Predictive model of ischemic optic neuropathy in spinal fusion surgery using a longitudinal medical claims database.
Predictive model of ischemic optic neuropathy in spinal fusion surgery using a longitudinal medical claims database.
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DOI：
10.1016/j.spinee.2020.11.011
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Roth S
中科院分区：
文献类型：
--
作者：
Moss HE;Xiao L;Shah SH;Chen YF;Joslin CE;Roth S
围手术期缺血性视神经病变（ION）是脊柱融合手术的一种严重并发症。使用纵向医疗管理索赔数据库开发这种设盲情况的预测模型，该数据库提供围手术期缺血性视神经病变和潜在风险因素的时间序列。嵌套病例对照研究Cliniformatics® Data Mart医疗索赔数据库（2007-2017年）中涉及腰椎或胸椎融合手术住院且无ION病史的参与者。腰椎或胸椎融合手术住院期间的围手术期离子（或非离子）。识别出65例ION病例和106，871例对照。根据手术年份和邮政编码选择匹配的对照组（n=211）。根据医疗索赔代码分配慢性和围手术期变量。使用具有十倍交叉验证的最小绝对收缩和选择（LASSO）惩罚条件逻辑回归从病例和匹配对照之间p < 0.15的变量子集（未调整的条件逻辑回归）中选择用于最佳预测模型的变量。生成分层独立匹配和全样本的受试者工作特征（ROC）曲线。预测模型包括年龄57-65岁，男性，糖尿病伴或不伴并发症，慢性贫血，高血压，心力衰竭，颈动脉狭窄，围手术期出血和围手术期器官损害。匹配样本的ROC曲线下面积为0.75（95%CI：0.68，0.82），全样本的ROC曲线下面积为0.72（95%CI：0.66，0.78）。考虑慢性疾病和围手术期疾病的脊柱融合中ION的预测模型迄今为止在使用纵向医疗索赔数据、纳入ICD-10代码和将眼科疾病作为风险因素的研究方面是独一无二的。与该疾病的其他研究相似，多变量模型包括年龄、男性、围手术期器官损伤和围手术期出血。高血压、慢性贫血和颈动脉狭窄是本研究确定的新预测因素。
Perioperative ischemic optic neuropathy (ION) is a devastating complication of spinal fusion surgery. To develop predictive models of this blinding condition using longitudinal medical administrative claims databases, which provide temporal sequence of perioperative ischemic optic neuropathy and potential risk factors. Nested case control study Participants in Cliniformatics® Data Mart medical claims database (2007–2017) with hospitalization involving lumbar or thoracic spinal fusion surgery and no history of ION. Peri-operative ION (or not) during hospitalization for lumbar or thoracic spinal fusion surgery. 65 ION cases and 106,871 controls were identified. Matched controls (n=211) were selected based on year of surgery and zip code. Chronic and peri-operative variables were assigned based on medical claims codes. Least absolute shrinkage and selection (LASSO) penalized conditional logistic regression with ten-fold cross validation was used to select variables for the optimal predictive model from the subset of variables with p < 0.15 between cases and matched controls (unadjusted conditional logistic regression). Receiver operating characteristic (ROC) curves were generated for the strata-independent matched and full sample. The predictive model included age 57–65 years, male gender, diabetes with and without complications, chronic anemia, hypertension, heart failure, carotid stenosis, perioperative hemorrhage and perioperative organ damage in the predictive model. Area under ROC curve was 0.75 (95% CI: 0.68, 0.82) for the matched sample and 0.72 (95% CI: 0.66, 0.78) for the full sample. This predictive model for ION in spine fusion considering chronic conditions and perioperative conditions is unique to date in its use of longitudinal medical claims data, inclusion of ICD-10 codes and study of ophthalmic conditions as risk factors. Similar to other studies of this condition the multivariable model included age, male gender, peri-operative organ damage and peri-operative hemorrhage. Hypertension, chronic anemia and carotid artery stenosis were new predictive factors identified by this study.
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回顾和评估用于事件少的低维数据风险预测的惩罚回归方法。
影响因子：
2
作者：
Pavlou M;Ambler G;Seaman S;De Iorio M;Omar RZ
通讯作者：
Omar RZ
影响因子：
8.8
作者：
Lee, Lorri A.;Roth, Steven;Metzner, Julia
通讯作者：
Metzner, Julia
DOI：
10.1016/j.spinee.2020.05.554
发表时间：
2020-10
期刊：
The spine journal : official journal of the North American Spine Society
影响因子：
--
作者：
Moss HE;Xiao L;Shah SH;Chen YF;Joslin C;Roth S
通讯作者：
Roth S
影响因子：
105.7
作者：
Greenland, Sander;Mansournia, Mohammad Ali;Altman, Douglas G.
通讯作者：
Altman, Douglas G.
脊柱融合手术中的围手术期视觉丧失：1998年至2012年美国在全国住院样本中的缺血性视神经病变。
影响因子：
8.8
作者：
Rubin DS;Parakati I;Lee LA;Moss HE;Joslin CE;Roth S
通讯作者：
Roth S