PURPOSE

Readmission is a vital component of healthcare quality and is one of the core group metrics for quality-dependent outcomes. Currently, variables predictive of readmission following elective craniotomies for intracranial tumors in the pediatric population are not known. We sought to identify such variables in our population of children and young adults.

METHODS

All elective craniotomies for tumor resection performed at our children's hospital from January 1, 2010, through December 31, 2022, were included for review, excluding those patients > 21 years of age. Demographic, clinical, and procedural covariates for each elective craniotomy for tumor resection were collected. Readmission was defined as readmission for any reason and to any service following discharge from the index admission (i.e., elective craniotomy). Readmission events were characterized as occurring within 90 days from discharge.

RESULTS

A total of 1,276 patients underwent a total of 1,497 elective craniotomies for tumor resection. The median age of the population at their index operations was 9.45 years, of which 58.5% of patients were male, 68.5% Caucasian, and 76.5% had private insurance. Most tumor resections were supratentorial (63.4%). There were 208 (13.9%) readmissions within 90 days of index operation, with 154 (74%) of those returning within the first 30 days. Bivariate analysis identified a number of associations, but multivariate testing found four significant predictors: age 0 to < 5 years (OR 1.55, p = 0.02), surgical time (OR 1.002, p = 0.02), high tumor grade (OR 3.15, p = 0.03), and return to the neurosurgical OR due to postoperative event (POE) (OR 2.81, p = 0.005).

CONCLUSION

Utilizing our large pediatric tumor database, we identified key drivers of readmission following elective tumor resection. These were young children (0 to < 5 years), surgical time, high tumor grade, and return to the neurosurgical OR due to POE, of which high tumor grade was the strongest. Future studies are warranted to explore the specific ways that these predictors increase readmission risk.