Developing an HIV-Specific Falls Risk Predictive Model with a Novel Clinical Index: A Systematic Review and Meta-Analysis

To identify, evaluate and stratify the physiological, behavioral, and HIV-specific factors associated with fall risk among people living with HIV, using a clinically relevant index to contribute to the development of a multivariate predictive model and tailored falls prevention strategies.

Systematic review and meta-analysis were conducted to explore predictors of falls in people living with HIV. Data was sourced, screened, extracted, and analyzed by two independent reviewers from eight databases up to January 2nd, 2024, following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Evidence quality and bias were assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and the Mixed Method Appraisal Tool (MMAT), respectively. Pooled odds ratios (OR) with 95% confidence intervals (CI) were computed using random-effects models to establish associations between predictors and falls risk. We applied established criteria (Bradford Hill’s criteria, Rothman’s and Nweke’s viewpoints) to stratify risk factors and create a weighted predictive algorithm.

This review included 12 studies on falls/balance dysfunction in 117,638 participants (54,513 people living with HIV), with varying ages (45-50 years), sample sizes (32-26,373), study durations (6 months to 15 years), disease stages (CD4+ counts 347.2 cells/mm³ to ≥500 cells/µL) and fall definitions (self-reported histories to real-time reporting). Some predictors of falls in people living with HIV including depression, cannabis use, cognitive impairment/neurocognitive adverse effects (NCAE), hypertension, and stavudine—showed perfect risk responsiveness (Ri=1), indicating their strong association with falls. Notably, cannabis use demonstrated the highest risk weight (Rw=3.0, p0.05, 95%CI:1.51-5.82), followed by NCAE (Rw=2.3, p0.05, 95%CI:1.66-3.21) and frailty with a broad confidence interval (Rw=2.2, p0.05, 95%CI:0.73-14.40). Other significant predictors included hypertension (Rw=1.8, p0.05, 95%CI:1.33-2.33), depression (Rw=1.6, p0.05, 95%CI:1.22-2.18), stavudine use (Rw=1.5, p0.05, 95%CI: 0.95-2.25), neuropathy (Rw=1.3, p0.05, 95%CI:1.26-2.11), and polypharmacy (Rw=1.2, p0.05, 95%CI:1.16-1.96). The fall risk threshold score was 12.8, representing the 76th percentile of the specific and sufficient risk weight.

Our meta-analysis identifies predictors of falls in people living with HIV, emphasizing physiological, behavioral, and HIV-specific factors. Integrating these into clinical practice could mitigate falls-related sequelae. We propose a novel approach to falls prevention/management employing our HIV-Specific fall risk assessment model in this population.

This study emphasizes the need for effective fall management in people living with HIV by integrating specific risk factors—such as cannabis use, cognitive impairment, and frailty—into assessment tools and employing a multidisciplinary approach that includes regular medication reviews, depression screenings, and targeted frailty management. Additionally, it identifies critical areas for future research, such as validating the predictive model across diverse populations and exploring the relationships between HIV-specific factors and falls, providing a framework for tailored interventions to enhance outcomes and reduce falls risk.