Development of Frail RISC-HIV: a Risk Score for Predicting Frailty Risk in the Short-term for Care of People with HIV

Author:

Ruderman Stephanie A.1,Nance Robin M.1,Drumright Lydia N.1,Whitney Bridget M.1,Hahn Andrew W.1,Ma Jimmy1,Haidar Lara2,Eltonsy Sherif2,Mayer Kenneth H.3,Eron Joseph J.4,Greene Meredith5,Mathews William C.6,Webel Allison1,Saag Michael S.7,Willig Amanda L.7,Kamen Charles8,McCaul Mary9,Chander Geetanjali19,Cachay Edward6,Lober William B.1,Pandya Chintan9,Cartujano-Barrera Francisco8,Kritchevsky Stephen B.10,Austad Steven N.7,Landay Alan11,Kitahata Mari M.1,Crane Heidi M.1,Delaney Joseph A.C.2

Affiliation:

1. University of Washington, Seattle, Washington, USA

2. University of Manitoba, Winnipeg, Manitoba, Canada

3. Harvard Medical School, Fenway Institute, Boston, Massachusetts

4. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina

5. University of California San Francisco, San Francisco

6. University of California San Diego, San Diego, California

7. University of Alabama at Birmingham, Birmingham, Alabama

8. University of Rochester, Rochester, New York

9. Johns Hopkins University, Baltimore, Maryland

10. Wake Forest University, Winston-Salem, North Carolina

11. Rush University, Chicago, Illinois, USA.

Abstract

Objective:Frailty is common among people with HIV (PWH), so we developed frail risk in the short-term for care (RISC)-HIV, a frailty prediction risk score for HIV clinical decision-making.Design:We followed PWH for up to 2 years to identify short-term predictors of becoming frail.Methods:We predicted frailty risk among PWH at seven HIV clinics across the United States. A modified self-reported Fried Phenotype captured frailty, including fatigue, weight loss, inactivity, and poor mobility. PWH without frailty were separated into training and validation sets and followed until becoming frail or 2 years. Bayesian Model Averaging (BMA) and five-fold-cross-validation Lasso regression selected predictors of frailty. Predictors were selected by BMA if they had a greater than 45% probability of being in the best model and by Lasso if they minimized mean squared error. We included age, sex, and variables selected by both BMA and Lasso in Frail RISC-HIV by associating incident frailty with each selected variable in Cox models. Frail RISC-HIV performance was assessed in the validation set by Harrell's C and lift plots.Results:Among 3170 PWH (training set), 7% developed frailty, whereas among 1510 PWH (validation set), 12% developed frailty. BMA and Lasso selected baseline frailty score, prescribed antidepressants, prescribed antiretroviral therapy, depressive symptomology, and current marijuana and illicit opioid use. Discrimination was acceptable in the validation set, with Harrell's C of 0.76 (95% confidence interval: 0.73–0.79) and sensitivity of 80% and specificity of 61% at a 5% frailty risk cutoff.Conclusions:Frail RISC-HIV is a simple, easily implemented tool to assist in classifying PWH at risk for frailty in clinics.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Infectious Diseases,Immunology,Immunology and Allergy

Reference61 articles.

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