Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Department of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Neurosurgery. 2022 Aug 1;91(2):322-330. doi: 10.1227/neu.0000000000001999. Epub 2022 Jun 17.
Extended postoperative hospital stays are associated with numerous clinical risks and increased economic cost. Accurate preoperative prediction of extended length of stay (LOS) can facilitate targeted interventions to mitigate clinical harm and resource utilization.
To develop a machine learning algorithm aimed at predicting extended LOS after cervical spine surgery on a national level and elucidate drivers of prediction.
Electronic medical records from a large, urban academic medical center were retrospectively examined to identify patients who underwent cervical spine fusion surgeries between 2008 and 2019 for machine learning algorithm development and in-sample validation. The National Inpatient Sample database was queried to identify cervical spine fusion surgeries between 2009 and 2017 for out-of-sample validation of algorithm performance. Gradient-boosted trees predicted LOS and efficacy was assessed using the area under the receiver operating characteristic curve (AUROC). Shapley values were calculated to characterize preoperative risk factors for extended LOS and explain algorithm predictions.
Gradient-boosted trees accurately predicted extended LOS across cohorts, achieving an AUROC of 0.87 (SD = 0.01) on the single-center validation set and an AUROC of 0.84 (SD = 0.00) on the nationwide National Inpatient Sample data set. Anterior approach only, elective admission status, age, and total number of Elixhauser comorbidities were important predictors that affected the likelihood of prolonged LOS.
Machine learning algorithms accurately predict extended LOS across single-center and national patient cohorts and characterize key preoperative drivers of increased LOS after cervical spine surgery.
术后住院时间延长与许多临床风险增加和经济成本增加有关。准确预测术后住院时间延长（LOS）可以促进有针对性的干预措施，以减轻临床危害和资源利用。
开发一种机器学习算法，旨在预测全国范围内颈椎手术后的 LOS 延长，并阐明预测的驱动因素。
回顾性检查了一家大型城市学术医疗中心的电子病历，以确定 2008 年至 2019 年期间接受颈椎融合手术的患者，用于机器学习算法的开发和样本内验证。查询了国家住院样本数据库，以确定 2009 年至 2017 年期间接受颈椎融合手术的患者，以验证算法性能的样本外验证。梯度提升树预测 LOS，并使用接收者操作特征曲线下的面积（AUROC）评估疗效。计算 Shapley 值来描述 LOS 延长的术前风险因素，并解释算法预测。
梯度提升树在两个队列中准确预测了 LOS 延长，在单中心验证集上的 AUROC 为 0.87（SD=0.01），在全国性的国家住院样本数据集上的 AUROC 为 0.84（SD=0.00）。仅前路、择期入院状态、年龄和 Elixhauser 合并症总数是影响 LOS 延长可能性的重要预测因素。
机器学习算法可以准确预测单中心和全国患者队列中的 LOS 延长，并描述颈椎手术后 LOS 延长的关键术前驱动因素。
