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

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.

Authors

Institutions

Publication Details

Journal
UNC Libraries
Published
2025-09-24
DOI
https://doi.org/10.17615/w58s-xr07
Primary Topic
HIV-related health complications and treatments
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Alan Landay, Joseph A. Delaney, Joseph J. Eron, Kenneth Hugh Mayer et al.
UNC Libraries
HIV-related health complications and treatments
article

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

Alan Landay, Joseph A. Delaney, Joseph J. Eron, Kenneth Hugh Mayer, Steven N. Austad, Allison R. Webel, Edward R. Cachay, Lydia N. Drumright, Charles Stewart Kamen, Jimmy Ma, Geetanjali Chander, Amanda L. Willig, Meredith Greene, Heidi M. Crane, Francisco Cartujano‐Barrera, Andrew W. Hahn, William B. Lober, Michael S. Saag, William C. Mathews, Sherif Eltonsy, Stephanie A. Ruderman, Mari M. Kitahata, Robin M. Nance, Bridget M. Whitney, Mary E. McCaul, Stephen B. Kritchevsky, Chintan Pandya, Lara Haidar
article en

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.

UNC Libraries
University of North Carolina at Chapel Hill (US)
National Institutes of Health, Agency for Healthcare Research and Quality, National Institute on Alcohol Abuse and Alcoholism, National Institute on Aging, National Institute on Drug Abuse
Openalex Percentile: Top 57%
HIV-related health complications and treatments
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.