Mueller Kyle B, Hou Yuefeng, Beach Karen, Griffin Leah P
Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
3M Healthcare, St. Paul, MN, USA.
J Spine Surg. 2024 Mar 20;10(1):40-54. doi: 10.21037/jss-23-89. Epub 2024 Jan 4.
Surgical site complications (SSCs) contribute to increased healthcare costs. Predictive analytics can aid in identifying high-risk patients and implementing optimization strategies. This study aimed to develop and validate a risk-assessment score for SSC-associated readmissions (SSC-ARs) in patients undergoing open spine surgery.
The Premier Healthcare Database (PHD) of adult patients (n=157,664; 3,182 SSC-ARs) between January 2019 and September 2020 was used for retrospective data analysis to create an SSC risk score using mixed effects logistic regression modeling. Full and reduced models were developed using patient-, facility-, or procedure-related predictors. The full model used 37 predictors and the reduced used 19.
The reduced model exhibited fair discriminatory capability (C-statistic =74.12%) and demonstrated better model fit [Pearson chi-square/degrees of freedom (DF) =0.93] compared to the full model (C-statistic =74.56%; Pearson chi-square/DF =0.92). The risk scoring system, based on the reduced model, comprised the following factors: female (1 point), blood disorder [2], congestive heart failure [2], dementia [3], chronic pulmonary disease [2], rheumatic disease [3], hypertension [2], obesity [2], severe comorbidity [2], nicotine dependence [1], liver disease [2], paraplegia and hemiplegia [3], peripheral vascular disease [2], renal disease [2], cancer [1], diabetes [2], revision surgery [2], operative hours ≥5 [4], emergency/urgent surgery [2]. A final risk score (sum of the points for each surgery; range, 0-40) was validated using a 1,000-surgery random hold-out sample (C-statistic =85.16%).
The resulting SSC-AR risk score, composed of readily obtainable clinical information, could serve as a robust predictive tool for unplanned readmissions related to wound complications in the preoperative setting of open spine surgery.
手术部位并发症(SSC)会导致医疗成本增加。预测分析有助于识别高危患者并实施优化策略。本研究旨在开发并验证一种针对接受开放性脊柱手术患者的SSC相关再入院(SSC-AR)风险评估评分。
使用2019年1月至2020年9月期间成人患者的Premier医疗数据库(PHD)(n=157,664;3182例SSC-AR)进行回顾性数据分析,采用混合效应逻辑回归模型创建SSC风险评分。使用与患者、医疗机构或手术相关的预测因素开发完整模型和简化模型。完整模型使用37个预测因素,简化模型使用19个。
与完整模型(C统计量=74.56%;Pearson卡方/自由度[DF]=0.92)相比,简化模型表现出中等的区分能力(C统计量=74.12%),且模型拟合度更好(Pearson卡方/DF=0.93)。基于简化模型的风险评分系统包括以下因素:女性(1分)、血液疾病[2]、充血性心力衰竭[2]、痴呆[3]、慢性肺病[2]、风湿性疾病[3]、高血压[2]、肥胖[2]、严重合并症[2]、尼古丁依赖[1]、肝病[2]、截瘫和偏瘫[3]、外周血管疾病[2]、肾病[2]、癌症[1]、糖尿病[2]、翻修手术[2]、手术时间≥5小时[4]、急诊/紧急手术[2]。使用1000例手术的随机留出样本对最终风险评分(每次手术得分总和;范围为0-40)进行验证(C统计量=85.16%)。
由此产生的SSC-AR风险评分由易于获得的临床信息组成,可作为开放性脊柱手术术前与伤口并发症相关的非计划再入院的有力预测工具。