-injection benchmark, and masked unobserved intervals rather than imputing them. On this cleaned stream, we define a rolling day-wise task and a transformer-based stacked ensemble that late-fuses a window-reduced sequence transformer with classical models using circuit-instability features and clinical EHR variables. In a leak-safe benchmark on the multi-center CRRTnet cohort (976 patients, 4,585 treatment days), the machine-only model had the lowest standalone prognostic value (AUROC 0.625), followed by the EHR-only model (0.717). Integrating EHR and machine streams reached a one-day mortality AUROC of 0.766. SHAP attribution showed that circuit-instability descriptors raised the machine share of the top 15 combined-model features from 3 to 7 (20.0% to 46.7%), highlighting filter pressure, transmembrane pressure (TMP), and access-to-return difference (ARD). To our knowledge, this is the first patient-level mortality prediction incorporating CRRT machine data, turning a discarded bedside stream into a continuous risk signal.

发表机构

• University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
• University of Cincinnati（辛辛那提大学）
• Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
• UT Southwestern Medical Center（德克萨斯大学西南医学中心）

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