Park Jee Soo, Kim Deok Won, Kwon Ja-Young, Park Yong Won, Kim Young Han, Cho Hee Young
From the Department of Medicine (JSP); Department of Medical Engineering (DWK); Graduate Program in Biomedical Engineering (DWK); and Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea (YHK, JK, YWP, HYC).
Medicine (Baltimore). 2016 Jan;95(1):e2204. doi: 10.1097/MD.0000000000002204.
Gestational diabetes mellitus (GDM) is a common disease in pregnancy causing maternal and fetal complications. To prevent these adverse outcomes, optimal screening and diagnostic criteria must be adequate, timely, and efficient. This study suggests a novel approach that is practical, efficient, and patient- and clinician-friendly in predicting adverse outcomes of GDM. The authors conducted a retrospective cohort study via medical record review of patients admitted between March 2001 and April 2013 at the Severance Hospital, Seoul, South Korea. Patients diagnosed by a conventional 2-step method were evaluated according to the presence of adverse outcomes (neonatal hypoglycemia, hyperbilirubinemia, and hyperinsulinemia; admission to the neonatal intensive care unit; large for gestational age; gestational insulin therapy; and gestational hypertension). Of 802 women who had an abnormal 50-g, 1-hour glucose challenge test, 306 were diagnosed with GDM and 496 did not have GDM (false-positive group). In the GDM group, 218 women (71.2%) had adverse outcomes. In contrast, 240 women (48.4%) in the false-positive group had adverse outcomes. Women with adverse outcomes had a significantly higher body mass index (BMI) at entry (P = 0.03) and fasting blood glucose (FBG) (P = 0.03). Our logistic regression model derived from 2 variables, BMI at entry and FBG, predicted GDM adverse outcome with an area under the curve of 0.642, accuracy of 61.3%, sensitivity of 57.2%, and specificity of 66.9% compared with the conventional 2-step method with an area under the curve of 0.610, accuracy of 59.1%, sensitivity of 47.6%, and specificity of 74.4%. Our model performed better in predicting GDM adverse outcomes than the conventional 2-step method using only BMI at entry and FBG. Moreover, our model represents a practical, inexpensive, efficient, reproducible, easy, and patient- and clinician-friendly approach.
妊娠期糖尿病（GDM）是妊娠期常见疾病，可导致母婴并发症。为预防这些不良后果，最佳筛查和诊断标准必须充分、及时且有效。本研究提出了一种新方法，该方法在预测GDM不良后果方面切实可行、高效且对患者和临床医生都很友好。作者通过回顾性队列研究，对2001年3月至2013年4月在韩国首尔Severance医院住院的患者病历进行了审查。根据传统两步法诊断的患者，根据不良后果（新生儿低血糖、高胆红素血症和高胰岛素血症；入住新生儿重症监护病房；大于胎龄；妊娠期胰岛素治疗；以及妊娠期高血压）的存在情况进行评估。在802名50克1小时葡萄糖耐量试验异常的女性中，306名被诊断为GDM，496名未患GDM（假阳性组）。在GDM组中，218名女性（71.2%）出现了不良后果。相比之下，假阳性组中有240名女性（48.4%）出现了不良后果。出现不良后果的女性入院时体重指数（BMI）（P = 0.03）和空腹血糖（FBG）（P = 0.03）显著更高。我们从入院时的BMI和FBG这两个变量得出的逻辑回归模型预测GDM不良后果的曲线下面积为0.642，准确率为61.3%，灵敏度为57.2%，特异性为66.9%；相比之下，传统两步法的曲线下面积为0.610，准确率为59.1%，灵敏度为47.6%，特异性为74.4%。我们的模型在预测GDM不良后果方面比仅使用入院时BMI和FBG的传统两步法表现更好。此外，我们的模型代表了一种切实可行、成本低廉、高效、可重复、简便且对患者和临床医生都很友好的方法。
