327 Machine Learning for Enhanced Prognostication: Predicting 30-Day Outcomes Following Posterior Fossa Decompression Surgery for Chiari Malformation Type I in a Pediatric Cohort

INTRODUCTION: Chiari malformation type I (CM-I) is a congenital disorder occurring in 0.1% of the population. In symptomatic cases, surgery with posterior fossa decompression (PFD) is the treatment of choice. Surgery is, however, associated with peri- and postoperative complications that may require readmission or renewed surgical intervention. METHODS: This was a retrospective study based on data from the National Surgical Quality Improvement Program-Pediatric database. Eligible patients were those undergoing PFD for CM-I between 2012-2021. Unplanned 30-day readmission reoperation were the main study outcomes. Additional outcome data considered included the length of hospital stay, 30-day complications, discharge disposition, and 30-day mortality. Training and testing samples were randomly generated(80:20) to study the 30-day readmission and reoperation using supervised machine learning algorithms. RESULTS: A total of 7106 pediatric patients undergoing PFD were included. The median age was 9.2 years. Most of the patients were female (56%). The 30-day readmission and reoperation rates were 7.5% and 3.4%, respectively. Headaches (32%) and wound-related complications (30%) were the most common reasons for 30-day readmission, while wound revisions and evacuation of fluid or blood (62%), followed by CSF diversion-related procedures (28%), were the most common reasons for 30-day reoperation. Random Forest(RF) had the highest predictive accuracy for both 30-day readmissions (Accuracy=0.96) and reoperations (AUC=0.99) compared with the other models. On feature importance analysis, sex, developmental delay, ethnicity, respiratory disease, premature birth, hydrocephalus, and congenital/genetic anomaly were some of the variables contributing the most to both RF models. CONCLUSIONS: Machine learning models for the prediction of both 30-day readmissions and reoperations were developed and achieved high accuracy. This highlights the utility of machine learning in risk stratification and surgical decision-making for pediatric CM-I.