Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN.
Robert D. and Patricia E. Kern Center for Science of Health Care Delivery, Mayo Clinic, Rochester, MN.
J Am Med Dir Assoc. 2019 Apr;20(4):444-450.e2. doi: 10.1016/j.jamda.2019.01.137. Epub 2019 Mar 7.
Patients discharged to a skilled nursing facility (SNF) for post-acute care have a high risk of hospital readmission. We aimed to develop and validate a risk-prediction model to prospectively quantify the risk of 30-day hospital readmission at the time of discharge to a SNF.
Retrospective cohort study.
Ten independent SNFs affiliated with the post-acute care practice of an integrated health care delivery system.
We evaluated 6032 patients who were discharged to SNFs for post-acute care after hospitalization.
The primary outcome was all-cause 30-day hospital readmission. Patient demographics, medical comorbidity, prior use of health care, and clinical parameters during the index hospitalization were analyzed by using gradient boosting machine multivariable analysis to build a predictive model for 30-day hospital readmission. Area under the receiver operating characteristic curve (AUC) was assessed on out-of-sample observations under 10-fold cross-validation.
Among 8616 discharges to SNFs from January 1, 2009, through June 30, 2014, a total of 1568 (18.2%) were readmitted to the hospital within 30 days. The 30-day hospital readmission prediction model had an AUC of 0.69, a 16% improvement over risk assessment using the Charlson Comorbidity Index alone. The final model included length of stay, abnormal laboratory parameters, and need for intensive care during the index hospitalization; comorbid status; and number of emergency department and hospital visits within the preceding 6 months.
We developed and validated a risk-prediction model for 30-day hospital readmission in patients discharged to a SNF for post-acute care. This prediction tool can be used to risk stratify the complex population of hospitalized patients who are discharged to SNFs to prioritize interventions and potentially improve the quality, safety, and cost-effectiveness of care.
入住康复护理机构（SNF）进行康复治疗的患者存在很高的再入院风险。我们旨在开发并验证一个风险预测模型，以便在 SNF 出院时前瞻性地量化 30 天内再入院的风险。
回顾性队列研究。
隶属于综合医疗服务系统康复后护理的 10 个独立 SNF。
我们评估了 6032 名因住院而入住 SNF 进行康复治疗的患者。
主要结局为全因 30 天内再入院。通过梯度提升机多变量分析，对患者人口统计学特征、合并症、既往使用医疗服务情况以及住院期间的临床参数进行分析，构建 30 天内再入院的预测模型。在 10 折交叉验证的外部样本观察中评估接受者操作特征曲线下面积（AUC）。
2009 年 1 月 1 日至 2014 年 6 月 30 日，在 8616 次 SNF 出院中，共有 1568 次（18.2%）在 30 天内再次入院。30 天内再入院预测模型的 AUC 为 0.69，与单独使用 Charlson 合并症指数相比，风险评估提高了 16%。最终模型包括住院时间、异常实验室参数以及住院期间需要重症监护；合并症状况；以及在过去 6 个月内的急诊和住院就诊次数。
我们开发并验证了一个用于预测 SNF 出院后康复治疗患者 30 天内再入院风险的模型。该预测工具可用于对出院到 SNF 的住院患者进行风险分层，以便优先进行干预，并有可能改善护理的质量、安全性和成本效益。
