Keck School of Medicine, University of Southern California, Los Angeles.
Department of Medical Engineering, California Institute of Technology, Pasadena.
Clin Spine Surg. 2023 Dec 1;36(10):E536-E544. doi: 10.1097/BSD.0000000000001512. Epub 2023 Aug 22.
A retrospective cohort.
We utilize big data and modeling techniques to create optimized comorbidity indices for predicting postoperative outcomes following cervical spine fusion surgery.
Cervical spine decompression and fusion surgery are commonly used to treat degenerative cervical spine pathologies. However, there is a paucity of high-quality data defining the optimal comorbidity indices specifically in patients undergoing cervical spine fusion surgery.
Using data from 2016 to 2019, we queried the Nationwide Readmissions Database (NRD) to identify individuals who had received cervical spine fusion surgery. The Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining indicator was used to assess frailty. To measure the level of comorbidity, Elixhauser Comorbidity Index (ECI) scores were queried. Receiver operating characteristic curves were developed utilizing comorbidity indices as predictor variables for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 1-year readmission.
A total of 453,717 patients were eligible. Nonroutine discharges occurred in 93,961 (20.7%) patients. The mean adjusted all-payer cost for the procedure was $22,573.14±18,274.86 (top quartile: $26,775.80) and the mean length of stay was 2.7±4.4 days (top quartile: 4.7 d). There were 703 (0.15%) mortalities and 58,254 (12.8%) readmissions within 1 year postoperatively. Models using frailty+ECI as primary predictors consistently outperformed the ECI-only model with statistically significant P -values for most of the complications assessed. Cost and mortality were the only outcomes for which this was not the case, as frailty outperformed both ECI and frailty+ECI in cost ( P <0.0001 for all) and frailty+ECI performed as well as ECI alone in mortality ( P =0.10).
Our data suggest that frailty+ECI may most accurately predict clinical outcomes in patients receiving cervical spine fusion surgery. These models may be used to identify high-risk populations and patients who may necessitate greater resource utilization following elective cervical spinal fusion.
回顾性队列研究。
我们利用大数据和建模技术，为颈椎融合术后的术后结果创建优化的合并症指数。
颈椎减压和融合术常用于治疗退行性颈椎病变。然而，对于接受颈椎融合术的患者，缺乏高质量的数据来定义最佳的合并症指数。
使用 2016 年至 2019 年的数据，我们从全国再入院数据库（NRD）中查询接受颈椎融合术的个体。使用约翰霍普金斯调整临床组（JHACG）脆弱性定义指标来评估脆弱性。为了衡量合并症的程度，查询了 Elixhauser 合并症指数（ECI）评分。使用合并症指数作为预测变量，开发了接受死亡率、非例行出院、四分位数成本、四分位数住院时间和 1 年再入院等相关并发症的接收者操作特征曲线。
共有 453717 名患者符合条件。非例行出院发生在 93961 名（20.7%）患者中。该手术的平均调整后所有支付者成本为 22573.14±18274.86 美元（四分位数：26775.80 美元），平均住院时间为 2.7±4.4 天（四分位数：4.7 天）。术后 1 年内有 703 例（0.15%）死亡，58254 例（12.8%）再入院。使用脆弱性+ECI 作为主要预测因子的模型在评估的大多数并发症方面始终优于仅使用 ECI 的模型，并且 P 值具有统计学意义。成本和死亡率是唯一不适用的情况，因为脆弱性在成本方面优于 ECI 和脆弱性+ECI（所有 P<0.0001），而脆弱性+ECI 在死亡率方面与 ECI 一样好（ P=0.10）。
我们的数据表明，脆弱性+ECI 可能最能预测接受颈椎融合术的患者的临床结果。这些模型可用于识别高危人群和可能需要在选择性颈椎融合后更多资源利用的患者。
