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Published on: September 16, 2022
External Validation of a Logistic Regression Model to Predict Cesarean Section Risk in Indian Women
Prachi Saoji1,2, Lakshmi Madireddy1, Ajeet Saoji3
1Department of Mathematics, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research, Sawangi, Nagpur, Maharashtra, India.
Annals of African Medicine
|June 9, 2026
Summary
An existing lower segment cesarean section (LSCS) prediction model performed well in a new population. Recalibration further improved its accuracy, supporting its use for antenatal risk assessment.
Area of Science:
- Obstetrics and Gynecology
- Clinical Epidemiology
- Biostatistics
Background:
- Clinical prediction models for lower segment cesarean section (LSCS) need external validation for diverse populations.
- This study evaluated an existing LSCS risk model in an independent cohort.
- The impact of recalibration on model fit was also assessed.
Purpose of the Study:
- To externally validate a logistic regression model predicting LSCS risk.
- To assess the model's performance in a new obstetric population.
- To determine if recalibration improves the model's calibration.
Main Methods:
- Applied a previously developed logistic regression model to an external dataset without refitting.
- Evaluated model performance using discrimination (AUC) and calibration (Hosmer-Lemeshow test).
- Performed intercept-only recalibration to address calibration drift.
Main Results:
- The model showed good discrimination (AUC: 0.82) and satisfactory calibration in the external cohort.
- At a 0.50 threshold, accuracy was 81.7%, sensitivity 90.0%, and specificity 73.3%.
- Recalibration improved the alignment between predicted and observed risks.
Conclusions:
- The LSCS prediction model maintained strong performance in an external population.
- Recalibration further enhanced the model's calibration.
- The findings support the model's applicability across clinical settings for antenatal risk stratification.
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