Development and External Validation of a Risk Calculator for Prediction of Major Complications and Readmission After Anterior Cervical Discectomy and Fusion

Abstract

Study Design. A retrospective, case-control study. Objective. We aim to build a risk calculator predicting major perioperative complications after anterior cervical fusion. In addition, we aim to externally validate this calculator with an institutional cohort of patients who underwent anterior cervical discectomy and fusion (ACDF). Summary of Background Data. The average age and proportion of patients with at least one comorbidity undergoing ACDF have increased in recent years. Given the increased morbidity and cost associated with perioperative complications and unplanned readmission, accurate risk stratification of patients undergoing ACDF is of great clinical utility. Methods. This is a retrospective cohort study of adults who underwent anterior cervical fusion at any nonfederal California hospital between 2015 and 2017. The primary outcome was major perioperative complication or 30-day readmission. We built standard and ensemble machine learning models for risk prediction, assessing discrimination, and calibration. The best-performing model was validated on an external cohort comprised of consecutive adult patients who underwent ACDF at our institution between 2013 and 2020. Results. A total of 23,184 patients were included in this study; there were 1886 cases of major complication or readmissions. The ensemble model was well calibrated and demonstrated an area under the receiver operating characteristic curve of 0.728. The variables most important for the ensemble model include male sex, medical comorbidities, history of complications, and teaching hospital status. The ensemble model was evaluated on the validation cohort (n = 260) with an area under the receiver operating characteristic curve of 0.802. The ensemble algorithm was used to build a web-based risk calculator. Conclusion. We report derivation and external validation of an ensemble algorithm for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion. This model has excellent discrimination and is well calibrated when tested on a contemporaneous external cohort of ACDF cases.

Utility of the LACE index to assess risk of mortality and readmission in patients with spinal infections

“…Regarding outcome prediction, age, insurance status, paralysis, and medical comorbidities are thought to be possible predictors of morbidity, mortality, and expense of care for patients following surgical treatment of spinal epidural abscess [ 13 ]. In contrast to widely used traditional statistical approaches, recent studies have reported on machine learning based models for prediction of major perioperative complications and 30-day readmission after anterior cervical fusion surgery, or for readmission and estimated cost savings for patients undergoing posterior lumbar fusion surgery [ 14 , 15 ]. Interestingly, despite acknowledging the discriminatory ability of the LACE index, Rezaii and colleagues maintained superior predictive ability of their machine learning model compared to the LACE index in predicting readmissions and projected cost reductions based on their data [ 1 , 14 ].…”