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Development and Internal Validation of Machine Learning Algorithms to Predict 30-Day Readmission in Patients

Audrey Andrews1, Nadia Islam2, George Bcharah2

  • 1Department of Obstetrics and Gynecology, Creighton University, Phoenix, AZ 85012, USA.

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Summary

Machine learning models can predict 30-day postpartum hospital readmissions after cesarean sections (C-sections). These models help identify high-risk patients for early intervention, improving outcomes and reducing costs.

Keywords:
Cesarean sectionmachine learningmaternal healthpostpartum readmissionpredictive modelingrisk stratification

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Area of Science:

  • Obstetrics and Gynecology
  • Surgical Outcomes Research
  • Health Informatics

Background:

  • Cesarean section (C-section) is a frequent surgical procedure.
  • C-sections are linked to a higher risk of 30-day postpartum hospital readmissions.
  • Predicting these readmissions is crucial for patient care and healthcare efficiency.

Purpose of the Study:

  • To develop and compare machine learning (ML) models for predicting 30-day postpartum readmissions after C-sections.
  • To identify key predictors of readmission using a large, nationwide dataset.
  • To assess the performance of different ML algorithms in this predictive task.

Main Methods:

  • Retrospective analysis of the National Surgical Quality Improvement Project (2012-2022) database.
  • Inclusion of 54,593 patients who underwent C-sections.
  • Development and comparison of Random Forests (RF), Extreme Gradient Boosting (XGBoost), and logistic regression (LR) models using demographic, preoperative, and perioperative data.

Main Results:

  • A total of 1306 (2.39%) patients were readmitted within 30 days.
  • Readmitted patients showed higher prevalence of African American race, diabetes, and hypertension (p < 0.001).
  • The RF model demonstrated the highest performance (AUC = 0.737), with a preoperative-only RF model achieving 83.14% sensitivity. Key predictors included age, BMI, operative time, WBC count, and hematocrit.

Conclusions:

  • Machine learning models are effective in predicting C-section readmissions.
  • Early identification of high-risk patients is feasible using ML.
  • These predictive capabilities can guide interventions to improve patient outcomes and reduce healthcare expenditures.