Yuhang Qian1, Richard D Moore2, Sally B Coburn1, Thibaut Davy-Mendez3,4, Kathleen M Akgün5,6, Kathleen A McGinnis7, Michael J Silverberg8, Jonathan A Colasanti9, Edward R Cachay10, Michael A Horberg11, Charles S Rabkin12, Jeffrey M Jacobson13, M John Gill14, Angel M Mayor15, Gregory D Kirk1,2, Kelly A Gebo1,2, Ank E Nijhawan16, Keri N Althoff1. 1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. 2. Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD. 3. Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC. 4. Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA. 5. Department of Internal Medicine and General Internal Medicine, VA Connecticut Healthcare System, West Haven, CT. 6. Department of Internal Medicine, Yale University School of Medicine, New Haven, CT. 7. VA CT Healthcare System, West Haven, CT. 8. Division of Research, Kaiser Permanente Northern California, Oakland, CA. 9. Department of Medicine, Emory University School of Medicine, Atlanta, GA. 10. Division of Infectious Diseases and Global Public Health, University of California at San Diego, San Diego, CA. 11. Kaiser Permanente Mid-Atlantic Permanente Research Institute, Rockville, MD. 12. Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD. 13. Division of Infectious Diseases, Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH. 14. Department of Medicine, University of Calgary, S Alberta HIV Clinic, Calgary, AB, Canada. 15. Department of Medicine, Universidad Central Del Caribe at Bayamón, Bayamón, Puerto Rico; and. 16. Division of Infectious Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX.
Abstract
BACKGROUND: People with HIV (PWH) have a higher hospitalization rate than the general population. The Veterans Aging Cohort Study (VACS) Index at study entry well predicts hospitalization in PWH, but it is unknown if the time-updated parameter improves hospitalization prediction. We assessed the association of parameterizations of the VACS Index 2.0 with the 5-year risk of hospitalization. SETTING: PWH ≥30 years old with at least 12 months of antiretroviral therapy (ART) use and contributing hospitalization data from 2000 to 2016 in North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were included. Three parameterizations of the VACS Index 2.0 were assessed and categorized by quartile: (1) "baseline" measurement at study entry; (2) time-updated measurements; and (3) cumulative scores calculated using the trapezoidal rule. METHODS: Discrete-time proportional hazard models estimated the crude and adjusted associations (and 95% confidence intervals [CIs]) of the VACS Index parameterizations and all-cause hospitalizations. The Akaike information criterion (AIC) assessed the model fit with each of the VACS Index parameters. RESULTS: Among 7289 patients, 1537 were hospitalized. Time-updated VACS Index fitted hospitalization best with a more distinct dose-response relationship [score <43: reference; score 43-55: aHR = 1.93 (95% CI: 1.66 to 2.23); score 55-68: aHR = 3.63 (95% CI: 3.12 to 4.23); score ≥68: aHR = 9.98 (95% CI: 8.52 to 11.69)] than study entry and cumulative VACS Index after adjusting for known risk factors. CONCLUSIONS: Time-updated VACS Index 2.0 had the strongest association with hospitalization and best fit to the data. Health care providers should consider using it when assessing hospitalization risk among PWH.
BACKGROUND: People with HIV (PWH) have a higher hospitalization rate than the general population. The Veterans Aging Cohort Study (VACS) Index at study entry well predicts hospitalization in PWH, but it is unknown if the time-updated parameter improves hospitalization prediction. We assessed the association of parameterizations of the VACS Index 2.0 with the 5-year risk of hospitalization. SETTING: PWH ≥30 years old with at least 12 months of antiretroviral therapy (ART) use and contributing hospitalization data from 2000 to 2016 in North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) were included. Three parameterizations of the VACS Index 2.0 were assessed and categorized by quartile: (1) "baseline" measurement at study entry; (2) time-updated measurements; and (3) cumulative scores calculated using the trapezoidal rule. METHODS: Discrete-time proportional hazard models estimated the crude and adjusted associations (and 95% confidence intervals [CIs]) of the VACS Index parameterizations and all-cause hospitalizations. The Akaike information criterion (AIC) assessed the model fit with each of the VACS Index parameters. RESULTS: Among 7289 patients, 1537 were hospitalized. Time-updated VACS Index fitted hospitalization best with a more distinct dose-response relationship [score <43: reference; score 43-55: aHR = 1.93 (95% CI: 1.66 to 2.23); score 55-68: aHR = 3.63 (95% CI: 3.12 to 4.23); score ≥68: aHR = 9.98 (95% CI: 8.52 to 11.69)] than study entry and cumulative VACS Index after adjusting for known risk factors. CONCLUSIONS: Time-updated VACS Index 2.0 had the strongest association with hospitalization and best fit to the data. Health care providers should consider using it when assessing hospitalization risk among PWH.
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