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

• Huvudupphovsmän: Romaina Iqbal (Författare, medförfattare), Iqbal Azam (Författare, medförfattare), Subhash Chandir (Författare, medförfattare), Rahat Qureshi (Författare, medförfattare), Ali Jaffar (Författare, medförfattare), Neelofur Babar (Författare, medförfattare), Sabahat Naz (Författare, medförfattare), Samreen Jamal (Författare, medförfattare), Aisha Syed Wali (Författare, medförfattare)
• Materialtyp: Bok
• Publicerad: BMJ Publishing Group, 2024-03-01T00:00:00Z.
• Ämnen: article
• Länkar: Connect to this object online.
• Notes: 10.1136/bmjph-2024-000920; 2753-4294
• Keywords: Public aspects of medicine; RA1-1270
• Source: BMJ Public Health, Vol 2, Iss 1 (2024)

Introduction 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.Methods 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.Results 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.Conclusion 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.