HIV Care Has Extended Survival, but Cardiovascular Risk Has Not Kept Pace
The rise of effective antiretroviral therapy has changed HIV from a rapidly fatal infection into a chronic condition, but longer survival has exposed a new problem: cardiovascular disease is now a major driver of illness and death among people living with HIV in sub-Saharan Africa. In this PLOS One protocol, investigators set out to map the prediction tools already developed or used in the region, with the explicit goal of identifying what exists, what performs poorly, and where the gaps are large enough to justify a new model.
That need is not abstract. HIV infection itself creates a milieu of chronic inflammation and immune dysregulation, while some antiretroviral agents contribute to cardiometabolic changes that may accelerate vascular injury. When those mechanisms operate in the same patient over many years, conventional risk calculators built for HIV-negative populations can miss the biology that drives events.
Why Existing Risk Scores May Miss the Mark
The protocol centers on a simple but consequential mismatch: many commonly used cardiovascular risk tools, including the Framingham Risk Score, Pooled Cohort Equations, Systematic COronary Risk Evaluation, and WHO cardiovascular risk charts, were developed in general populations from high-income settings. Those models generally do not include HIV-specific predictors. In clinical practice, that means the score may look precise on paper while still failing to capture the added burden imposed by immune activation, treatment exposure, and the long time horizon of chronic HIV care.
Investigators also point to HIV-specific tools such as the D:A:D risk equation, which was developed in high-income countries rather than in sub-Saharan African cohorts. Prior work has shown that cardiovascular prediction models can perform suboptimally among people living with HIV, and the protocol treats that limitation as a reason to systematically catalogue the evidence instead of assuming one imported algorithm fits all settings.
The practical consequence is immediate at the bedside. If a risk score underestimates true cardiovascular danger, clinicians may delay statin therapy, blood pressure treatment, smoking cessation intensification, or more aggressive follow-up for a patient already carrying excess risk.
A Scoping Review Designed to Map, Not to Meta-Analyze
The planned review will follow the established scoping review approach of Arksey and O’Malley, together with methodological guidance from the Joanna Briggs Institute and reporting standards aligned with PRISMA-P and PRISMA-ScR. The structure is deliberate: this is a protocol intended to chart the territory rather than pool effect sizes. The authors will identify the research question, search for relevant studies, screen eligible reports, extract data, and then collate and summarize the findings.
The search window runs from 1 January 2000 through 3 June 2026. Five databases will be searched: PubMed, Scopus, Web of Science, Google Scholar, and CINAHL. Search terms will combine concepts related to HIV, cardiovascular disease, risk prediction models, and sub-Saharan Africa. Synonyms within each concept will be linked with OR, while the major concepts will be linked with AND. Retrieved citations will be imported into EndNote.
Two independent reviewers will handle screening and data extraction. That dual-review design is important in a field where the relevant literature may be sparse, heterogeneous, and unevenly indexed across databases. English-language studies that develop, validate, or apply cardiovascular risk prediction models among people living with HIV in sub-Saharan Africa will be eligible.
The Questions the Review Will Try to Answer
The protocol lays out three core questions. First, which cardiovascular disease risk prediction models have been developed or used for people living with HIV in sub-Saharan Africa? Second, which predictors and outcomes were included in those models? Third, how did the models perform in predicting cardiovascular disease?
Those questions matter because model performance is only useful when the inputs match the local patient population. A calculator that depends heavily on variables common in European or North American cohorts may behave differently in African settings where background epidemiology, health system access, and HIV treatment patterns vary. The review will therefore focus not only on naming the tools, but also on describing their structure and the context in which they were applied.
A Region Where HIV and Noncommunicable Disease Now Intersect
Sub-Saharan Africa carries the largest share of the global HIV burden, and the protocol places that epidemiologic reality at the center of its rationale. The authors cite the scale of the epidemic, the expanding use of antiretroviral therapy, and the resulting shift toward noncommunicable diseases as the main reason cardiovascular prediction deserves separate attention in this population. With more people living longer, the burden of atherosclerotic disease, stroke, and other cardiovascular outcomes becomes more visible in routine HIV care.
One sentence captures the clinical pressure behind the protocol: HIV services can no longer treat cardiovascular risk as an afterthought.
The pathophysiology supports that shift. Chronic immune activation can sustain vascular injury even when viral suppression is achieved, while treatment-related metabolic effects may compound traditional risk factors. In a setting where prevention resources are finite, choosing the wrong risk tool can distort triage and blur the line between low-risk and high-risk patients.
What This Protocol Can Deliver, and What It Cannot Yet Prove
Because this is a protocol, it does not present pooled results, model comparisons, or a validated ranking of predictive accuracy. It does, however, establish a clear route toward identifying which models have been used in sub-Saharan Africa and whether any are sufficiently adapted to the local HIV population to inform practice. The authors also state that no datasets were generated or analyzed for the current study and that the review has been registered on the Open Science Framework.
The most likely value of the completed review will be diagnostic rather than definitive. If the literature proves thin, fragmented, or dominated by models imported from outside the region, that finding will strengthen the case for building a region-specific cardiovascular prediction model for people living with HIV in sub-Saharan Africa. If better-performing local models already exist, the same review could help clarify which features deserve wider use and which assumptions should be discarded.
What the protocol cannot do is resolve the underlying performance problem without the evidence it plans to collect. The real test will come when the screening is finished and the study set is charted in detail, including how many models were developed, how many were validated, and whether any were built with HIV-specific predictors that reflect the biology of long-term treatment in African settings.