RESUMEN
Objectives: Comprehensive preoperative management involves the identification and optimization of medical comorbidities while avoiding excessive healthcare utilization. While diabetes and heart disease are major causes of morbidity that can worsen surgical outcomes, further study is needed to evaluate how well current perioperative strategies mitigate their risks. This study employs an exact matching protocol to isolate the effects of both diabetes and cardiovascular disease on spine surgery outcomes. Methods: 4680 consecutive patients undergoing single-level, posterior-only lumbar fusion were retrospectively enrolled. Univariate logistic regression was performed on comorbidity subgroups, then coarsened exact matching (CEM) was employed for patients with diabetes or cardiovascular disease. Patients were matched 1:1 on ten patient and procedural characteristics known to affect neurosurgical outcomes. Separate pairs of exact-matched cohorts were generated to isolate both cardiovascular disease (matched n = 192), and diabetes (matched n = 380). Primary outcomes were surgical complications; length of stay; discharge disposition (home vs. non-home); and 30- and 90-day Emergency Department (ED) visits, readmissions, reoperations, and mortality. Results: Cardiovascular disease and diabetes subgroups were not associated with short term outcomes after matching to control for confounders. Compared to univariate statistics, this method demonstrates that confounding control variables may drive outcomes more than these comorbidities themselves. Conclusion: Between otherwise exactly matched patients undergoing lumbar fusion, diabetes and cardiovascular disease posed no greater risk of short-term adverse outcomes. This suggests proper selection criteria for surgical candidates and effective current perioperative strategies to mitigate these common comorbidities. Further studies are warranted to assess and optimize the cost-effectiveness of preoperative management for patients with common comorbidities.
RESUMEN
OBJECTIVE: Preoperative management requires the identification and optimization of modifiable medical comorbidities, though few studies isolate comorbid status from related patient-level variables. This study evaluates Charlson Comorbidity Index (CCI)-an easily derived measure of aggregate medical comorbidity-to predict outcomes from spinal fusion surgery. Coarsened exact matching is employed to control for key patient characteristics and isolate CCI. METHODS: We retrospectively assessed 4680 consecutive patients undergoing single-level, posterior-only lumbar fusion at a single academic center. Logistic regression evaluated the univariate relationship between CCI and patient outcomes. Coarsened exact matching generated exact demographic matches between patients with high comorbid status (CCI >6) or no medical comorbidities (matched n = 524). Patients were matched 1:1 on factors associated with surgical outcomes, and outcomes were compared between matched cohorts. Primary outcomes included surgical complications, discharge status, 30- and 90-day risk of readmission, emergency department (ED) visits, reoperation, and mortality. RESULTS: Univariate regression of increasing CCI was significantly associated with non-home discharge, as well as 30- and 90-day readmission, ED visits, and mortality (all P < 0.05). Subsequent isolation of comorbidity between otherwise exact-matched cohorts found comorbid status did not affect readmissions, reoperations, or mortality; high CCI score was significantly associated with non-home discharge (OR = 2.50, P < 0.001) and 30-day (OR = 2.44, P = 0.02) and 90-day (OR = 2.29, P = 0.008) ED evaluation. CONCLUSIONS: Comorbidity, measured by CCI, did not increase the risk of readmission, reoperation, or mortality. Single-level, posterior lumbar fusions may be safe in appropriately selected patients regardless of comorbid status. Future studies should determine whether CCI can guide discharge planning and postoperative optimization.