Silverstein Marc D, Qin Huanying, Mercer S Quay, Fong Jaclyn, Haydar Ziad
Institute for Health Care Research and Improvement, Baylor Health Care System, Dallas, Texas, USA.
Proc (Bayl Univ Med Cent). 2008 Oct;21(4):363-72. doi: 10.1080/08998280.2008.11928429.
The objective of the study was to develop and validate predictors of 30-day hospital readmission using readily available administrative data and to compare prediction models that use alternate comorbidity classifications. A retrospective cohort study was designed; the models were developed in a two-thirds random sample and validated in the remaining one-third sample. The study cohort consisted of 29,292 adults aged 65 or older who were admitted from July 2002 to June 2004 to any of seven acute care hospitals in the Dallas-Fort Worth metropolitan area affiliated with the Baylor Health Care System. Demographic variables (age, sex, race), health system variables (insurance, discharge location, medical vs surgical service), comorbidity (classified by the Elixhauser classification or the High-Risk Diagnoses in the Elderly Scale), and geographic variables (distance from patient's residence to hospital and median income) were assessed by estimating relative risk and risk difference for 30-day readmission. Population-attributable risk was calculated. Results showed that age 75 or older, male sex, African American race, medical vs surgical service, Medicare with no other insurance, discharge to a skilled nursing facility, and specific comorbidities predicted 30-day readmission. Models with demographic, health system, and either comorbidity classification covariates performed similarly, with modest discrimination (C statistic, 0.65) and acceptable calibration (Hosmer-Lemeshow χ² = 6.08; P > 0.24). Models with demographic variables, health system variables, and number of comorbid conditions also performed adequately. Discharge to long-term care (relative risk, 1.94; 95% confidence interval, 1.80- 2.09) had the highest population-attributable risk of 30-day readmission (12.86%). A 25% threshold of predicted probability of 30-day readmission identified 4.1 % of patients ≥65 years old as priority patients for improved discharge planning. We conclude that elders with a high risk of 30-day hospital readmission can be identified early in their hospital course.
本研究的目的是利用现成的管理数据开发并验证30天再入院的预测指标,并比较使用替代共病分类的预测模型。设计了一项回顾性队列研究;模型在三分之二的随机样本中开发,并在其余三分之一的样本中进行验证。研究队列包括29292名65岁及以上的成年人,他们于2002年7月至2004年6月入住达拉斯-沃思堡都会区与贝勒医疗保健系统相关的七家急性护理医院中的任何一家。通过估计30天再入院的相对风险和风险差异,评估了人口统计学变量(年龄、性别、种族)、卫生系统变量(保险、出院地点、内科与外科服务)、共病(按Elixhauser分类或老年人高风险诊断量表分类)和地理变量(患者住所到医院的距离和收入中位数)。计算了人群归因风险。结果显示,75岁及以上、男性、非裔美国人种族、内科与外科服务、只有医疗保险而无其他保险、出院到熟练护理机构以及特定共病可预测30天再入院。包含人口统计学、卫生系统以及任何一种共病分类协变量的模型表现相似,具有适度的区分度(C统计量为0.65)和可接受的校准度(Hosmer-Lemeshow χ² = 6.08;P > 0.24)。包含人口统计学变量、卫生系统变量和共病数量的模型也表现良好。出院到长期护理机构(相对风险,1.94;95%置信区间,1.80 - 2.09)的30天再入院人群归因风险最高(12.86%)。30天再入院预测概率的25%阈值确定了4.1%的65岁及以上患者为改善出院计划的优先患者。我们得出结论,30天医院再入院高风险的老年人可在其住院过程早期被识别出来。