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Objective: Despite the proven efficacy of surgical intervention for achieving seizure freedom and improved quality of life for many epilepsy patients, this treatment remains underutilized. In this study, the authors assessed sociodemographic trends in epilepsy surgery in the National Inpatient Sample (NIS) and the Kids' Inpatient Database (KID) and sought to determine whether disparities in surgical intervention for epilepsy may be attributed to insurance and comorbidity status., Methods: This cross-sectional study utilized data from the NIS database and KID from the Healthcare Cost and Utilization Project between the years 2012 and 2018. Outcomes of interest were rates of neurosurgical intervention, including resection, neuromodulation, or laser ablation. The authors utilized logit regression models to test the association between rates of neurosurgical intervention and the variables of interest and calculated the adjusted mean proportion of patients who received surgery using marginal effects., Results: Of 336,015 admissions with intractable epilepsy in the NIS, 6.1% were patients who underwent neurosurgical treatment. Of 39,655 admissions from KID, 5.0% received surgical treatment. Private insurance was associated with a greater odds of surgical intervention compared with Medicaid (NIS: OR 1.63, KID: OR 1.62; p < 0.001). Patients assigned White race had an increased odds ratio of undergoing surgery when compared with those assigned Black race, adjusted for comorbidity burden (NIS: OR 1.59, p < 0.001; KID: OR 1.44, p = 0.027). Patients with an Elixhauser Comorbidity Index score of 0 or 1 were associated with a lower likelihood of surgery when compared to their higher scoring counterparts who had 4 or more comorbidities (NIS: OR 0.74, KID: OR 0.62; both p < 0.001)., Conclusions: This study demonstrates that marginalized patients and those with Medicaid had decreased odds of neurosurgical intervention for epilepsy. Results of this research support the need for increased attention toward epilepsy patients from marginalized groups. Further investigation into the root cause of socioeconomic inequities in epilepsy surgery is necessary.

Objective: Research has documented significant growth in neurosurgical expenditures and practice consolidation. The authors evaluated the relationship between interhospital competition and inpatient charges or costs in patients undergoing cranial neurosurgery., Methods: The authors identified all admissions in 2006 and 2009 from the National Inpatient Sample. Admissions were classified into 5 subspecialties: cerebrovascular, tumor, CSF diversion, neurotrauma, or functional. Hospital-specific interhospital competition levels were quantified using the Herfindahl-Hirschman Index (HHI), an economic metric ranging continuously from 0 (significant competition) to 1 (monopoly). Inpatient charges (hospital billing) were multiplied with reported cost-to-charge ratios to calculate costs (actual resource use). Multivariate regressions were used to assess the association between HHI and inpatient charges or costs separately, controlling for 17 patient, hospital, severity, and economic factors. The reported β-coefficients reflect percentage changes in charges or costs (e.g., β-coefficient = 1.06 denotes a +6% change). All results correspond to a standardized -0.1 change in HHI (increase in competition)., Results: In total, 472,938 nationwide admissions for cranial neurosurgery treated at 896 unique hospitals met inclusion criteria. Hospital HHIs ranged from 0.099 to 0.724 (mean 0.298 ± 0.105). Hospitals in more competitive markets had greater charge/cost markups (β-coefficient = 1.10, p < 0.001) and area wage indices (β-coefficient = 1.04, p < 0.001). Between 2006 and 2009, average neurosurgical charges and costs rose significantly ($62,098 to $77,812, p < 0.001; $21,385 to $22,389, p < 0.001, respectively). Increased interhospital competition was associated with greater charges for all admissions (β-coefficient = 1.07, p < 0.001) as well as cerebrovascular (β-coefficient = 1.08, p < 0.001), tumor (β-coefficient = 1.05, p = 0.039), CSF diversion (β-coefficient = 1.08, p < 0.001), neurotrauma (β-coefficient = 1.07, p < 0.001), and functional neurosurgery (β-coefficient = 1.11, p = 0.037) admissions. However, no significant associations were observed between HHI and costs, except for CSF diversion surgery (β-coefficient = 1.03, p = 0.021). Increased competition was not associated with important clinical outcomes, such as inpatient mortality, favorable discharge disposition, or complication rates, except for lower mortality for brain tumors (OR 0.78, p = 0.026), but was related to greater length of stay for all admissions (β-coefficient = 1.06, p < 0.001). For a sensitivity analysis adjusting for outcomes, all findings for charges and costs remained the same., Conclusions: Hospitals in more competitive markets exhibited higher charges for admissions of patients undergoing an in-hospital cranial procedure. Despite this, interhospital competition was not associated with increased inpatient costs except for CSF diversion surgery. There was no corresponding improvement in outcomes with increased competition, with the exception of a potential survival benefit for brain tumor surgery.

Objective: Traumatic intracranial hemorrhage (tICH) is a significant source of morbidity and mortality in trauma patients. While prognostic models for tICH outcomes may assist in alerting clinicians to high-risk patients, previously developed models face limitations, including low accuracy, poor generalizability, and the use of more prognostic variables than is practical. This study aimed to construct a simpler and more accurate method of risk stratification for all tICH patients., Methods: The authors retrospectively identified a consecutive series of 4110 patients admitted to their institution's level 1 trauma center between 2003 and 2013. For each admission, they collected the patient's sex, age, systolic blood pressure, blood alcohol concentration, antiplatelet/anticoagulant use, Glasgow Coma Scale (GCS) score, Injury Severity Score, presence of epidural hemorrhage, presence of subdural hemorrhage, presence of subarachnoid hemorrhage, and presence of intraparenchymal hemorrhage. The final study population comprised 3564 patients following exclusion of records with missing data. The dependent variable under study was patient death. A k-fold cross-validation was carried out with the best models selected via the Akaike Information Criterion. These models risk stratified the study partitions into grade I (< 1% predicted mortality), grade II (1%-10% predicted mortality), grade III (10%-40% predicted mortality), or grade IV (> 40% predicted mortality) tICH. Predicted mortalities were compared with actual mortalities within grades to assess calibration. Concordance was also evaluated. A final model was constructed using the entire data set. Subgroup analysis was conducted for each hemorrhage type., Results: Cross-validation demonstrated good calibration (p < 0.001 for all grades) with a mean concordance of 0.881 (95% CI 0.865-0.898). In the authors' final model, older age, lower blood alcohol concentration, antiplatelet/anticoagulant use, lower GCS score, and higher Injury Severity Score were all associated with greater mortality. Subgroup analysis showed successful stratification for subarachnoid, intraparenchymal, grade II-IV subdural, and grade I epidural hemorrhages., Conclusions: The authors developed a risk stratification model for tICH of any GCS score with concordance comparable to prior models and excellent calibration. These findings are applicable to multiple hemorrhage subtypes and can assist in identifying low-risk patients for more efficient resource allocation, facilitate family conversations regarding goals of care, and stratify patients for research purposes. Future work will include testing of more variables, validation of this model across institutions, as well as creation of a simplified model whose outputs can be calculated mentally.