Adams Alyce S, Lee Catherine, Escobar Gabriel, Bayliss Elizabeth A, Callaghan Brian, Horberg Michael, Schmittdiel Julie A, Trinacty Connie, Gilliam Lisa K, Kim Eileen, Hejazi Nima S, Ma Lin, Neugebauer Romain
Departments of Health Policy and of Epidemiology and Population Health, School of Medicine, Stanford University, Encina Commons, 615 Crothers Way, Stanford, CA, 94305, United States, 1 6507247555.
Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, United States.
JMIR Diabetes. 2025 May 29;10:e60141. doi: 10.2196/60141.
Diabetes-related lower extremity complications, such as foot ulceration and amputation, are on the rise, currently affecting nearly 131 million people worldwide. Methods for early detection of individuals at high risk remain elusive. While data-driven diabetic polyneuropathy algorithms exist, high-performing, clinically useful tools to assess risk are needed to improve clinical care.
This study aimed to develop an electronic medical record-based machine learning algorithm that would predict lower extremity complications.
We conducted a retrospective longitudinal cohort study to predict the risk of lower extremity complications within 24 months of an initial diagnosis of diabetic polyneuropathy. From an initial cohort of 468,162 individuals with at least 1 diagnosis of diabetic polyneuropathy at one of 2 multispecialty health care systems (based in northern California and Colorado) between April 2012 and December 2016, we created an analytic cohort of 48,209 adults with continuous enrollment, who were newly diagnosed with no evidence of end-of-life care. The outcome was any lower extremity complication, including foot ulceration, osteomyelitis, gangrene, or lower extremity amputation. We randomly split the data into training (38,569/48209; 80%) and testing (9,640/48209; 20%) datasets. In the training dataset, we used super Learner (SL), an ensemble learning method that employs cross-validation and combines multiple candidate risk predictors, into a single risk predictor. We evaluated the performance of the SL risk predictor in the testing dataset using the receiver operating characteristic curve and a calibration plot.
Of the 48,209 individuals in the cohort, 2327 developed a lower extremity complication during follow-up. The SL risk estimator exhibited good discrimination (AUC=0.845, 95% CI 0.826-0.863) and calibration. A modified version of our SL algorithm, simplified to facilitate real-world adoption, had only slightly reduced discrimination (AUC=0.817, 95%CI 0.797-0.837). The modified version slightly outperformed the naïve logistic regression model (AUC=0.804, 95% CI 0.783-0.825) in terms of precision gained relative to the frequency of alerts and number of patients that needed to be evaluated.
We have built a machine learning-based risk estimator with the potential to improve clinical detection of diabetic patients at high risk for lower extremity complications at the time of an initial diabetic polyneuropathy diagnosis. The algorithm exhibited good discriminant validity and calibration using only data from the electronic medical record. Additional research will be needed to identify optimal contexts and strategies for maximizing algorithmic fairness in both interpretation and deployment.
糖尿病相关的下肢并发症，如足部溃疡和截肢，正在增加，目前全球近1.31亿人受其影响。早期发现高危个体的方法仍然难以捉摸。虽然存在数据驱动的糖尿病性多发性神经病变算法，但仍需要高性能、临床实用的风险评估工具来改善临床护理。
本研究旨在开发一种基于电子病历的机器学习算法，以预测下肢并发症。
我们进行了一项回顾性纵向队列研究，以预测糖尿病性多发性神经病变初诊后24个月内发生下肢并发症的风险。在2012年4月至2016年12月期间，从两个多专科医疗系统（位于加利福尼亚州北部和科罗拉多州）中至少有1次糖尿病性多发性神经病变诊断的468162名个体的初始队列中，我们创建了一个48209名成年人的分析队列，这些人持续入组，新诊断且无临终关怀证据。结局为任何下肢并发症，包括足部溃疡、骨髓炎、坏疽或下肢截肢。我们将数据随机分为训练集（38569/48209；80%）和测试集（9640/48...
