Washington Chad W, Derdeyn Colin P, Dacey Ralph G, Dhar Rajat, Zipfel Gregory J
Departments of Neurological Surgery.
J Neurosurg. 2014 Aug;121(2):482-9. doi: 10.3171/2014.4.JNS131100. Epub 2014 Jun 20.
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).
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.
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.
使用全国住院患者样本（NIS）这一基于国际疾病分类第九版（ICD - 9）的大型行政数据库来分析动脉瘤性蛛网膜下腔出血（SAH）的研究，因无法控制SAH严重程度以及使用未经验证的结局指标而受到限制。为解决这些局限性，作者开发并验证了一种SAH严重程度的替代标志物，即NIS - SAH严重程度评分（NIS - SSS；类似于Hunt和Hess [HH]分级），以及一种SAH结局的二分法测量指标，即NIS - SAH结局测量指标（NIS - SOM；类似于改良Rankin量表[mRS]评分）。
使用三个独立且不同的患者队列来定义并随后验证NIS - SSS和NIS - SOM。一个源自1998 - 2009年NIS的队列（n = 148,958，“模型人群”）用于开发NIS - SSS和NIS - SOM模型。将最能反映SAH严重程度的诊断纳入预测不良结局的回归模型；显著因素的模型系数用于生成NIS - SSS。使用全国住院患者样本中最可能反映不良结局的编码（例如，出院处置、气管切开术）来创建NIS - SOM。作者所在机构治疗的716例SAH患者（“验证人群”）的数据分别用于对照HH分级和mRS评分来验证NIS - SSS和NIS - SOM。最后，来自1998 - 2009年NIS且独立于模型人群的147,395例患者（“评估人群”）用于评估NIS - SSS在预测结局方面的性能。将NIS - SSS预测结局的能力与其他常见的疾病严重程度测量指标（所有患者精细化诊断相关组[APR - DRG]、所有支付方严重程度调整后的DRG [APS - DRG]和DRG）进行比较。结果NIS - SSS与HH分级显著相关，并且NIS - SSS和HH分级在预测基于mRS的结局方面的能力无统计学差异。与APR - DRG、APS - DRG和DRG相比，NIS - SSS在预测SAH结局方面更准确（曲线下面积[AUC]分别为0.69、0.71、0.71和0.79）。发现NIS - SOM与mRS之间存在强相关性，当将mRS评分>2定义为不良结局时，一致性和kappa统计量分别为85%和0.63，当将mRS评分>3定义为不良结局时，一致性和kappa统计量分别为95%和0.84。
本研究中的数据表明，在分析NIS数据集时，NIS - SSS是一种有效的SAH严重程度测量指标，其性能优于以往的疾病严重程度测量指标，并且NIS - SOM是一种有效的SAH结局测量指标。使用行政数据集进行SAH结局研究时，纳入NIS - SSS和NIS - SOM以调整特定于神经科的疾病严重程度至关重要。
