Comparison of comorbidity classification methods for predicting outcomes in a population-based cohort of adults with human immunodeficiency virus infection

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• 作者：Tony Antoniou ; PhD^a ; ^b ; ^c ; ^d ; ^{tantoniou@smh.ca" class="auth_mail} ; Ryan Ng ; MSc^d ; Richard H. Glazier ; MD ; MPH^a ; ^b ; ^c ; ^d ; ^e ; ^f ; ^g ; Alexander Kopp ; BA^d ; Peter C. Austin ; PhD^d ; ^f
• 关键词：Comorbidity ; Databases ; Factual ; Diagnosis-related groups ; Risk adjustment ; HIV ; Predictive value of tests ; ROC curve ; Logistic models
• 刊名：Annals of Epidemiology
• 出版年：July 2014
• 年：2014
• 卷：24
• 期：7
• 页码：532-537
• 全文大小：569 K

文摘

We compared the John's Hopkins' Aggregated Diagnosis Groups (ADGs), which are derived using inpatient and outpatient records, with the hospital record-derived Charlson and Elixhauser comorbidity indices for predicting outcomes in human immunodeficiency virus (HIV)-infected patients.

d="absSec_2">Methods

d="abspara0015">We used a validated algorithm to identify HIV-infected adults (n = 14,313) in Ontario, Canada, and randomly divided the sample into derivation and validation samples 100 times. The primary outcome was all-cause mortality within 1 year, and secondary outcomes included hospital admission and all-cause mortality within 1–2 years.

d="absSec_3">Results

d="abspara0020">The ADG, Elixhauser, and Charlson methods had comparable discriminative performance for predicting 1-year mortality, with median c-statistics of 0.785, 0.767, and 0.788, respectively, across the 100 validation samples. All methods had lower predictive accuracy for all-cause mortality within 1–2 years. For hospital admission, the ADG method had greater discriminative performance than either the Elixhauser or Charlson methods, with median c-statistics of 0.727, 0.678, and 0.668, respectively. All models displayed poor calibration for each outcome.

d="absSec_4">Conclusions

d="abspara0025">In patients with HIV, the ADG, Charlson, and Elixhauser methods are comparable for predicting 1-year mortality. However, poor calibration limits the use of these methods for provider profiling and clinical application.