Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites.

翻译：背景：急性肾损伤（AKI）是肾脏排泄功能下降的疾病，在住院患者中发生率高达18%，病情进展可能导致不可逆的肾损伤。方法：本回顾性队列研究纳入匹兹堡大学医学中心（UPMC）非重症监护病房（n=46,815）和佛罗里达大学健康中心（UFH）（n=127,202）的成年住院患者。我们开发并比较了深度学习与传统机器学习模型，以预测患者在未来48小时内进展至2级及以上AKI的风险。针对各医疗机构（UFH模型基于UFH数据训练，UPMC模型基于UPMC数据训练）分别训练本地模型，并构建包含两个机构患者联合开发队列的独立模型（UFH-UPMC模型）。对各模型进行内部及外部验证，并按性别和种族进行亚组分析。结果：UFH和UPMC患者中2级及以上AKI的发生率分别为3%（n=3,257）和8%（n=2,296）。UFH测试队列的受试者工作特征曲线下面积（AUROC）介于0.77（UPMC模型）至0.81（UFH模型）之间，UPMC测试队列的AUROC介于0.79（UFH模型）至0.83（UPMC模型）之间。UFH-UPMC模型在UFH和UPMC测试队列的AUROC分别为0.81（95%置信区间[CI][0.80,0.83]）和0.82（95% CI[0.81,0.84]）；精确率-召回率曲线下面积（AUPRC）分别为0.6（95% CI[0.05,0.06]）和0.13（95% CI[0.11,0.15]）。动力性估算肾小球滤过率、肾毒性药物负担和血尿素氮始终是各模型与医疗中心影响最大的前三项特征。结论：本地开发模型在另一机构测试时表现出轻微的判别能力下降，但各模型与机构间最显著的影响特征集保持高度一致。