Risk of Gestational Diabetes Mellitus (GDM) Using Clinical Prediction Model Based on Maternal Characteristics

Abstract

Introduction: The healthcare delivery challenges in Bangladesh are phenomenal. Improving maternal and child health, reducing the high maternal and infant mortality & morbidity are challenging. Arrangement of additional expenditure for GDM screening is again challenging. The efficiency of screening could be enhanced by considering womens risks of gestational diabetes on the basis of their clinical characteristics.

Objectives: To find out the use of the clinical prediction model of gestational diabetes mellitus (GDM) is valid for Bangladeshi pregnant women and to assess the risk of gestational diabetes by using clinical prediction model based on maternal characteristics.

Materials and Methods: A cross sectional study was carried out from July 2011 to June 2012 among purposively selected 217 pregnant women of ?24 weeks of gestation in the Gynae and Obstetric outpatient department of Combined Military Hospital, Dhaka. Data were collected by face to face interview, anthropometric measurement and record review. Two step oral glucose tests were done for diagnosis of GDM.

Results: According to Chadakaran clinical prediction model 84 (38.7%) respondents were at high risk, 92 (42.4%) were at intermediate risk and 41(18.9%) found at low risk of gestational diabetes but only 24(11.05%) developed gestational diabetes. Highest occurrence of gestational diabetes was found in high risk group 17 (20.2%) with zero occurrence in low risk group. Risk score performance at the level of ?380, sensitivity was 100% and specificity 21.8%, 13.6% positive predictive value, 100% negative predictive value and area under curve was 0.385. At the level of 460 score the sensitivity and specificity was found closest (70.8% and 65.3%, respectively) and area under curve was highest 0.657. The receiver operating characteristics curve of the risk score in the study sample for predicting women with glucose tolerance test demonstrated an area 0.763 (95%, 0.682 0.845).

Conclusion: The use of clinical prediction model is a simple, non invasive, cost effective useful method to identify women at increased risk of gestational diabetes mellitus and could be short listed for further testing.

Journal of Armed Forces Medical College Bangladesh Vol.11(1) 2015: 64-68