Traumatic spinal cord injury (TSCI) is a debilitating disease that poses significant functional and economic burden on both the individual and societal levels. Prognosis is dependent on the extent of the spinal injury and the severity of neurological dysfunction. If not treated rapidly, patients with TSCI can suffer further secondary damage and experience escalating disability and complications. It is important to quickly assess the patient to identify the location and severity of injury to make a decision to pursue a surgical and/or conservative management. However, there are many conditions that factor into the management of TSCI patients, ranging from the initial presentation of the patient to long-term care for optimal recovery. Here, we provide a comprehensive review of the etiologies of spinal cord injury and the complications that may arise, and present an algorithm to aid in the management of TSCI.

OBJECTIVES Certain patients ultimately undergo explantation of their spinal cord stimulation (SCS) devices. Understanding the predictors and rates of SCS explantation has important implications for healthcare resource utilization (HCRU) and pain management. The present study identifies explant predictors and discerns differences in HCRU for at-risk populations. METHODS We designed a large, retrospective analysis using the Truven MarketScan Database. We included all adult patients who underwent a SCS trial from 2007 to 2012. Patients were grouped into cohorts that remained explant-free or underwent explantation over a 3-year period, and multivariate models evaluated differences in healthcare resource utilization. RESULTS A total of 8,727 unique instances of trial implants between 2007 and 2012 were identified. Overall, 805 (9.2%) patients underwent device explantation. One year prior to SCS implantation, the explant cohort had significantly higher median baseline costs ($42,140.3 explant vs. $27,821.7 in non-explant groups; p<0.0001), total number of pain encounters (180 vs. 103 p<0.0001), and associated costs ($15,446.9 vs. $9,227.9; p<0.0001). The explant cohort demonstrated increased use of procedures (19.0 vs. 9.0; p<0.0001) compared to non-explanted patients. For each month after initial SCS implantation, explanted patients had a slower decrease in total costs (4% vs. 6% in non-explant; p<0.01). At the month of explant, explant patients were expected to have incurred 2.65 times the total cost compared to the non-explant cohort (CR 2.65, 95% CI [1.83, 3.84]; p<0.001). Medium volume providers had lower rates of explantation at 1-year and 3-years compared to low volume providers (p=0.042). Increased age and Charlson index were independent predictors of explantation during the same periods. CONCLUSIONS In this nationwide analysis, we identified that SCS device explantation is correlated with patients who have higher baseline costs, higher total cost post-SCS implantation, and increased use of procedures to control pain. The higher rates of explantation at 3-years post-implant among low volume providers suggest that variations in provider experience and approach also contributes to differences in explantation rates.

Introduction A shorter delay time from chronic pain diagnosis to spinal cord stimulation (SCS) implantation may make it more likely to achieve lasting therapeutic efficacy with SCS. The objective of this analysis was to determine the impact of pain-to-SCS time on patients’ post-implant healthcare resource utilization. Methods A retrospective observational study was performed using a real-world patient cohort derived from MarketScan® Commercial and Medicare Supplemental claims databases from April 2008 through March 2013. The predictor variable was the time from the first diagnosis of chronic pain to permanent SCS implant. Using multivariable analysis, we studied the impact of pain-to-SCS time on healthcare resource utilization in the first year post-implant. For some regression tests, patients were grouped into terciles by healthcare resource utilization. Results A total of 762 patients met inclusion criteria, with a median pain-to-SCS time of 1.35 years (Q1: 0.8, Q3: 1.9). For every one-year increase in pain-to-SCS time, the odds increased by 33% for being in the high medical expenditures group (defined using the upper tercile: $4,133 over above) over the low group (first lower: $603 or less). The odds increased by 39% for being in the high opioid prescriptions group (10-58 prescriptions) over the low group (0-1). The odds increased by 44% and 55%, respectively, for being in the high office visits (8-77) or hospitalizations (3-28) group over the low office visits (0-2) or hospitalizations (0) group. Conclusions Healthcare resource utilization increased in the year following SCS implantation with longer pain-to-SCS time. These results suggest that considering SCS earlier in the care continuum for chronic pain may improve patient outcomes, with reductions in hospitalizations, clinic visits, and opioid usage.