Department of Epidemiology, Brown School of Public Health, Providence, Rhode Island.
Department of Health Services, Policy and Practice; Brown School of Public Health, Providence, Rhode Island.
Am J Perinatol. 2024 May;41(S 01):e282-e289. doi: 10.1055/a-1877-9587. Epub 2022 Jun 16.
This article aimed to develop a predictive model to identify persons with recent gestational diabetes mellitus (GDM) most likely to progress to impaired glucose tolerance postpartum.
We conducted an observational study among persons with GDM in their most recent pregnancy, defined by Carpenter-Coustan criteria. Participants were followed up from delivery through 1-year postpartum. We used lasso regression with k-fold cross validation to develop a multivariable model to predict progression to impaired glucose tolerance, defined as HbA1c≥5.7%, at 1-year postpartum. Predictive ability was assessed by the area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV).
Of 203 participants, 71 (35%) had impaired glucose tolerance at 1-year postpartum. The final model had an AUC of 0.79 (95% confidence interval [CI]: 0.72, 0.85) and included eight indicators of weight, body mass index, family history of type 2 diabetes, GDM in a prior pregnancy, GDM diagnosis<24 weeks' gestation, and fasting and 2-hour plasma glucose at 2 days postpartum. A cutoff point of ≥ 0.25 predicted probability had sensitivity of 80% (95% CI: 69, 89), specificity of 58% (95% CI: 49, 67), PPV of 51% (95% CI: 41, 61), and NPV of 85% (95% CI: 76, 91) to identify women with impaired glucose tolerance at 1-year postpartum.
Our predictive model had reasonable ability to predict impaired glucose tolerance around delivery for persons with recent GDM.
· We developed a predictive model to identify persons with GDM most likely to develop IGT postpartum.. · The final model had an AUC of 0.79 (95% CI: 0.72, 0.85) and included eight clinical indicators.. · If validated, our model could help prioritize diabetes prevention efforts among persons with GDM..
本研究旨在建立预测模型，以识别近期患有妊娠糖尿病（GDM）的患者中最有可能在产后进展为糖耐量受损的患者。
我们对最近一次妊娠符合 Carpenter-Coustan 标准的 GDM 患者进行了一项观察性研究。从分娩到产后 1 年对参与者进行随访。我们使用带 k 折交叉验证的套索回归来建立一个多变量模型，以预测产后 1 年时进展为糖耐量受损的情况，定义为 HbA1c≥5.7%。通过曲线下面积（AUC）、敏感性、特异性和阳性及阴性预测值（PPV 和 NPV）来评估预测能力。
在 203 名参与者中，71 名（35%）在产后 1 年时患有糖耐量受损。最终模型的 AUC 为 0.79（95%置信区间：0.72，0.85），包括体重、体重指数、2 型糖尿病家族史、既往妊娠 GDM、GDM 诊断<24 周和产后 2 天的空腹和 2 小时血浆葡萄糖等 8 个指标。截断值≥0.25 预测概率的敏感性为 80%（95%置信区间：69，89），特异性为 58%（95%置信区间：49，67），阳性预测值为 51%（95%置信区间：41，61），阴性预测值为 85%（95%置信区间：76，91），以识别产后 1 年时患有糖耐量受损的女性。
我们的预测模型对近期患有 GDM 的患者在分娩时预测糖耐量受损有较好的能力。
·我们开发了一种预测模型，以识别最有可能在产后发生 IGT 的 GDM 患者。·最终模型的 AUC 为 0.79（95%置信区间：0.72，0.85），包含 8 个临床指标。·如果得到验证，我们的模型可以帮助优先对 GDM 患者进行糖尿病预防。
