Kuo Cathleen C, Hess Ryan M, Soliman Mohamed A R, Khan Asham, Pollina John, Mullin Jeffrey P
Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, 100 High Street, Suite B4, Buffalo, NY, 14203, USA.
Acta Neurochir (Wien). 2022 Oct;164(10):2655-2665. doi: 10.1007/s00701-022-05334-3. Epub 2022 Aug 4.
With growing emphasis on high-value care, many institutions have been working on improving surgical efficiency, quality, and complication reduction. Unfortunately, data are limited regarding perioperative factors that may influence length of stay (LOS) following transforaminal lumbar interbody fusion (TLIF). We sought to design a predictive algorithm that determined patients at risk of prolonged LOS after TLIF. The goal was to identify patients who would benefit from preoperative intervention aimed to reduce LOS.
We conducted a review of perioperative data for patients who underwent TLIF between 2014 and 2019. Univariate and multivariate stepwise regression models were used to analyze risk factor effects on postoperative LOS.
Two hundred and sixty-nine patients were identified (57.2% women). Mean age at surgery was 61.7 ± 12.3 years. Mean postoperative LOS was 3.08 ± 1.54 days. In multivariate analysis, American Society of Anesthesiologists class (odds ratio [OR] = 1.441, 95% confidence interval [CI] 1.321-1.571), preoperative functional status (OR = 1.237, 95% CI 1.122-1.364), Oswestry Disability Index (OR = 1.010, 95% CI 1.004-1.016), and estimated blood loss (OR = 1.050, 95% CI 1.003-1.101) were independent risk factors for postoperative LOS ≥ 5 days. The final model had an area under the curve of 0.948 with good discrimination and was implemented in the form of an online calculator ( https://spine.shinyapps.io/TLIF_LOS/ ).
The prediction tool derived can be useful for assessing likelihood of prolonged LOS in patients undergoing TLIF. With external validation, this calculator may ultimately assist healthcare providers in identifying patients at risk for prolonged hospitalization so preoperative interventions can be undertaken to reduce LOS, thus reducing resource utilization.
随着对高价值医疗的日益重视，许多机构一直在致力于提高手术效率、质量并减少并发症。遗憾的是，关于可能影响经椎间孔腰椎椎间融合术（TLIF）后住院时间（LOS）的围手术期因素的数据有限。我们试图设计一种预测算法，以确定TLIF后有住院时间延长风险的患者。目标是识别那些将从旨在缩短住院时间的术前干预中受益的患者。
我们对2014年至2019年间接受TLIF的患者的围手术期数据进行了回顾。使用单变量和多变量逐步回归模型分析危险因素对术后住院时间的影响。
共纳入269例患者（57.2%为女性）。手术时的平均年龄为61.7±12.3岁。术后平均住院时间为3.08±1.54天。在多变量分析中，美国麻醉医师协会分级（比值比[OR]=1.441，95%置信区间[CI]1.321 - 1.571）、术前功能状态（OR=1.237，95%CI 1.122 - 1.364）、Oswestry功能障碍指数（OR=1.010，95%CI 1.004 - 1.016）和估计失血量（OR=1.050，95%CI 1.003 - 1.101）是术后住院时间≥5天的独立危险因素。最终模型的曲线下面积为0.948，具有良好的区分度，并以在线计算器（https://spine.shinyapps.io/TLIF_LOS/ ）的形式实现。
所推导的预测工具可用于评估接受TLIF患者住院时间延长的可能性。经过外部验证后，该计算器最终可能有助于医疗保健提供者识别有住院时间延长风险的患者，以便能够采取术前干预措施来缩短住院时间，从而减少资源利用。
