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基于机器学习的决策规则估计以减少 1 型糖尿病老年患者的低血糖:WISDM 研究中的连续血糖监测的事后分析。

Estimation of a Machine Learning-Based Decision Rule to Reduce Hypoglycemia Among Older Adults With Type 1 Diabetes: A Post Hoc Analysis of Continuous Glucose Monitoring in the WISDM Study.

机构信息

Department of Nutrition, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

UNC Center for Aging and Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

出版信息

J Diabetes Sci Technol. 2024 Sep;18(5):1079-1086. doi: 10.1177/19322968221149040. Epub 2023 Jan 11.

Abstract

BACKGROUND

The Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study demonstrated continuous glucose monitoring (CGM) reduced hypoglycemia over 6 months among older adults with type 1 diabetes (T1D) compared with blood glucose monitoring (BGM). We explored heterogeneous treatment effects of CGM on hypoglycemia by formulating a data-driven decision rule that selects an intervention (ie, CGM vs BGM) to minimize percentage of time <70 mg/dL for each individual WISDM participant.

METHOD

The precision medicine analyses used data from participants with complete data (n = 194 older adults, including those who received CGM [n = 100] and BGM [n = 94] in the trial). Policy tree and decision list algorithms were fit with 14 baseline demographic, clinical, and laboratory measures. The primary outcome was CGM-measured percentage of time spent in hypoglycemic range (<70 mg/dL), and the decision rule assigned participants to a subgroup reflecting the treatment estimated to minimize this outcome across all follow-up visits.

RESULTS

The optimal decision rule was found to be a decision list with 3 steps. The first step moved WISDM participants with baseline time-below range >1.35% and no detectable C-peptide levels to the CGM subgroup (n = 139), and the second step moved WISDM participants with a baseline time-below range of >6.45% to the CGM subgroup (n = 18). The remaining participants (n = 37) were left in the BGM subgroup. Compared with the BGM subgroup (n = 37; 19%), the group for whom CGM minimized hypoglycemia (n = 157; 81%) had more baseline hypoglycemia, a lower proportion of detectable C-peptide, higher glycemic variability, longer disease duration, and higher proportion of insulin pump use.

CONCLUSIONS

The decision rule underscores the benefits of CGM for older adults to reduce hypoglycemia. Diagnostic CGM and laboratory markers may inform decision-making surrounding therapeutic CGM and identify older adults for whom CGM may be a critical intervention to reduce hypoglycemia.

摘要

背景

与血糖监测(BGM)相比,Wireless Innovation for Seniors with Diabetes Mellitus(WISDM)研究表明,连续血糖监测(CGM)可在 6 个月内减少老年 1 型糖尿病(T1D)患者的低血糖。我们通过制定数据驱动的决策规则来探索 CGM 对低血糖的异质治疗效果,该规则选择干预措施(即 CGM 与 BGM),以最小化每位 WISDM 参与者的 <70mg/dL 时间百分比。

方法

使用来自具有完整数据的参与者(n = 194 名老年人,包括在试验中接受 CGM [n = 100]和 BGM [n = 94]的参与者)的精准医学分析。使用 14 项基线人口统计学、临床和实验室指标拟合了决策树和决策列表算法。主要结局是 CGM 测量的低血糖范围(<70mg/dL)时间百分比,决策规则将参与者分配到一个亚组,反映出在所有随访访问中估计最小化该结局的治疗方法。

结果

发现最佳决策规则是一个具有 3 个步骤的决策列表。第一步将基线时间<范围>1.35%和无可检测 C 肽水平的 WISDM 参与者转移到 CGM 亚组(n = 139),第二步将基线时间<范围>6.45%的 WISDM 参与者转移到 CGM 亚组(n = 18)。其余参与者(n = 37)留在 BGM 亚组。与 BGM 亚组(n = 37;19%)相比,CGM 最小化低血糖的组(n = 157;81%)的基线低血糖更多,可检测 C 肽的比例较低,血糖变异性更高,疾病持续时间更长,胰岛素泵使用率更高。

结论

该决策规则强调了 CGM 对减少老年患者低血糖的益处。诊断性 CGM 和实验室标志物可能有助于围绕治疗性 CGM 的决策,并确定 CGM 可能是减少低血糖的关键干预措施的老年患者。

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