D patients who are at high risk for developing kidney failure over short time frames (6-12 months) may help reduce the rates of unplanned dialysis and improve the quality of transitions from CKD to kidney failure. Methods: We performed a retrospective study employing machine learning random forest algorithms incorporating routinely collected age and sex data along with time-varying trends in laboratory measurements to derive and validate six- and 12-month kidney failure risk prediction models in the advanced CKD population. The models were comprehensively characterized in three independent cohorts in Ontario, Canada – derived in a cohort of 1,849 consecutive advanced CKD patients (mean [standard deviation] age 66 [15] years, eGFR 19 [7] mL/min/1.73m2), and validated in two external advanced CKD cohorts (n=1,356; age 69 [14] years, eGFR 22 [7] mL/min/1.73m2). Results: Across all cohorts, 55% of patients experienced kidney failure, of which 35% involved unplanned dialysis. The six- and 12-month models demonstrated excellent discrimination with area under the receiver operating characteristic curve of 0.88 (95%CI: 0.87-0.89) and 0.87 (95%CI: 0.86-0.87) along with high probabilistic accuracy with Brier scores of 0.10 (95%CI 0.09-0.10) and 0.14 (95%CI 0.13-0.14), respectively. The models were also well-calibrated and delivered timely alerts on a significant number of patients who ultimately initiated dialysis in an unplanned fashion. Similar results were found upon external validation testing. Conclusion: These machine-learning models using routinely collected patient data accurately predict near-future kidney failure risk among the advanced CKD population, and retrospectively deliver advanced warning on a substantial proportion of unplanned dialysis events. Optimal implementation strategies still need to be elucidated. Copyright © 2024 by the American Society of Nephrology...

中文翻译：

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用于预防意外透析的机器学习模型的推导和验证


在短时间内（6-12 个月）处于肾衰竭高风险的 D 患者可能有助于降低计划外透析的发生率并提高从 CKD 到肾衰竭的过渡质量。方法：我们进行了一项回顾性研究，采用机器学习随机森林算法，结合常规收集的年龄和性别数据以及实验室测量值的时变趋势，得出并验证晚期 CKD 人群的 6 个月和 12 个月肾衰竭风险预测模型。这些模型在加拿大安大略省的三个独立队列中进行了全面表征——源自 1,849 名连续晚期 CKD 患者的队列（平均[标准差]年龄 66 [15] 岁，eGFR 19 [7] mL/min/1.73m2），并在两个外部晚期 CKD 队列中得到验证（n=1,356；年龄 69 [14] 岁，eGFR 22 [7] mL/min/1.73m2）。结果：在所有队列中，55% 的患者出现肾衰竭，其中 35% 涉及计划外透析。 6 个月和 12 个月模型表现出出色的辨别力，受试者工作特征曲线下面积分别为 0.88（95%CI：0.87-0.89）和 0.87（95%CI：0.86-0.87），并且具有较高的概率准确性，Brier 分数为分别为 0.10 (95% CI 0.09-0.10) 和 0.14 (95% CI 0.13-0.14)。这些模型也经过了良好的校准，并对大量最终以计划外方式开始透析的患者发出及时警报。外部验证测试也发现了类似的结果。结论：这些机器学习模型使用常规收集的患者数据准确预测晚期 CKD 人群近期肾衰竭风险，并回顾性地对相当大比例的计划外透析事件发出预警。 最佳实施策略仍需阐明。版权所有 © 2024 美国肾脏病学会...