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.
World Neurosurg. 2022 Sep;165:e83-e91. doi: 10.1016/j.wneu.2022.05.105. Epub 2022 May 30.
Delays in postoperative referrals to rehabilitation or skilled nursing facilities contribute toward extended hospital stays. Facilitating more efficient referrals through accurate preoperative prediction algorithms has the potential to reduce unnecessary economic burden and minimize risk of hospital-acquired complications. We develop a robust machine learning algorithm to predict non-home discharge after thoracolumbar spine surgery that generalizes to unseen populations and identifies markers for prediction.
Retrospective electronic health records were obtained from our single-center data warehouse (SCDW) to identify patients undergoing thoracolumbar spine surgeries between 2008 and 2019 for algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify thoracolumbar surgeries between 2009 and 2017 for out-of-sample validation. Ensemble decision trees were constructed for prediction and area under the receiver operating characteristic curve (AUROC) was used to assess performance. Shapley additive explanations values were derived to identify drivers of non-home discharge for interpretation of algorithm predictions.
A total of 5224 cases of thoracolumbar spine surgeries were isolated from the SCDW and 492,312 cases were identified from NIS. The model achieved an AUROC of 0.81 (standard deviation [SD] = 0.01) on the SCDW test set and 0.77 (SD = 0.01) on the nationwide NIS data set, thereby demonstrating robust prediction of non-home discharge across all diverse patient cohorts. Age, total Elixhauser comorbidities, Medicare insurance, weighted Elixhauser score, and female sex were among the most important predictors of non-home discharge.
Machine learning algorithms reliably predict non-home discharge after thoracolumbar spine surgery across single-center and national cohorts and identify preoperative features of importance that elucidate algorithm decision-making.
术后转介至康复或熟练护理机构的延迟导致住院时间延长。通过准确的术前预测算法来促进更有效的转介，有可能减轻不必要的经济负担，并最大限度地降低医院获得性并发症的风险。我们开发了一种强大的机器学习算法，用于预测胸腰椎手术后非家庭出院，该算法可推广到未见人群，并确定预测标记。
从我们的单中心数据仓库（SCDW）中获取回顾性电子健康记录，以确定 2008 年至 2019 年期间接受胸腰椎手术的患者，用于算法开发和内部验证。查询国家住院患者样本（NIS）数据库，以确定 2009 年至 2017 年期间的胸腰椎手术，用于样本外验证。构建了用于预测的集成决策树，并使用接收者操作特征曲线下的面积（AUROC）来评估性能。导出 Shapley 加法解释值，以确定非家庭出院的驱动因素，用于解释算法预测。
从 SCDW 中分离出 5224 例胸腰椎手术病例，从 NIS 中确定了 492312 例病例。该模型在 SCDW 测试集上的 AUROC 为 0.81（标准差[SD] = 0.01），在全国性的 NIS 数据集上的 AUROC 为 0.77（SD = 0.01），从而证明了对所有不同患者群体的非家庭出院的可靠预测。年龄、总 Elixhauser 合并症、医疗保险、加权 Elixhauser 评分和女性是预测非家庭出院的最重要预测因素之一。
机器学习算法可可靠地预测胸腰椎手术后非家庭出院，涵盖单中心和全国队列，并确定术前重要特征，阐明算法决策。
