Model Development to Predict Cesarean Section: A Retrospective Study in Indian Women

Area of Science:

• Obstetrics and Gynecology
• Public Health
• Epidemiology

Background:

• Rising cesarean delivery rates globally and in India necessitate identification of associated risk factors.
• Improving maternal and neonatal outcomes is a key public health concern linked to delivery methods.

Purpose of the Study:

• To develop and validate a predictive model for cesarean delivery.
• To identify maternal, clinical, and sociodemographic risk factors in an Indian population.

Main Methods:

• Case-control study involving 190 participants (95 cesarean, 95 vaginal deliveries) in Nagpur, India.
• Data collection via structured interviews and medical record reviews.
• Logistic regression analysis and ROC curve assessment for model performance.

Main Results:

• Key predictors for cesarean delivery identified: urban residence, short stature, obesity, comorbidities, inadequate antenatal care (ANC), and below poverty line (BPL) status.
• The predictive model showed good discrimination with an AUC of 0.835, sensitivity of 82%, and specificity of 78%.

Conclusions:

• Maternal urban residence, health status, and insufficient ANC are significant predictors of cesarean delivery.
• The developed model serves as a valuable risk stratification tool for clinical application in managing delivery modes.