Diabetes and heart disease do not affect short-term lumbar fusion outcomes accounting for other risk factors in a matched cohort analysis

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.