Diabetes research and clinical practice · 2018
Gestational diabetes mellitus risk score: A practical tool to predict gestational diabetes mellitus risk in Tanzania.
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Nombo Anna Patrick, Mwanri Akwilina Wendelin, Brouwer-Brolsma Elske M, Ramaiya Kaushik L, Feskens Edith J M
Аннотация
BACKGROUND: Universal screening for hyperglycemia during pregnancy may be in-practical in resource constrained countries. Therefore, the aim of this study was to develop a simple, non-invasive practical tool to predict undiagnosed Gestational diabetes mellitus (GDM) in Tanzania.
METHODS: We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives).
RESULTS: Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals.
CONCLUSION: The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.
METHODS: We used cross-sectional data of 609 pregnant women, without known diabetes, collected in six health facilities from Dar es Salaam city (urban). Women underwent screening for GDM during ante-natal clinics visit. Smoking habit, alcohol consumption, pre-existing hypertension, birth weight of the previous child, high parity, gravida, previous caesarean section, age, MUAC ≥ 28 cm, previous stillbirth, haemoglobin level, gestational age (weeks), family history of type 2 diabetes, intake of sweetened drinks (soda), physical activity, vegetables and fruits consumption were considered as important predictors for GDM. Multivariate logistic regression modelling was used to create the prediction model, using a cut-off value of 2.5 to minimise the number of undiagnosed GDM (false negatives).
RESULTS: Mid-upper arm circumference (MUAC) ≥ 28 cm, previous stillbirth, and family history of type 2 diabetes were identified as significant risk factors of GDM with a sensitivity, specificity, positive predictive value, and negative predictive value of 69%, 53%, 12% and 95%, respectively. Moreover, the inclusion of these three predictors resulted in an area under the curve (AUC) of 0.64 (0.56-0.72), indicating that the current tool correctly classifies 64% of high risk individuals.
CONCLUSION: The findings of this study indicate that MUAC, previous stillbirth, and family history of type 2 diabetes significantly predict GDM development in this Tanzanian population. However, the developed non-invasive practical tool to predict undiagnosed GDM only identified 6 out of 10 individuals at risk of developing GDM. Thus, further development of the tool is warranted, for instance by testing the impact of other known risk factors such as maternal age, pre-pregnancy BMI, hypertension during or before pregnancy and pregnancy weight gain.