Predicting Post-surgery Discharge Time in Pediatric Patients Using Machine Learning
Marco Cascella1, Cosimo Guerra1, Atanas G Atanasov2,3,4
1Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, Baronissi, 84081, Italy.
Insights
Machine learning models accurately predict pediatric discharge times after tonsillectomy, identifying key factors like postoperative nausea and vomiting (PONV) to improve hospital resource management.
Area of Science:
- Pediatric Surgery
- Health Informatics
- Machine Learning in Healthcare
Background:
- Prolonged hospital stays after pediatric tonsillectomy and adenoidectomy increase costs and impact patient care.
- Identifying determinants of extended hospitalization is vital for optimizing postoperative management and resource allocation.
Purpose of the Study:
- To apply machine learning (ML) for predicting post-surgery discharge times in pediatric patients.
- To identify key variables influencing hospital stay duration after tonsillectomy and/or adenoidectomy.
Main Methods:
- Analysis of data from 423 pediatric patients undergoing tonsillectomy/adenoidectomy.
- Data preprocessing included handling missing values, outlier detection, and variable transformation.
- Four ML algorithms (Random Forest, Logistic Regression, RUSBoost, AdaBoost) were trained and evaluated using 10-fold cross-validation.
Main Results:
- Postoperative nausea and vomiting (PONV), dexmedetomidine infusion, fentanyl use, discharge pain, and extubation time were significant predictors of delayed discharge.
- The AdaBoost model achieved high accuracy and reliability in predicting discharge times.
Conclusions:
- ML models effectively predict pediatric discharge times and pinpoint critical factors for prolonged hospitalization.
- These predictive insights can refine postoperative care strategies and resource management in pediatric surgery.
- Integration of ML models into clinical practice can support real-time decision-making.
Background:
Prolonged hospital stays after pediatric surgeries, such as tonsillectomy and adenoidectomy, pose significant concerns regarding cost and patient care. Dissecting the determinants of extended hospitalization is crucial for optimizing postoperative care and resource allocation.
Objective:
This study aims to utilize machine learning (ML) techniques to predict post-surgery discharge times in pediatric patients and identify key variables influencing hospital stays.
Methods:
The study analyzed data from 423 children who underwent tonsillectomy and/or adenoidectomy at the IRCCS Istituto Giannina Gaslini, Genoa, Italy. Variables included demographic factors, anesthesia-related details, and postoperative events. Preprocessing involved handling missing values, detecting outliers, and converting categorical variables to numerical classes. Univariate statistical analyses identified features correlated with discharge time. Four ML algorithms-Random Forest (RF), Logistic Regression, RUSBoost, and AdaBoost-were trained and evaluated using stratified 10-fold cross-validation.
Results:
Significant predictors of delayed discharge included postoperative nausea and vomiting (PONV), continuous infusion of dexmedetomidine, fentanyl use, pain during discharge, and extubation time. The best-performing model, AdaBoost, demonstrated high accuracy and reliable prediction capabilities, with strong performance metrics across all evaluation criteria.
Conclusion:
ML models can effectively predict discharge times and highlight critical factors impacting prolonged hospitalization. These insights can enhance postoperative care strategies and resource management in pediatric surgical settings. Future research should explore integrating these predictive models into clinical practice for real-time decision support.
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