Ma Sisi, Alvear Alison, Schreiner Pamela J, Seaquist Elizabeth R, Kirsh Thomas, Chow Lisa S
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
J Diabetes Sci Technol. 2025 Jan;19(1):105-113. doi: 10.1177/19322968231184497. Epub 2023 Jun 28.
The recent availability of high-quality data from clinical trials, together with machine learning (ML) techniques, presents exciting opportunities for developing prediction models for clinical outcomes.
As a proof-of-concept, we translated a hypoglycemia risk model derived from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study into the HypoHazardScore, a risk assessment tool applicable to electronic health record (EHR) data. To assess its performance, we conducted a 16-week clinical study at the University of Minnesota where participants (N = 40) with type 2 diabetes mellitus (T2DM) had hypoglycemia assessed prospectively by continuous glucose monitoring (CGM).
The HypoHazardScore combines 16 risk factors commonly found within the EHR. The HypoHazardScore successfully predicted (area under the curve [AUC] = 0.723) whether participants experienced least one CGM-assessed hypoglycemic event (glucose <54 mg/dL for ≥15 minutes from two CGMs) while significantly correlating with frequency of CGM-assessed hypoglycemic events (r = 0.38) and percent time experiencing CGM-assessed hypoglycemia (r = 0.39). Compared to participants with a low HypoHazardScore (N = 19, score <4, median score of 4), participants with a high HypoHazardScore (N = 21, score ≥4) had more frequent CGM-assessed hypoglycemic events (high: 1.6 ± 2.2 events/week; low: 0.3 ± 0.5 events/week) and experienced more CGM-assessed hypoglycemia (high: 1.4% ± 2.0%; low: 0.2% ± 0.4% time) during the 16-week follow-up.
We demonstrated that a hypoglycemia risk model can be successfully adapted from the ACCORD data to the EHR, with validation by a prospective study using CGM-assessed hypoglycemia. The HypoHazardScore represents a significant advancement toward implementing an EHR-based decision support system that can help reduce hypoglycemia in patients with T2DM.
近期来自临床试验的高质量数据，连同机器学习（ML）技术，为开发临床结局预测模型提供了令人兴奋的机会。
作为概念验证，我们将源自糖尿病心血管风险控制行动（ACCORD）研究的低血糖风险模型转化为适用于电子健康记录（EHR）数据的低血糖风险评分（HypoHazardScore），这是一种风险评估工具。为评估其性能，我们在明尼苏达大学进行了一项为期16周的临床研究，其中40名2型糖尿病（T2DM）参与者通过持续葡萄糖监测（CGM）对低血糖进行前瞻性评估。
低血糖风险评分（HypoHazardScore）结合了电子健康记录中常见的16个风险因素。HypoHazardScore成功预测（曲线下面积[AUC]=0.723）参与者是否至少经历一次经CGM评估的低血糖事件（两次CGM显示血糖<54mg/dL持续≥15分钟），同时与经CGM评估的低血糖事件频率（r=0.38）和经历经CGM评估的低血糖的时间百分比（r=0.39）显著相关。与低血糖风险评分低的参与者（N=19，评分<4，中位数评分为4）相比，低血糖风险评分高的参与者（N=21，评分≥4）在16周随访期间经CGM评估的低血糖事件更频繁（高：1.6±2.2次/周；低：0.3±0.5次/周），且经历经CGM评估的低血糖情况更多（高：1.4%±2.0%；低：0.2%±0.4%时间）。
我们证明了低血糖风险模型可以成功地从ACCORD数据改编到电子健康记录，并通过使用CGM评估低血糖的前瞻性研究进行验证。HypoHazardScore代表了朝着实施基于电子健康记录的决策支持系统迈出了重要一步，该系统有助于减少T2DM患者的低血糖情况。
