From the Department of Orthopaedic Surgery and Rehabilitation, Loyola University Medical Center, Maywood, IL (Dr. Hecht), the Quality and Safety Department, University of California, Davis, Sacramento, CA (Ms. Slee), the Department of Orthopaedic Surgery, UCSF-Fresno, Fresno, CA (Dr. Goodell), the Clinical and Translational Science Center, University of California, Davis (Dr. Taylor), and the Department of Orthopedics, University of California, Davis, Sacramento, CA (Dr. Wolinsky).
J Am Acad Orthop Surg. 2019 Mar 15;27(6):e293-e300. doi: 10.5435/JAAOS-D-17-00447.
Averaging length of stay (LOS) ignores patient complexity and is a poor metric for quality control in geriatric hip fracture programs. We developed a predictive model of LOS that compares patient complexity to the logistic effects of our institution's hip fracture care pathway.
A retrospective analysis was performed on patients enrolled into a hip fracture co-management pathway at an academic level I trauma center from 2014 to 2015. Patient complexity was approximated using the Charlson Comorbidity Index and ASA score. A predictive model of LOS was developed from patient-specific and system-specific variables using a multivariate linear regression analysis; it was tested against a sample of patients from 2016.
LOS averaged 5.95 days. Avoidance of delirium and reduced time to surgery were found to be notable predictors of reduced LOS. The Charlson Comorbidity Index was not a strong predictor of LOS, but the ASA score was. Our predictive LOS model worked well for 63% of patients from the 2016 group; for those it did not work well for, 80% had postoperative complications.
Predictive LOS modeling accounting for patient complexity was effective for identifying (1) reasons for outliers to the expected LOS and (2) effective measures to target for improving our hip fracture program.
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平均住院日（LOS）忽略了患者的复杂性，并且是老年髋部骨折计划质量控制的一个糟糕指标。我们开发了一种 LOS 的预测模型，将患者的复杂性与我们机构的髋部骨折护理途径的逻辑效果进行比较。
对 2014 年至 2015 年在一家学术一级创伤中心参与髋部骨折共同管理途径的患者进行了回顾性分析。使用 Charlson 合并症指数和 ASA 评分来近似患者的复杂性。使用多元线性回归分析从患者特定和系统特定变量开发 LOS 的预测模型；并对 2016 年的患者样本进行了测试。
LOS 平均为 5.95 天。避免谵妄和缩短手术时间被发现是降低 LOS 的显著预测因素。Charlson 合并症指数不是 LOS 的强预测因素，但 ASA 评分是。我们的预测 LOS 模型对 2016 年组的 63%的患者效果良好；对于那些效果不佳的患者，80%有术后并发症。
考虑患者复杂性的预测 LOS 建模对于识别（1）异常 LOS 的原因和（2）改善我们的髋部骨折计划的有效措施是有效的。
III。
