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

Published: April 18, 2014
Category: Bibliography > Papers
Authors: Antoniou T, Austin PC, Glazier RH, Kopp A, Ng R
Countries: Canada
Language: null
Types: Population Health
Settings: Hospital, PCP

Ann Epidemiol 24:532-537.

St. Michael’s Hospital, Toronto, ON, Canada; University of Toronto, Toronto, ON, Canada; Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

PURPOSE: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

PMID: 24837611

Canada,Medical Conditions,High-Impact Chronic Conditions, Co-morbidity,Outcome Measures,Adolescent,Adult,Aged,Canada/epidemiology,Cause of Death,Databases,Factual,Gender,HIV Infections/mortality,Logistic Models,Middle Aged,Ontario,ROC Curve,Residence Characteristics,Risk Assessment/methods,Time Factors,Young Adult

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