Hassen, H.Y.; Gebreyesus, S.H.; Endris, B.S.; Roro, M.A.; Van Geertruyden, J.-P. Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia. J. Clin. Med.2020, 9, 1587.
Hassen, H.Y.; Gebreyesus, S.H.; Endris, B.S.; Roro, M.A.; Van Geertruyden, J.-P. Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia. J. Clin. Med. 2020, 9, 1587.
Hassen, H.Y.; Gebreyesus, S.H.; Endris, B.S.; Roro, M.A.; Van Geertruyden, J.-P. Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia. J. Clin. Med.2020, 9, 1587.
Hassen, H.Y.; Gebreyesus, S.H.; Endris, B.S.; Roro, M.A.; Van Geertruyden, J.-P. Development and Validation of a Risk Score to Predict Low Birthweight Using Characteristics of the Mother: Analysis from BUNMAP Cohort in Ethiopia. J. Clin. Med. 2020, 9, 1587.
Abstract
At least one ultrasound is recommended to predict fetal growth restriction and low birthweight earlier in pregnancy. However, in low-income countries imaging equipment and trained manpower are scarce. Hence, we developed and validated a model and risk score to predict low birthweight using maternal characteristics during pregnancy, for use in resource limited settings. We conducted a prospective cohort study among 379 pregnant women in South Ethiopia. A step-wise multivariable analysis was done to develop the prediction model. To improve clinical utility, we developed a simplified risk score to classify pregnant women at high- or low-risk of low birthweight. The accuracy of the model was evaluated using the area under the receiver operating characteristics curve (AUC) and calibration plot. We evaluated the clinical impact of the model using a decision curve analysis across various threshold probabilities. Age at pregnancy, underweight, anemia, height, gravidity, and presence of comorbidity remained in the final multivariable prediction model. The area under the receiver operating characteristics curve (AUC) of the model was 0.83 (95% confidence interval: 0.78 to 0.88). The decision curve analysis shows the model provides a higher net benefit across ranges of threshold probabilities. In general, this study showed the possibility of predicting low birthweight using maternal characteristics during pregnancy. The model could help to identify those at higher risk of having a low birthweight baby. This feasible prediction model would offer an opportunity to reduce obstetric-related complications and thus improving the overall maternal and child healthcare in low- and middle-income countries.
Medicine and Pharmacology, Pediatrics, Perinatology and Child Health
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