# Development and Internal Validation of Machine Learning Algorithms to Predict 30-Day Readmission in Patients Undergoing a C-Section: A Nation-Wide Analysis

## TL;DR

This study uses machine learning to predict which patients who had a C-section are likely to be readmitted to the hospital within 30 days, aiming to improve care and reduce costs.

## Contribution

The study introduces and validates machine learning models for predicting C-section readmissions using nationwide data and identifies key risk factors.

## Key findings

- 2.39% of patients who underwent C-sections were readmitted within 30 days.
- Machine learning models, especially Random Forests, outperformed logistic regression in predicting readmissions.
- Key predictors of readmission included age, BMI, operative time, white blood cell count, and hematocrit.

## Abstract

Background/Objectives: Cesarean section (C-section) is a common surgical procedure associated with an increased risk of 30-day postpartum hospital readmissions. This study utilized machine learning (ML) to predict readmissions using a nationwide database. Methods: A retrospective analysis of the National Surgical Quality Improvement Project (2012–2022) included 54,593 patients who underwent C-sections. Random Forests (RF) and Extreme Gradient Boosting (XGBoost) models were developed and compared to logistic regression (LR) using demographic, preoperative, and perioperative data. Results: Of the cohort, 1306 (2.39%) patients were readmitted. Readmitted patients had higher rates of being of African American race (17.99% vs. 9.83%), diabetes (11.03% vs. 8.19%), and hypertension (11.49% vs. 4.68%) (p < 0.001). RF achieved the highest performance (AUC = 0.737, sensitivity = 72.03%, specificity: 61.33%), and a preoperative-only RF model achieved a sensitivity of 83.14%. Key predictors included age, BMI, operative time, white blood cell count, and hematocrit. Conclusions: ML effectively predicts C-section readmissions, supporting early identification and interventions to improve patient outcomes and reduce healthcare costs.