Lewis Katz School of Medicine at Temple University, Section of Endocrinology, Diabetes, and Metabolism, 3322 N. Broad ST., Ste 205, Philadelphia, PA 19140.
Division of Endocrinology, Diabetes, and Metabolism, Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University School of Medicine, 1830 E. Monument Street, Room 9052, Baltimore, MD 21287.
J Diabetes Complications. 2017 Aug;31(8):1332-1339. doi: 10.1016/j.jdiacomp.2017.04.021. Epub 2017 May 5.
To develop and validate a tool that predicts 30d readmission risk of patients with diabetes hospitalized for cardiovascular disease (CVD), the Diabetes Early Readmission Risk Indicator-CVD (DERRI-CVD™).
A cohort of 8189 discharges was retrospectively selected from electronic records of adult patients with diabetes hospitalized for CVD. Discharges of 60% of the patients (n=4950) were randomly selected as a training sample and the remaining 40% (n=3219) were the validation sample.
Statistically significant predictors of all-cause 30d readmission risk were identified by multivariable logistic regression modeling: education level, employment status, living within 5miles of the hospital, pre-admission diabetes therapy, macrovascular complications, admission serum creatinine and albumin levels, having a hospital discharge within 90days pre-admission, and a psychiatric diagnosis. Model discrimination and calibration were good (C-statistic 0.71). Performance in the validation sample was comparable. Predicted 30d readmission risk was similar in the training and validation samples (38.6% and 35.1% in the highest quintiles).
The DERRI-CVD™ may be a valid tool to predict all-cause 30d readmission risk of patients with diabetes hospitalized for CVD. Identifying high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs.
开发和验证一种预测因心血管疾病（CVD）住院的糖尿病患者 30 天再入院风险的工具，即糖尿病早期再入院风险指标 - CVD（DERRI-CVD™）。
从电子病历中回顾性选择 8189 名患有 CVD 的成年糖尿病住院患者的出院记录。将 60%的患者（n=4950）的出院记录随机抽取作为训练样本，其余 40%（n=3219）作为验证样本。
通过多变量逻辑回归建模确定了全因 30 天再入院风险的统计学显著预测因素：教育水平、就业状况、居住在医院 5 英里内、入院前糖尿病治疗、大血管并发症、入院血清肌酐和白蛋白水平、在入院前 90 天内出院以及精神科诊断。模型的区分度和校准度均较好（C 统计量为 0.71）。在验证样本中的表现相当。在训练和验证样本中，预测的 30 天再入院风险相似（最高五分位数分别为 38.6%和 35.1%）。
DERRI-CVD™可能是一种预测因 CVD 住院的糖尿病患者全因 30 天再入院风险的有效工具。识别高风险患者可能会鼓励使用针对风险最高患者的干预措施，从而有可能改善结局并降低医疗保健成本。
