Chona Deepak, Lakomkin Nikita, Bulka Catherine, Mousavi Idine, Kothari Parth, Dodd Ashley C, Shen Michelle S, Obremskey William T, Sethi Manish K
Department of Orthopaedics, Vanderbilt Orthopaedic Institute Center for Health Policy, Vanderbilt University Medical Center, 1215 21st Avenue South, Suite 4200, Medical Center East, South Tower, Nashville, TN, 37232, USA.
Department of Biostatistics, Vanderbilt University, Nashville, TN, USA.
Int Orthop. 2017 May;41(5):859-868. doi: 10.1007/s00264-017-3425-2. Epub 2017 Feb 21.
Length of stay (LOS) is a major driver of cost and quality of care. A bundled payment system makes it essential for orthopaedic surgeons to understand factors that increase a patient's LOS. Yet, minimal data regarding predictors of LOS currently exist. Using the ACS-NSQIP database, this is the first study to identify risk factors for increased LOS for orthopaedic trauma patients and create a personalized LOS calculator.
All orthopaedic trauma surgery between 2006 and 2013 were identified from the ACS-NSQIP database using CPT codes. Patient demographics, pre-operative comorbidities, anatomic location of injury, and post-operative in-hospital complications were collected. To control for individual patient comorbidities, a negative binomial regression model evaluated hospital LOS after surgery. Betas (β), were determined for each pre-operative patient characteristic. We selected significant predictors of LOS (p < 0.05) using backwards stepwise elimination.
49,778 orthopaedic trauma patients were included in the analysis. Deep incisional surgical site infections and superficial surgical site infections were associated with the greatest percent change in predicted LOS (β = 1.2760 and 1.2473, respectively; p < 0.0001 for both). A post-operative LOS risk calculator was developed based on the formula: [Formula: see text].
Utilizing a large prospective cohort of orthopaedic trauma patients, we created the first personalized LOS calculator based on pre-operative comorbidities, post-operative complications and location of surgery. Future work may assess the use of this calculator and attempt to validate its utility as an accurate model. To improve the quality measures of hospitals, orthopaedists must employ such predictive tools to optimize care and better manage resources.
住院时间（LOS）是护理成本和质量的主要驱动因素。捆绑支付系统使骨科医生必须了解增加患者住院时间的因素。然而，目前关于住院时间预测因素的数据极少。利用美国外科医师学会国家外科质量改进计划（ACS-NSQIP）数据库，本研究首次识别骨科创伤患者住院时间增加的风险因素，并创建个性化住院时间计算器。
使用现行程序编码（CPT）从ACS-NSQIP数据库中识别2006年至2013年期间所有的骨科创伤手术。收集患者人口统计学信息、术前合并症、损伤解剖位置和术后院内并发症。为控制个体患者合并症，采用负二项回归模型评估术后住院时间。确定每个术前患者特征的β系数。我们使用向后逐步排除法选择住院时间的显著预测因素（p < 0.05）。
49778例骨科创伤患者纳入分析。深部手术切口感染和浅表手术切口感染与预测住院时间的最大百分比变化相关（β分别为1.2760和1.2473；两者p < 0.0001）。基于以下公式开发了术后住院时间风险计算器：[公式：见正文]。
利用大量骨科创伤患者的前瞻性队列，我们基于术前合并症、术后并发症和手术位置创建了首个个性化住院时间计算器。未来的工作可能会评估该计算器的使用情况，并尝试验证其作为准确模型的效用。为提高医院的质量指标，骨科医生必须采用此类预测工具来优化护理并更好地管理资源。
