Predicting Risk of Hospitalization or Death Among Patients Receiving Primary Care in the Veterans Health Administration
Predicting Risk of Hospitalization or Death Among Patients Receiving Primary Care in the Veterans Health Administration
复制标题
DOI：
10.1097/mlr.0b013e31827da95a
复制
发表时间：
2013-04-01
期刊：
影响因子：
3
通讯作者：
Fihn, Stephan D.
中科院分区：
文献类型：
--
作者：
Wang, Li;Porter, Brian;Fihn, Stephan D.
背景：识别死亡或住院风险升高的患者的统计模型侧重于人群亚组，如具有特定临床条件的患者或住院患者。大多数模型在临床应用上都有局限性。我们的目标是建立识别高危初级保健患者的模型。方法：使用退伍军人卫生署S企业数据仓库中的初级保健管理模块，我们识别了2010年10月1日登记并分配到退伍军人卫生署初级保健提供者的所有患者。结果变量是在随后的90天和1年内住院或死亡的发生情况。结果：在预测的95个危险百分位数中，住院、死亡、住院或死亡的90天事件发生率分别为19.6%、6.2%和22.6%，而总体平均值分别为2.7%、0.7%和3.4%；1年事件发生率分别为42.3%、19.4%和51.3%，而人口平均水平分别为8.2%、2.6%和10.8%。对于住院、死亡、住院或死亡，90天结果的C统计量分别为0.83、0.86和0.81；对于1年结果，C统计量分别为0.81、0.85和0.79。结论：使用电子临床数据的预测模型准确地识别了住院或死亡风险增加的患者。这些信息可以加强对临床情况复杂的患者的护理协调。
Background: Statistical models that identify patients at elevated risk of death or hospitalization have focused on population subsets, such as those with a specific clinical condition or hospitalized patients. Most models have limitations for clinical use. Our objective was to develop models that identified high-risk primary care patients.Methods: Using the Primary Care Management Module in the Veterans Health Administration (VHA)'s Corporate Data Warehouse, we identified all patients who were enrolled and assigned to a VHA primary care provider on October 1, 2010. The outcome variable was the occurrence of hospitalization or death during the subsequent 90 days and 1 year. We extracted predictors from 6 categories: sociodemographics, medical conditions, vital signs, prior year use of health services, medications, and laboratory tests and then constructed multinomial logistic regression models to predict outcomes for over 4.6 million patients.Results: In the predicted 95th risk percentiles, observed 90-day event rates were 19.6%, 6.2%, and 22.6%, respectively, for hospitalization, death, and either hospitalization or death, compared with population averages of 2.7%, 0.7%, and 3.4%, respectively; 1-year event rates were 42.3%, 19.4%, and 51.3%, respectively, compared with population averages of 8.2%, 2.6%, and 10.8%, respectively. The C-statistics for 90-day outcomes were 0.83, 0.86, and 0.81, respectively, for hospitalization, death, and either hospitalization or death and were 0.81, 0.85, and 0.79, respectively, for 1-year outcomes.Conclusions: Prediction models using electronic clinical data accurately identified patients with elevated risk for hospitalization or death. This information can enhance the coordination of care for patients with complex clinical conditions.