Mueller Kyle B, Hou Yuefeng, Beach Karen, Griffin Leah P
Dept of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA.
3M Company, St. Paul, MN, USA.
N Am Spine Soc J. 2022 Dec 23;13:100196. doi: 10.1016/j.xnsj.2022.100196. eCollection 2023 Mar.
Surgical site infection (SSI) after open spine surgery increases healthcare costs and patient morbidity. Predictive analytics using large databases can be used to develop prediction tools to aid surgeons in identifying high-risk patients and strategies for optimization. The purpose of this study was to develop and validate an SSI risk-assessment score for patients undergoing open spine surgery.
The Premier Healthcare Database of adult open spine surgery patients ( = 157,664; 2,650 SSIs) was used to create an SSI risk scoring system using mixed effects logistic regression modeling. Full and reduced multilevel logistic regression models were developed using patient, surgery or facility predictors. The full model used 38 predictors and the reduced used 16 predictors. The resulting risk score was the sum of points assigned to 16 predictors.
The reduced model showed good discriminatory capability (C-statistic = 0.75) and good fit of the model ([Pearson Chi-square/DF] = 0.90, CAIC=25,517) compared to the full model (C-statistic = 0.75, [Pearson Chi-square/DF] =0.90, CAIC=25,578). The risk scoring system, based on the reduced model, included the following: female (5 points), hypertension (4), blood disorder (8), peripheral vascular disease (9), chronic pulmonary disease (6), rheumatic disease (16), obesity (12), nicotine dependence (5), Charlson Comorbidity Index (2 per point), revision surgery (14), number of ICD-10 procedures (1 per procedure), operative time (1 per hour), and emergency/urgent surgery (12). A final risk score as the sum of the points for each surgery was validated using a 1,000-surgery random hold-out (independent from the study cohort) sample (C-statistic = 0.77).
The resulting SSI risk score composed of readily obtainable clinical information could serve as a strong prediction tool for SSI in preoperative settings when open spine surgery is considered.
开放性脊柱手术后手术部位感染(SSI)会增加医疗成本和患者发病率。利用大型数据库进行预测分析可用于开发预测工具,以帮助外科医生识别高危患者并制定优化策略。本研究的目的是为接受开放性脊柱手术的患者开发并验证一种SSI风险评估评分。
使用成人开放性脊柱手术患者的Premier医疗数据库(n = 157,664;2,650例SSI),通过混合效应逻辑回归模型创建SSI风险评分系统。使用患者、手术或机构预测因素开发完整和简化的多级逻辑回归模型。完整模型使用38个预测因素,简化模型使用16个预测因素。最终的风险评分是分配给16个预测因素的分数总和。
与完整模型(C统计量 = 0.75,[Pearson卡方/自由度] = 0.90,CAIC = 25,578)相比,简化模型显示出良好的区分能力(C统计量 = 0.75)和良好的模型拟合度([Pearson卡方/自由度] = 0.90,CAIC = 25,517)。基于简化模型的风险评分系统包括以下内容:女性(5分)、高血压(4分)、血液疾病(8分)、外周血管疾病(9分)、慢性肺部疾病(6分)、风湿性疾病(16分)、肥胖(12分)、尼古丁依赖(5分)、Charlson合并症指数(每分2分)、翻修手术(14分)、ICD - 10手术数量(每个手术1分)、手术时间(每小时1分)以及急诊/紧急手术(12分)。使用1000例手术的随机留出(独立于研究队列)样本对每个手术的分数总和作为最终风险评分进行验证(C统计量 = 0.77)。
由易于获得的临床信息组成的SSI风险评分可作为在考虑进行开放性脊柱手术时术前环境中SSI的强大预测工具。