Langella Francesco, Barile Francesca, Bellosta-Lòpez Pablo, Fusini Federico, Compagnone Domenico, Vanni Daniele, Damilano Marco, Berjano Pedro
IRCCS Ospedale Galeazzi-Sant'Ambrogio, Milan, Italy.
Universidad San Jorge, Campus Universitario, Villanueva de Gállego, Zaragoza, Spain.
Global Spine J. 2025 Apr 1:21925682251331451. doi: 10.1177/21925682251331451.
Study DesignRetrospective Cohort Study.ObjectivesTo develop and validate a multivariable predictive model for length of hospital stay (LOS) following spine surgery, incorporating sociodemographic characteristics, medical data, and self-reported patient outcomes.MethodsA retrospective analysis of 4583 patients from a spine surgery registry was conduct-ed. Predictors included age, sex, BMI, ASA score, surgical complexity, and patient-reported outcomes. Binary logistic regression was used to model LOS (<3 days vs ≥3 days).ResultsLower age, active work status, lower ASA scores, and specific surgical procedures were associated with shorter LOS. The model demonstrated good accuracy and dis-criminative ability.ConclusionsSociodemographic, medical, and patient-reported outcomes are valuable predictors of LOS. These findings can help improve preoperative planning and resource allocation in spine surgery.
研究设计
回顾性队列研究。
目的
建立并验证一个用于预测脊柱手术后住院时间（LOS）的多变量预测模型，该模型纳入社会人口统计学特征、医学数据和患者自我报告的结果。
方法
对来自脊柱手术登记处的4583例患者进行回顾性分析。预测因素包括年龄、性别、体重指数（BMI）、美国麻醉医师协会（ASA）评分、手术复杂性和患者报告的结果。采用二元逻辑回归对住院时间（<3天与≥3天）进行建模。
结果
年龄较小、在职状态、较低的ASA评分以及特定的手术方式与较短的住院时间相关。该模型显示出良好的准确性和判别能力。
结论
社会人口统计学、医学和患者报告的结果是住院时间的重要预测因素。这些发现有助于改善脊柱手术的术前规划和资源分配。
