School of Nursing, Beijing University of Chinese Medicine, Beijing, China.
Nursing Department, Beijing Hospital, Beijing, China.
Front Endocrinol (Lausanne). 2024 Nov 14;15:1366184. doi: 10.3389/fendo.2024.1366184. eCollection 2024.
Hypoglycemic episodes cause varying degrees of damage in the functional system of elderly inpatients with type 2 diabetes mellitus (T2DM). The purpose of the study is to construct a nomogram prediction model for the risk of hypoglycemia in elderly inpatients with T2DM and to evaluate the predictive performance of the model.
From August 2022 to April 2023, 546 elderly inpatients with T2DM were recruited in seven tertiary-level general hospitals in Beijing and Inner Mongolia province, China. Medical history and clinical data of the inpatients were collected with a self-designed questionnaire, with follow up on the occurrence of hypoglycemia within one week. Factors related to the occurrence of hypoglycemia were screened using regularized logistic analysis(r-LR), and a nomogram prediction visual model of hypoglycemia was constructed. AUROC, Hosmer-Lemeshow, and DCA were used to analyze the prediction performance of the model.
The incidence of hypoglycemia of elderly inpatients with T2DM was 41.21% (225/546). The risk prediction model included 8 predictors as follows(named ADOCHBIU): duration of diabetes (=2.276, 95% 2.097˜2.469), urinary microalbumin(=0.864, 95% 0.798˜0.935), oral hypoglycemic agents (=1.345, 95% 1.243˜1.452), cognitive impairment (=1.226, 95% 1.178˜1.276), insulin usage (=1.002, 95% 0.948˜1.060), hypertension (=1.113, 95% 1.103˜1.124), blood glucose monitoring (=1.909, 95% 1.791˜2.036), and abdominal circumference (=2.998, 95% 2.972˜3.024). The AUROC of the prediction model was 0.871, with sensitivity of 0.889 and specificity of 0.737, which indicated that the nomogram model has good discrimination. The Hosmer-Lemeshow was = 2.147 (=0.75), which meant that the prediction model is well calibrated. DCA curve is consistently higher than all the positive line and all the negative line, which indicated that the nomogram prediction model has good clinical utility.
The nomogram hypoglycemia prediction model constructed in this study had good prediction effect. It is used for early detection of high-risk individuals with hypoglycemia in elderly inpatients with T2DM, so as to take targeted measures to prevent hypoglycemia.
ChiCTR2200062277. Registered on 31 July 2022.
低血糖发作会对 2 型糖尿病老年住院患者的功能系统造成不同程度的损害。本研究旨在构建 2 型糖尿病老年住院患者低血糖风险的列线图预测模型，并评估该模型的预测性能。
2022 年 8 月至 2023 年 4 月，在北京和内蒙古自治区的 7 家三级综合医院招募了 546 例 2 型糖尿病老年住院患者。使用自行设计的问卷收集患者的病史和临床数据，并对其在一周内发生低血糖的情况进行随访。采用正则化逻辑回归（r-LR）筛选与低血糖发生相关的因素，并构建低血糖列线图预测模型。使用 AUROC、Hosmer-Lemeshow 和 DCA 分析模型的预测性能。
2 型糖尿病老年住院患者的低血糖发生率为 41.21%（225/546）。风险预测模型包括 8 个预测因子（命名为 ADOCHBIU）：糖尿病病程（=2.276，95%置信区间[CI]：2.0972.469）、尿微量白蛋白（=0.864，95%CI：0.7980.935）、口服降糖药（=1.345，95%CI：1.2431.452）、认知障碍（=1.226，95%CI：1.1781.276）、胰岛素使用（=1.002，95%CI：0.9481.060）、高血压（=1.113，95%CI：1.1031.124）、血糖监测（=1.909，95%CI：1.7912.036）和腹围（=2.998，95%CI：2.9723.024）。该预测模型的 AUROC 为 0.871，灵敏度为 0.889，特异性为 0.737，表明列线图模型具有良好的区分度。Hosmer-Lemeshow 检验=2.147（=0.75），表明预测模型具有较好的校准度。DCA 曲线始终高于所有阳性线和所有阴性线，表明列线图预测模型具有良好的临床实用性。
本研究构建的列线图低血糖预测模型具有较好的预测效果。用于早期检测 2 型糖尿病老年住院患者的低血糖高危个体，以便采取针对性措施预防低血糖。
ChiCTR2200062277。于 2022 年 7 月 31 日注册。
