Development of frail RISC-HIV: a risk score for predicting frailty risk in the short-term for care of people with HIV. Ruderman, S. A., Nance, R. M., Drumright, L. N., Whitney, B. M., Hahn, A. W., Ma, J., Haidar, L., Eltonsy, S., Mayer, K. H., Eron, J. J., Greene, M., Mathews, W. C., Webel, A., Saag, M. S., Willig, A. L., Kamen, C., Mccaul, M., Chander, G., Cachay, E., Lober, W. B., Pandya, C., Cartujano-barrera, F., Kritchevsky, S. B., Austad, S. N., Landay, A., Kitahata, M. M., Crane, H. M., & Delaney, J. A. C. AIDS, February, 2023.
Development of frail RISC-HIV: a risk score for predicting frailty risk in the short-term for care of people with HIV [link]Paper  doi  abstract   bibtex   
Objective:  Frailty is common among people with HIV (PWH), so we developed Frail 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 7 HIV clinics across the US. 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 5-fold-cross-validation Lasso regression selected predictors of frailty. Predictors were selected by BMA if they had \textgreater45% 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 3,170 PWH (training set), 7% developed frailty, while among 1,510 PWH (validation set), 12% developed frailty. BMA and Lasso selected baseline frailty score, prescribed antidepressants, prescribed ART, depressive symptomology, and current marijuana and illicit opioid use. Discrimination was acceptable in the validation set, with Harrell's C of 0.76 (95%CI: 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.
@article{ruderman_development_2023,
	title = {Development of frail {RISC}-{HIV}: a risk score for predicting frailty risk in the short-term for care of people with {HIV}},
	issn = {0269-9370},
	shorttitle = {Development of frail {RISC}-{HIV}},
	url = {https://journals.lww.com/aidsonline/Abstract/9900/Development_of_frail_RISC_HIV__a_risk_score_for.200.aspx},
	doi = {10.1097/QAD.0000000000003501},
	abstract = {Objective: 
        Frailty is common among people with HIV (PWH), so we developed Frail 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 7 HIV clinics across the US. 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 5-fold-cross-validation Lasso regression selected predictors of frailty. Predictors were selected by BMA if they had {\textgreater}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 3,170 PWH (training set), 7\% developed frailty, while among 1,510 PWH (validation set), 12\% developed frailty. BMA and Lasso selected baseline frailty score, prescribed antidepressants, prescribed ART, depressive symptomology, and current marijuana and illicit opioid use. Discrimination was acceptable in the validation set, with Harrell's C of 0.76 (95\%CI: 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.},
	language = {en-US},
	urldate = {2023-03-15},
	journal = {AIDS},
	author = {Ruderman, Stephanie A. and Nance, Robin M. and Drumright, Lydia N. and Whitney, Bridget M. and Hahn, Andrew W. and Ma, Jimmy and Haidar, Lara and Eltonsy, Sherif and Mayer, Kenneth H. and Eron, Joseph J. and Greene, Meredith and Mathews, William C. and Webel, Allison and Saag, Michael S. and Willig, Amanda L. and Kamen, Charles and Mccaul, Mary and Chander, Geetanjali and Cachay, Edward and Lober, William B. and Pandya, Chintan and Cartujano-barrera, Francisco and Kritchevsky, Stephen B. and Austad, Steven N. and Landay, Alan and Kitahata, Mari M. and Crane, Heidi M. and Delaney, Joseph A. C.},
	month = feb,
	year = {2023},
	pages = {10.1097/QAD.0000000000003501},
}
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