Adverse Events and Bundled Costs after Cranial Neurosurgical Procedures: Validation of the LACE Index Across 40,431 Admissions and Development of the LACE-Cranial Index

Abstract

Objective: Anticipating postdischarge complications after neurosurgery remains difficult. The LACE index, based on 4 hospitalization descriptors, stratifies patients by risk of 30-day postdischarge adverse events but has not been validated in a procedure-specific manner in neurosurgery. Our study sought to explore the usefulness of the LACE index in a population undergoing cranial neurosurgery and to develop an enhanced model, LACE-Cranial.

Methods: The OptumClinformatics Database was used to identify cranial neurosurgery admissions (2004-2017). Procedures were grouped as trauma/hematoma/intracranial pressure, open vascular, functional/pain, skull base, tumor, or endovascular. Adverse events were defined as postdischarge death/readmission. LACE-Cranial was developed using a logistic regression framework incorporating an expanded feature set in addition to the original LACE components.

Results: A total of 40,431 admissions were included. Predictions of 30-day readmissions was best for skull base (area under the curve [AUC], 0.636) and tumor (AUC, 0.63) admissions but was generally poor. Predictive ability of 30-day mortality was best for functional/pain admissions (AUC, 0.957) and poorest for trauma/hematoma/intracranial pressure admissions (AUC, 0.613). Across procedure types except for functional/pain, a high-risk LACE score was associated with higher postdischarge bundled payment costs. Incorporating features identified to contribute independent predictive value, the LACE-Cranial model achieved procedure-specific 30-day mortality AUCs ranging from 0.904 to 0.98. Prediction of 30-day and 90-day readmissions was also improved, with tumor and skull base cases achieving 90-day readmission AUCs of 0.718 and 0.717, respectively.

Conclusions: Although the unmodified LACE index shows inconsistent classification performance, the enhanced LACE-Cranial model offers excellent prediction of short-term postdischarge mortality across procedure groups and significantly improved anticipation of short-term postdischarge readmissions.

Keywords: Complications; Craniotomy; Predictive modeling; Readmissions.