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Abstract Number: 2114

Development of a Fracture Prediction Model for Patients Aging with HIV

Evelyn Hsieh1, Terrence E. Murphy2, Baylah Tessier-Sherman3, Linda Leo-Summers3, Farah Kidwai-Khan4, Samah Fodeh5, Rixin Wang6, Barbara Gulanski3, Michael T. Yin7, Melissa Skanderson8, Cynthia Brandt9, Amy C. Justice4 and Julie Womack10, 1Section of Rheumatology, Allergy and Immunology, Yale School of Medicine / VA Connecticut Healthcare System, New Haven, CT, 2Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 3Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 4Department of Internal Medicine, Yale School of Medicine / VA Connecticut Healthcare System, New Haven, CT, 5Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, 6Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT, 7Columbia University Irving Medical Center, New York, NY, 8VA Connecticut Healthcare System, West Haven, CT, 9Department of Biomedical Informatics & Data Science, Yale School of Medicine / VA Connecticut Healthcare System, New Haven, CT, 10Yale School of Nursing / VA Connecticut Healthcare System, Orange, CT

Meeting: ACR Convergence 2025

Keywords: Bioinformatics, Fracture, osteoporosis, risk assessment

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Session Information

Date: Tuesday, October 28, 2025

Title: (2106–2123) Osteoporosis & Metabolic Bone Disease – Basic & Clinical Science Poster II

Session Type: Poster Session C

Session Time: 10:30AM-12:30PM

Background/Purpose: Fragility fractures are a critical outcome for people ageing with HIV (PAH). Studies demonstrate higher prevalence, incidence, and earlier onset than for the general population. Current fragility fracture prediction models for the general population underestimate risk in PAH. To address this gap, we developed one- and three-year predictive models for risk of fragility fracture that better capture risk among PAH.

Methods: We used data from the Veterans Aging Cohort Study (VACS), a national electronic health records-based cohort that includes all individuals diagnosed with HIV receiving care within the Veterans Health Administration. We included all PAH (≥50 years of age) and followed them from 01/01/2009 through 12/31/2019. Observations consisted of one-year person-intervals and three-year person-intervals. Fragility fracture outcomes (at the spine, hip/femur, wrist/forearm, and shoulder/upper arm) were identified using validated ICD9 and ICD10 codes. From an initial pool of 27 candidate predictors (including HIV-specific variables) selected from the literature, clinical experience, and prior research, multivariable logistic regression chose 15 predictors that were subsequently reduced to a set of 10 based on statistical criteria and feedback from HIV providers. This feedback motivated two versions of each model that permit optional inclusion of race.

Results: The cohort included 32,842 individuals contributing a total of 223,607 person-years of data. Table 1 shows the distribution of the 15 short-listed candidate predictors in the first year of follow-up. Over the follow up period, there were 2,355 incident fractures. The ten final predictors, with odds ratios from the one year model, included: age OR = 1.02 (95%CI 1.01 – 1.02), albumin OR = 0.7 (0.64 – 0.76), alcohol use ≥3 drinks per day OR = 1.19 (1.11 – 1.27), BMI OR = 0.96 (0.95 – 0.97), chronic medication count OR = 1.05 (1.05 – 1.06), COPD OR = 1.36 (1.23 – 1.50), fall in previous year OR = 1.57 (1.35 – 1.82), female sex OR = 1.66 (1.33 – 2.08), fracture in previous year OR = 15.46 (13.45 – 17.77), and nonwhite race OR = 0.62 (0.57 – 0.68). The one-year and three-year models with race exhibited good discrimination, with areas under the curve (AUC) of 74.7% and 70.6%, respectively. For the models without race, the AUCs were 73.9% and 69.7% for the one- and three-year models. Figure 1 presents the ROC curves for all four models; all four exhibited excellent calibration.

Conclusion: Based upon rigorous statistical analysis and provider input, we present a new risk prediction model for fragility fracture that is statistically robust and uniquely optimized for the care of PAH. Future studies will externally validate this model using datasets from outside the VA.

Supporting image 1Table 1. Distribution of the 15 short-listed candidate predictors in the first year of follow-up within the study cohort.

Supporting image 2Figure 2. Receiver Operating Characteristic (ROC) Curves of Models for Risk of Fragility Fracture in One- (left) and Three-Year (right) Periods.


Disclosures: E. Hsieh: None; T. Murphy: None; B. Tessier-Sherman: None; L. Leo-Summers: None; F. Kidwai-Khan: None; S. Fodeh: None; R. Wang: None; B. Gulanski: None; M. Yin: None; M. Skanderson: None; C. Brandt: None; A. Justice: None; J. Womack: None.

To cite this abstract in AMA style:

Hsieh E, Murphy T, Tessier-Sherman B, Leo-Summers L, Kidwai-Khan F, Fodeh S, Wang R, Gulanski B, Yin M, Skanderson M, Brandt C, Justice A, Womack J. Development of a Fracture Prediction Model for Patients Aging with HIV [abstract]. Arthritis Rheumatol. 2025; 77 (suppl 9). https://acrabstracts.org/abstract/development-of-a-fracture-prediction-model-for-patients-aging-with-hiv/. Accessed .
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