BACKGROUND 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. OBJECTIVE To combine machine learning classification and feature importance analysis to identify drivers of readmission in a large cohort of spine patients. METHODS 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. RESULTS 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. CONCLUSION This strategy provides insight into global patterns of feature importance and better understanding of drivers of readmissions following spine surgery.

中文翻译：

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具有特征域的机器学习阐明了大型单中心患者队列中脊柱手术后重新入院的候选驱动因素

背景技术计划外再入院构成医疗保健中的重大成本负担。识别导致再入院风险的因素为采取可行的改变以降低再入院率提供了机会。目的 将机器学习分类和特征重要性分析相结合，以确定大量脊柱患者再入院的驱动因素。方法 对 2008 年至 2016 年间涉及退行性脊柱疾病的手术病例进行回顾性分析。在 11 150 例病例中，396 例患者（3.6%）在出院后 30 天内意外再次入院。收集了超过 75 个出院前变量，并将其分类为人口统计、围手术期和资源利用特征领域。随机森林分类用于构建从特征域重新入院的预测模型。使用特定于集成树的方法来按相对重要性对特征进行量化和排序。结果在人口统计学领域，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最重要的围手术期特征，而总直接成本和住院时间在资源利用领域是最重要的。在预测再入院的监督学习实验中，人口统计领域模型单独表现最好，这表明人口统计特征可能比脊柱手术后的围手术期变量对再入院风险的影响更大。仅使用显示出实质性重要性的丰富特征创建的预测模型与之前的模型相比，表现出更高的预测能力，并且接近最先进的深度学习模型的再入院性能。结论 该策略提供了对特征重要性的全局模式的洞察，并更好地了解脊柱手术后再入院的驱动因素。