Chad W. Washington, Colin P. Derdeyn, Ralph G. Dacey, Rajat Dhar, Gregory J. Zipfel

Object Studies using the Nationwide Inpatient Sample (NIS), a large ICD-9-based (International Classification of Diseases, Ninth Revision) administrative database, to analyze aneurysmal subarachnoid hemorrhage (SAH) have been limited by an inability to control for SAH severity and the use of unverified outcome measures. To address these limitations, the authors developed and validated a surrogate marker for SAH severity, the NIS-SAH Severity Score (NIS-SSS; akin to Hunt and Hess [HH] grade), and a dichotomous measure of SAH outcome, the NIS-SAH Outcome Measure (NIS-SOM; akin to modified Rankin Scale [mRS] score). Methods Three separate and distinct patient cohorts were used to define and then validate the NIS-SSS and NIS-SOM. A cohort (n = 148,958, the "model population") derived from the 1998-2009 NIS was used for developing the NIS-SSS and NIS-SOM models. Diagnoses most likely reflective of SAH severity were entered into a regression model predicting poor outcome; model coefficients of significant factors were used to generate the NIS-SSS. Nationwide Inpatient Sample codes most likely to reflect a poor outcome (for example, discharge disposition, tracheostomy) were used to create the NIS-SOM. Data from 716 patients with SAH (the "validation population") treated at the authors' institution were used to validate the NIS-SSS and NIS-SOM against HH grade and mRS score, respectively. Lastly, 147,395 patients (the "assessment population") from the 1998-2009 NIS, independent of the model population, were used to assess performance of the NIS-SSS in predicting outcome. The ability of the NIS-SSS to predict outcome was compared with other common measures of disease severity (All Patient Refined Diagnosis Related Group [APR-DRG], All Payer Severity-adjusted DRG [APS-DRG], and DRG). Results The NIS-SSS significantly correlated with HH grade, and there was no statistical difference between the abilities of the NIS-SSS and HH grade to predict mRS-based outcomes. As compared with the APR-DRG, APSDRG, and DRG, the NIS-SSS was more accurate in predicting SAH outcome (area under the curve [AUC] = 0.69, 0.71, 0.71, and 0.79, respectively). A strong correlation between NIS-SOM and mRS was found, with an agreement and kappa statistic of 85% and 0.63, respectively, when poor outcome was defined by an mRS score > 2 and 95% and 0.84 when poor outcome was defined by an mRS score > 3. Conclusions Data in this study indicate that in the analysis of NIS data sets, the NIS-SSS is a valid measure of SAH severity that outperforms previous measures of disease severity and that the NIS-SOM is a valid measure of SAH outcome. It is critically important that outcomes research in SAH using administrative data sets incorporate the NIS-SSS and NIS-SOM to adjust for neurology-specific disease severity.