Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York.
Department of Anesthesiology, Perioperative, and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
Neurosurgery. 2020 Sep 15;87(4):E500-E510. doi: 10.1093/neuros/nyaa136.
Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk presents opportunities for actionable change to reduce readmission rates.
To combine machine learning classification and feature importance analysis to identify drivers of readmission in a large cohort of spine patients.
Cases involving surgical procedures for degenerative spine conditions between 2008 and 2016 were retrospectively reviewed. Of 11 150 cases, 396 patients (3.6%) experienced an unplanned hospital readmission within 30 d of discharge. Over 75 pre-discharge variables were collected and categorized into demographic, perioperative, and resource utilization feature domains. Random forest classification was used to construct predictive models for readmission from feature domains. An ensemble tree-specific method was used to quantify and rank features by relative importance.
In the demographics domain, age and comorbidity burden were the most important features for readmission prediction. Surgical duration and intraoperative oral morphine equivalents were the most important perioperative features, whereas total direct cost and length of stay were most important in the resource utilization domain. In supervised learning experiments for predicting readmission, the demographic domain model performed the best alone, suggesting that demographic features may contribute more to readmission risk than perioperative variables following spine surgery. A predictive model, created using only enriched features showing substantial importance, demonstrated improved predictive capacity compared to previous models, and approached the performance of state-of-the-art, deep-learning models for readmission.
This strategy provides insight into global patterns of feature importance and better understanding of drivers of readmissions following spine surgery.
计划外的医院再入院给医疗保健带来了巨大的经济负担。确定导致再入院风险的因素为降低再入院率提供了可采取行动的改变机会。
结合机器学习分类和特征重要性分析，确定大型脊柱患者队列中导致再入院的驱动因素。
回顾性分析了 2008 年至 2016 年间手术治疗退行性脊柱疾病的病例。在 11150 例病例中，396 例（3.6%）在出院后 30 d 内出现计划外医院再入院。收集了超过 75 个出院前变量，并分为人口统计学、围手术期和资源利用特征域。随机森林分类用于从特征域构建再入院预测模型。使用集成树特定方法对特征进行量化和排序，以确定其相对重要性。
在人口统计学领域，年龄和合并症负担是再入院预测的最重要特征。手术持续时间和术中口服吗啡当量是围手术期最重要的特征，而总直接费用和住院时间在资源利用领域最重要。在预测再入院的有监督学习实验中，仅使用显示出显著重要性的丰富特征创建的人口统计学模型表现最佳，这表明与脊柱手术后的围手术期变量相比，人口统计学特征可能对再入院风险的贡献更大。与之前的模型相比，仅使用重要性显著的丰富特征创建的预测模型显示出了更好的预测能力，并且接近再入院的最先进深度学习模型的性能。
该策略提供了对特征重要性全局模式的深入了解，并更好地理解了脊柱手术后再入院的驱动因素。
