Abstract
Background: - Preterm complications are the leading cause of death in children under the age of 5. Estimating the probability of a pregnant woman being at risk of preterm delivery would help to initiate preventive measures to reduce preterm delivery. The available risk prediction models used non-feasible predictors and did not consider fetal characteristics. This study aimed to develop an easily interpretable nomogram based on maternal and fetal characteristics.
Methods: - A retrospective follow-up study was conducted with a total of 1039 pregnant women who were enrolled from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital. Stata version 17 was used for data analysis. Important predictors were selected by the least absolute shrinkage and selection operator and entered into multivariable logistic regression. Statistically and clinically significant predictors were used for the nomogram’s development. Model performance was assessed by the area under the receiver operating curve (AUROC) and calibration plot. Internal validation was done through the bootstrapping method, and decision curve analysis was performed to evaluate the clinical and public health impacts of the model
Result: - The incidence proportion of preterm birth among pregnant women was 14.15% (95%CI: 12.03, 16.27). Antepartum hemorrhage, preeclampsia, polyhydramnios, anemia, human immune virus, malpresentation, premature rupture of membrane, and diabetic mellitus were used to develop a nomogram. The nomogram had a discriminating power AUROC of 0.79 (95% CI: 0.74, 0.83) and 0.78 (95% CI: 0.73, 0.82) on the development and validation sets. The calibration plots exhibited optimal agreement between the predicted and observed values; the Hosmer-Lemeshow test yielded a P-value of 0.602. The decision curve analysis revealed that the nomogram would add net clinical benefits at threshold probabilities less than 0.8.
Conclusion: - The developed nomogram had good discriminative performance and good calibration. Using this model could help identify pregnant women at a higher risk of preterm delivery and provide interventions like corticosteroid and progesterone administration, cervical cerclage, and nutritional support.