开发和验证用于筛查巴基斯坦妊娠糖尿病高危孕妇的非 INvaSive Pregnancy RIsk ScoRE (INSPIRE)

Sabahat Naz, Samreen Jamal, Ali Jaffar, Iqbal Azam, Subhash Chandir, Rahat Qureshi, Neelofur Babar, Aisha Syed Wali, Romaina Iqbal
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在巴基斯坦等中低收入国家,妊娠糖尿病(GDM)的发病率呈上升趋势。因此,需要开发一种简单、负担得起且易于管理的风险评分。我们的研究旨在根据文献报道的风险因素,为巴基斯坦孕妇的 GDM 筛查开发一种非 INvaSive Pregnancy RIsk ScoRE(INSPIRE)。我们采用横断面研究设计,在卡拉奇的一家三级医院和两家二级医院的产前检查诊所招募了 500 名妊娠 28 周至 32 周的孕妇。我们将数据随机分为衍生数据集(n=404;80%)和验证数据集(n=96;20%)。我们进行了访谈以收集社会人口因素和糖尿病家族史的信息,测量了中上臂围(MUAC),并查看了妇女的产科病史和口服葡萄糖耐量试验(OGTT)结果的医疗记录。我们对衍生数据集进行了多变量逻辑回归分析,以获得 GDM 的选定预测因子系数。校准采用皮尔逊 χ2 拟合优度检验进行估计,判别则采用验证数据集的曲线下面积(AUC)进行检查。INSPIRE 基于六个预测因素:产妇年龄、MUAC、糖尿病家族史、GDM 病史、既往不良产科结果和巨大儿病史。我们开发并验证的 INSPIRE 能有效区分 GDM 高风险和低风险的巴基斯坦孕妇,从而减轻 OGTT 试验的不必要负担。
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Development and validation of a Non-INvaSive Pregnancy RIsk ScoRE (INSPIRE) for the screening of high-risk pregnant women for gestational diabetes mellitus in Pakistan
The prevalence of gestational diabetes mellitus (GDM) is on the rise in low-income and middle-income countries, such as Pakistan. Therefore, the development of a risk score that is simple, affordable and easy to administer is needed. Our study aimed to develop a Non-INvaSive Pregnancy RIsk ScoRE (INSPIRE) for GDM screening in Pakistani pregnant women based on risk factors reported in the literature.Using a cross-sectional study design, we enrolled 500 pregnant women who attended antenatal clinics at one tertiary and two secondary care hospitals in Karachi between the 28th and 32nd weeks of gestation. We randomly divided data into derivation (n=404; 80%) and validation datasets (n=96; 20%). We conducted interviews to collect information on sociodemographic factors and family history of diabetes, measured mid-upper arm circumference (MUAC) and reviewed the medical records of women for obstetric history and oral glucose tolerance test (OGTT) results. We performed a multivariable logistic regression analysis to obtain coefficients of selected predictors for GDM in the derivation dataset. Calibration was estimated using Pearson’s χ2 goodness of fit test while discrimination was checked using the area under the curve (AUC) in the validation dataset.Overall, the GDM prevalence was 26% (n=130). INSPIRE was based on six predictors: maternal age, MUAC, family history of diabetes, a history of GDM, previous bad obstetrical outcome and a history of macrosomia. INSPIRE achieved a good calibration (Pearson’s χ2=29.55, p=0.08) and acceptable discrimination with an AUC of 0.721 (95% CI 0.61 to 0.83) with a sensitivity of 74.1% and specificity of 59.4% in the validation dataset.We developed and validated an INSPIRE that efficiently differentiates Pakistani pregnant women at high risk of GDM from those at low risk, thus reducing the unnecessary burden of the OGTT test.
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