Temporal trends and volume-outcome associations after traumatic brain injury: a 12-year study in Taiwan

Abstract

Object: The purpose of this study was to evaluate temporal trends in traumatic brain injury (TBI); the impact of hospital volume and surgeon volume on length of stay (LOS), hospitalization cost, and in-hospital mortality rate; and to explore predictors of these outcomes in a nationwide population in Taiwan.

Methods: This population-based patient cohort study retrospectively analyzed 16,956 patients who had received surgical treatment for TBI between 1998 and 2009. Bootstrap estimation was used to derive 95% confidence intervals for differences in effect sizes. Hierarchical linear regression models were used to predict outcomes.

Results: Patients treated in very-high-volume hospitals were more responsive than those treated in low-volume hospitals in terms of LOS (-0.11; 95% CI -0.20 to -0.03) and hospitalization cost (-0.28; 95% CI -0.49 to -0.06). Patients treated by high-volume surgeons were also more responsive than those treated by low-volume surgeons in terms of LOS (-0.19; 95% CI -0.37 to -0.01) and hospitalization cost (-0.43; 95% CI -0.81 to -0.05). The mean LOS was 24.3 days and the average LOS for very-high-volume hospitals and surgeons was 61% and 64% shorter, respectively, than that for low-volume hospitals and surgeons. The mean hospitalization cost was US $7,292.10, and the average hospitalization cost for very-high-volume hospitals and surgeons was 19% and 22% lower, respectively, than that for low-volume hospitals and surgeons. Advanced age, male sex, high Charlson Comorbidity Index score, treatment in a low-volume hospital, and treatment by a low-volume surgeon were significantly associated with adverse outcomes (p < 0.001).

Conclusions: The data suggest that annual surgical volume is the key factor in surgical outcomes in patients with TBI. The results improve the understanding of medical resource allocation for this surgical procedure, and can help to formulate public health policies for optimizing hospital resource utilization for related diseases.