Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada
Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to Predict Mortality in a General Adult Population Cohort in Ontario, Canada
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DOI：
10.1097/mlr.0b013e318215d5e2
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发表时间：
2011-10-01
期刊：
影响因子：
3
通讯作者：
Anderson, Geoffrey M.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.;van Walraven, Carl;Anderson, Geoffrey M.
背景：行政卫生保健数据库越来越多地用于卫生服务和比较有效性研究。当比较不同治疗、干预或暴露之间的结局时，调整治疗组之间结局风险差异的能力很重要。同样，在进行医疗保健提供者分析时，需要进行充分的风险调整，以使有关提供者绩效的结论有效。有有限的有效的方法来调整风险在流动人口使用行政healthcare databases.Objectives目的：检查能力的约翰霍普金斯的聚集诊断组（ADGs）预测死亡率在一般流动人口cohol.Research设计：回顾性队列使用人口为基础的行政数据构建。加拿大安大略所有10，498，413名年龄在20岁至100岁之间的居民，他们在2007年生日时还活着。受试者被随机分为推导样本和验证样本。测量：受试者在2007年出生后1年内死亡。结果：由年龄、性别和指标变量组成的Logistic回归模型对32个ADG类别中的28个类别具有良好的区分度：在推导和验证样品中，C-统计量（相当于受试者工作特征曲线下的面积）均为0.917。此外，该模型显示出非常好的校准。相比之下，使用的Charlson合并症指数或Elixhauser合并症导致歧视的轻微下降相比，使用ADGs.Conclusions：Logistic回归模型使用年龄，性别和约翰霍普金斯ADGs能够准确预测1年的死亡率在一般门诊人群的主题。
Background: Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provider performance to be valid. There are limited validated methods for risk adjustment in ambulatory populations using administrative healthcare databases.Objectives: To examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict mortality in a general ambulatory population cohort.Research Design: Retrospective cohort constructed using population-based administrative data.Subjects: All 10,498,413 residents of Ontario, Canada between the ages of 20 and 100 years who were alive on their birthday in 2007. Subjects were randomly divided into derivation and validation samples.Measures: Death within 1 year of the subject's birthday in 2007.Results: A logistic regression model consisting of age, sex, and indicator variables for 28 of the 32 ADG categories had excellent discrimination: the c-statistic (equivalent to the area under the receiver operating characteristic curve) was 0.917 in both derivation and validation samples. Furthermore, the model showed very good calibration. In comparison, the use of the Charlson comorbidity index or the Elixhauser comorbidities resulted in a minor decrease in discrimination compared with the use of the ADGs.Conclusions: Logistic regression models using age, sex, and the John Hopkins ADGs were able to accurately predict 1-year mortality in a general ambulatory population of subjects.