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Model Development to Predict Cesarean Section: A Retrospective Study in Indian Women
Prachi Saoji1, Lakshmi Madireddy2, Ajeet Saoji3
1Department of Mathematics, Ramdeobaba University, Nagpur, Maharashtra, India.
Background:
Cesarean delivery rates are increasing globally, including in India, highlighting the need to identify risk factors to improve maternal and neonatal outcomes.
Objective:
To develop a predictive model for cesarean delivery based on maternal, clinical, and sociodemographic factors in an Indian cohort.
Methods:
A case-control study was conducted at a tertiary care hospital in Nagpur, India, including 190 participants (95 cesarean and 95 vaginal deliveries). Data were collected through structured interviews and medical record reviews. Univariate and multivariate logistic regression analyses were performed. Model performance was assessed using the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test.
Results:
Significant predictors of cesarean delivery included urban residence (adjusted odds ratio (AOR): 4.2), short stature (AOR: 2.4), obesity (AOR: 2.4), comorbidities (AOR: 2.7), inadequate antenatal care (ANC) (AOR: 2.3), and below poverty line (BPL) status (AOR: 2.1). The model demonstrated good discrimination (area under the curve (AUC): 0.835), with sensitivity of 82% and specificity of 78%.
Conclusion:
Maternal urban residence, maternal health status, and poor ANC were important predictors of cesarean delivery. The predictive model provides a useful risk stratification tool for clinical practice.
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