Procedural volume is linearly associated with mortality, major complications, and readmissions in patients undergoing malignant brain tumor resection

Abstract

Abstract Purpose Improved outcomes have been noted in patients undergoing malignant brain tumor resection at high-volume centers. Studies have arbitrarily chosen high-volume dichotomous cutoffs and have not evaluated volume-outcome associations at specific institutional procedural volumes. We sought to establish the continuous association of volume with patient outcomes and identify cutoffs significantly associated with mortality, major complications, and readmissions. We hypothesized that a linear volume-outcome relationship can estimate likelihood of adverse outcomes when comparing any two volumes. Methods The patient cohort was identified with ICD-10 coding in the Nationwide Readmissions Database(NRD). The association of volume and mortality, major complications, and 30-/90-day readmissions were evaluated in multivariate analyses. Volume was used as a continuous variable with two/three-piece splines, with various knot positions to reflect the best model performance, based on the Quasi Information Criterion(QIC). Results From 2016 to 2018, 34,486 patients with malignant brain tumors underwent resection. When volume was analyzed as a continuous variable, mortality risk decreased at a steady rate of OR 0.988 per each additional procedure increase for hospitals with 1–65 cases/year(95% CI 0.982–0.993, p < 0.0001). Risk of major complications decreased from 1 to 41 cases/year(OR 0.983, 95% CI 0.979–0.988, p < 0.0001), 30-day readmissions from 1 to 24 cases/year(OR 0.987, 95% CI 0.979–0.995, p = 0.001) and 90-day readmissions from 1 to 23 cases/year(OR 0.989, 95% CI 0.983–0.995, p = 0.0003) and 24–349 cases/year(OR 0.9994, 95% CI 0.999–1, p = 0.01). Conclusion In multivariate analyses, institutional procedural volume remains linearly associated with mortality, major complications, and 30-/90-day readmission up to specific cutoffs. The resulting linear association can be used to calculate relative likelihood of adverse outcomes between any two volumes.