Division of General Medicine and Primary Care, Brigham andWomen’s Hospital, Boston,MA 02120, USA.
JAMA Intern Med. 2013 Apr 22;173(8):632-8. doi: 10.1001/jamainternmed.2013.3023.
Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit.
To derive and internally validate a prediction model for potentially avoidable 30-day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge.
Retrospective cohort study.
Academic medical center in Boston, Massachusetts.
All patient discharges from any medical services between July 1, 2009, and June 30, 2010.
Potentially avoidable 30-day readmissions to 3 hospitals of the Partners HealthCare network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort.
Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30-day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: h emoglobin at discharge, discharge from an o ncology service, s odium level at discharge, p rocedure during the index admission, i ndex t ype of admission, number of a dmissions during the last 12 months, and l ength of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration.
This simple prediction model identifies before discharge the risk of potentially avoidable 30-day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.
由于减少医院再入院的有效干预措施通常实施成本高昂，因此预测潜在可避免再入院的评分可能有助于确定最有可能受益的患者。
使用出院前可获得的行政和临床数据，为医疗患者潜在可避免的 30 天医院再入院建立预测模型并进行内部验证。
回顾性队列研究。
马萨诸塞州波士顿的学术医疗中心。
2009 年 7 月 1 日至 2010 年 6 月 30 日期间从任何医疗服务中出院的所有患者。
使用基于行政数据的经过验证的计算机算法（SQLape）确定合作伙伴医疗保健网络的 3 家医院的潜在可避免的 30 天再入院。使用多变量逻辑回归开发了一个简单的评分，将样本的三分之二随机选择为推导队列，三分之一为验证队列。
在 10731 名合格出院患者中，有 2398 名患者（22.3%）在 30 天内再次入院，其中 879 名（所有出院患者的 8.5%）被确定为潜在可避免的。预测评分确定了 7 个独立因素，称为 HOSPITAL 评分：出院时的血红蛋白、肿瘤服务出院、出院时的钠水平、索引期间的手术、索引类型入院、过去 12 个月内的入院次数和住院时间。在验证集中，26.7%的患者被归类为高危人群，预计潜在可避免的再入院风险为 18.0%（观察值为 18.2%）。HOSPITAL 评分具有良好的判别能力（C 统计量为 0.71）和良好的校准度。
该简单预测模型可在出院前确定医疗患者潜在可避免的 30 天再入院风险。该评分具有识别可能需要更强化过渡护理干预的患者的潜力。
