Division of General Pediatrics, Department of Pediatrics, Icahn School of Medicine At Mount Sinai, 1 Gustave L. Levy Place Box 1077, New York, NY, 10029, USA.
Department of Population Health Science and Policy, Icahn School of Medicine At Mount Sinai, New York, NY, USA.
Sci Rep. 2021 May 27;11(1):11212. doi: 10.1038/s41598-021-90406-0.
Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06-0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.
近年来，青少年中糖尿病前期和糖尿病（preDM/DM）的发病率呈惊人上升趋势。然而，目前仅有适用于成年人的简单问卷调查式筛查工具，无法用于青少年。作为开发此类工具的第一步，我们利用来自全国健康和营养调查（NHANES）的大型数据集，根据美国糖尿病协会的诊断生物标志物，检验一项已发表的儿科临床筛查指南在识别患有 preDM/DM 的青少年方面的性能。我们使用既定的评估指标（敏感性、特异性、阳性/阴性预测值、阳性/阴性 preDM/DM 类别的 F 度量、Kappa）来评估临床指南与生物标志物标准之间的一致性。我们还将指南的性能与基于机器学习（ML）的来自 NHANES 数据集的 preDM/DM 分类器进行了比较。在我们的研究人群中，约 29%的 2858 名青少年根据生物标志物标准患有 preDM/DM。临床指南的敏感性为 43.1%，特异性为 67.6%，阳性/阴性预测值为 35.2%/74.5%，阳性/阴性 F 度量为 38.8%/70.9%，Kappa 为 0.1（95%CI：0.06-0.14）。该指南在不同的人口统计学亚组中的表现存在差异。一些基于 ML 的分类器的性能与筛查指南相当或更好，尤其是在识别 preDM/DM 青少年方面（p=5.23×10）。我们表明，推荐的儿科临床筛查指南在识别青少年 preDM/DM 状态方面表现不佳。需要进一步开发简单而准确的青少年糖尿病风险筛查工具，可能需要使用先进的 ML 方法和更广泛的临床和行为健康数据。
