Department of Pharmacy Services, St. Mary Mercy Hospital, Livonia, MI, and Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI.
Department of Pharmacy Services, Detroit Medical Center, Detroit, MI, and Eugene Applebaum College of Pharmacy and Health Sciences, Wayne State University, Detroit, MI.
Am J Health Syst Pharm. 2019 Mar 19;76(7):436-443. doi: 10.1093/ajhp/zxy085.
The study derives and validates a 30-day hospital readmission risk index to predict a patient's likelihood of readmission, utilizing a health systems electronic medical record.
A retrospective data extraction and analysis was conducted using data from the electronic medical record to identify risks of 30-day all-cause hospital readmission on adult patients admitted to a large multi-site health system. Univariate and multivariable logistic regression was performed on a derivation cohort of hospital admissions (n = 40,668) and analyzed 91 variables associated with 30-day hospital readmission. A 10-variable risk prediction equation was generated and validated in a second patient cohort (n = 7,820). The prediction index's discriminative ability was determined using the c-statistic, and calibration of the prediction index was assessed with the use of the Hosmer-Lemeshow test.
The hospital all-cause thirty-day readmission index (HATRIX) identified 10 variables to be highly associated with 30-day readmission. The discriminative ability of the derived prediction equation was determined using the c-statistic and was calculated to be 0.73 (95% confidence interval [CI] 0.72-0.73) for the derivation cohort. The prediction equation was validated using a second cohort of patients and resulted with an area under the curve (AUC) of 0.72 (95% CI 0.70-0.73), indicating modest discrimination.
An original risk prediction index for 30-day hospital readmission was derived and validated using 2 cohorts of patients. Identifying patients who have an increased risk of 30-day hospital readmission with the use of the electronic medical record is an ideal method for targeting interventions and improving transitions-of-care to reduce hospital readmissions.
本研究利用医疗系统电子病历，推导并验证了一种 30 天住院再入院风险指数，以预测患者再入院的可能性。
通过电子病历中的数据进行回顾性数据提取和分析，确定了大型多地点医疗系统中成年患者 30 天内全因住院再入院的风险。对住院患者队列（n=40668）进行单变量和多变量逻辑回归分析，并分析了与 30 天内医院再入院相关的 91 个变量。生成并验证了一个由 10 个变量组成的风险预测方程，在第二个患者队列（n=7820）中进行分析。使用 C 统计量确定预测指数的判别能力，使用 Hosmer-Lemeshow 检验评估预测指数的校准情况。
医院全因 30 天再入院指数（HATRIX）确定了与 30 天再入院高度相关的 10 个变量。使用 C 统计量确定了推导预测方程的判别能力，其在推导队列中的计算值为 0.73（95%置信区间 [CI] 0.72-0.73）。使用第二个患者队列验证预测方程，得出曲线下面积（AUC）为 0.72（95%CI 0.70-0.73），表明适度的判别能力。
使用两个患者队列推导并验证了一种新的 30 天住院再入院风险预测指数。使用电子病历识别具有 30 天内住院再入院风险增加的患者，是针对干预措施和改善过渡护理以降低住院再入院率的理想方法。
