Kung Justin E, Camacho Jael E, Bruckner Jacob, Ye Ivan B, Thomson Alexandra E, Cavanaugh Daniel, Koh Eugene Y, Gelb Daniel E, Sansur Charles, Ludwig Steven C
Department of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Department of Orthopaedics, University of Maryland School of Medicine, Baltimore, Maryland, USA
Int J Spine Surg. 2022 Jun;16(3):417-426. doi: 10.14444/8242.
Length of stay (LOS) is a meaningful outcome measure for more efficient and effective quality of care. However, algorithms to predict LOS have yet to be created for patients who undergo surgical management for traumatic spinal fractures.
The objectives of this study were to (1) identify preoperative, perioperative, and postoperative factors associated with increased LOS and (2) create predictive formulas to estimate LOS in thoracolumbar trauma patients who undergo surgical correction.
This is a retrospective case series of 196 patients operated for thoracolumbar spine trauma from January 2012 to December 2017 at a level 1 trauma and academic institution. Bivariate analysis between LOS and various preoperative, perioperative, and postoperative factors was conducted to identify significant associations. Multivariate analysis was conducted to create models capable of predicting LOS.
LOS was significantly associated with various preoperative (eg, Charlson Comorbidity Index, Glasgow Coma Scale [GCS], injury severity score), operative (eg, length of surgery, number of instrumented segments, surgical technique), and postoperative variables (eg, complications, discharge location). Multivariate analysis of preoperative variables identified 5 significant independent predictors that could predict LOS with strong correlation with observed LOS ( = 0.63). With all variables considered, multivariate analysis identified 8 variables (GCS, American Society of Anesthesiologists score, neurological status, polytrauma, packed red blood cell transfusion, number of unique postoperative complications, skin complications, and discharge facility) that could predict LOS with strong correlation ( = 0.80).
Various preoperative, perioperative, and postoperative factors are significantly associated with LOS in traumatic thoracolumbar spine patients. We developed models with good predictive capacity for LOS. If validated, these models should help in risk stratifying patients for increased LOS and consequently improve perioperative patient counseling.
This article contributes to identifying and predicting patients who are high risk for extended LOS after traumatic thoracolumbar injuries.
住院时间（LOS）是衡量医疗服务质量更高效和有效的一个有意义的结果指标。然而，对于接受创伤性脊柱骨折手术治疗的患者，尚未建立预测住院时间的算法。
本研究的目的是：（1）确定与住院时间延长相关的术前、围手术期和术后因素；（2）创建预测公式，以估计接受手术矫正的胸腰椎创伤患者的住院时间。
这是一项回顾性病例系列研究，研究对象为2012年1月至2017年12月在一家一级创伤和学术机构接受胸腰椎脊柱创伤手术的196例患者。对住院时间与各种术前、围手术期和术后因素进行双变量分析，以确定显著相关性。进行多变量分析以创建能够预测住院时间的模型。
住院时间与各种术前因素（如查尔森合并症指数、格拉斯哥昏迷量表[GCS]、损伤严重程度评分）、手术因素（如手术时间、固定节段数、手术技术）和术后变量（如并发症、出院地点）显著相关。术前变量的多变量分析确定了5个显著的独立预测因素，这些因素可预测住院时间，且与观察到的住院时间有很强的相关性（=0.63）。考虑所有变量后，多变量分析确定了8个变量（GCS、美国麻醉医师协会评分、神经状态、多发伤、浓缩红细胞输注、独特术后并发症数量、皮肤并发症和出院机构），这些变量可预测住院时间，且相关性很强（=0.80）。
各种术前、围手术期和术后因素与创伤性胸腰椎脊柱患者的住院时间显著相关。我们开发了对住院时间具有良好预测能力的模型。如果得到验证，这些模型应有助于对住院时间延长风险的患者进行风险分层，从而改善围手术期患者咨询。
本文有助于识别和预测创伤性胸腰椎损伤后住院时间延长的高危患者。
