Roy Joanna M, Segura Aaron C, Rumalla Kranti, Skandalakis Georgios P, Covell Michael M, Bowers Christian A
Topiwala National Medical College, Mumbai, India.
Bowers Neurosurgical Frailty and Outcomes Data Science Lab, Sandy, UT, USA.
Neurospine. 2023 Dec;20(4):1337-1345. doi: 10.14245/ns.2346840.420. Epub 2023 Dec 31.
Although failure to rescue (FTR) has been utilized as a quality-improvement metric in several surgical specialties, its current utilization in spine surgery is limited. Our study aims to identify the patient characteristics that are independent predictors of FTR among thoracolumbar fusion (TLF) patients.
Patients who underwent TLF were identified using relevant diagnostic and procedural codes from the National Surgical Quality Improvement Program (NSQIP) database from 2011-2020. Frailty was assessed using the risk analysis index (RAI). FTR was defined as death, within 30 days, following a major complication. Univariate and multivariable analyses were used to compare baseline characteristics and early postoperative sequelae across FTR and non-FTR cohorts. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminatory accuracy of the frailty-driven predictive model for FTR.
The study cohort (N = 15,749) had a median age of 66 years (interquartile range, 15 years). Increasing frailty, as measured by the RAI, was associated with an increased likelihood of FTR: odds ratio (95% confidence interval [CI]) is RAI 21-25, 1.3 [0.8-2.2]; RAI 26-30, 4.0 [2.4-6.6]; RAI 31-35, 7.0 [3.8-12.7]; RAI 36-40, 10.0 [4.9-20.2]; RAI 41- 45, 21.5 [9.1-50.6]; RAI ≥ 46, 45.8 [14.8-141.5]. The frailty-driven predictive model for FTR demonstrated outstanding discriminatory accuracy (C-statistic = 0.92; CI, 0.89-0.95).
Baseline frailty, as stratified by type of postoperative complication, predicts FTR with outstanding discriminatory accuracy in TLF patients. This frailty-driven model may inform patients and clinicians of FTR risk following TLF and help guide postoperative care after a major complication.
尽管未能挽救(FTR)已在多个外科专业中用作质量改进指标,但其目前在脊柱手术中的应用有限。我们的研究旨在确定胸腰椎融合(TLF)患者中FTR的独立预测因素。
使用2011年至2020年国家外科质量改进计划(NSQIP)数据库中的相关诊断和程序代码识别接受TLF的患者。使用风险分析指数(RAI)评估虚弱程度。FTR定义为在出现重大并发症后的30天内死亡。单因素和多因素分析用于比较FTR和非FTR队列的基线特征和术后早期后遗症。采用受试者工作特征(ROC)曲线分析来评估虚弱驱动的FTR预测模型的判别准确性。
研究队列(N = 15,749)的中位年龄为66岁(四分位间距为15岁)。根据RAI测量,虚弱程度增加与FTR可能性增加相关:比值比(95%置信区间[CI])为RAI 21 - 25时,1.3[0.8 - 2.2];RAI 26 - 30时,4.0[2.4 - 6.6];RAI 31 - 35时,7.0[3.8 - 12.7];RAI 36 - 40时,10.0[4.9 - 20.2];RAI 41 - 45时,21.5[9.1 - 50.6];RAI≥46时,45.8[14.8 - 141.5]。虚弱驱动的FTR预测模型显示出出色的判别准确性(C统计量 = 0.92;CI,0.89 - 0.95)。
根据术后并发症类型分层的基线虚弱程度,在TLF患者中对FTR具有出色的判别准确性。这种基于虚弱程度的模型可以告知患者和临床医生TLF后FTR的风险,并有助于指导重大并发症后的术后护理。