The stakes are considerable. Clinicians routinely use risk prediction models to decide who should receive blood pressure medication, statins, aspirin or intensified lifestyle counseling. When those models underestimate risk in women, the consequences ripple through entire lifetimes of preventive care. Many widely used tools, including some versions of the Framingham Risk Score, derive primarily from cohorts in which women were underrepresented or in which female-specific predictors such as pregnancy complications, preeclampsia, gestational diabetes, premature menopause and autoimmune diseases were simply not collected. The result is a persistent gap between the risk a calculator reports and the risk a woman actually carries.

The new protocol, developed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Protocols, known as PRISMA-P, describes a rigorous and updated search strategy. The researchers will query MEDLINE and EMBASE for primary studies published between July 27, 2017 and May 2026, a window chosen to extend a previous systematic review by Baart and colleagues that covered earlier literature. This update matters because the past several years have seen an explosion of new models, driven in part by machine learning methods and by growing recognition of sex-specific cardiovascular biology.

What distinguishes this review is its explicit focus on female-specific predictors. Contemporary evidence has established that conditions unique to or more common in women carry substantial prognostic weight. Hypertensive disorders of pregnancy, for example, are associated with roughly a doubling of later cardiovascular risk, and gestational diabetes signals elevated risk of future type 2 diabetes and heart disease. Early menopause, polycystic ovary syndrome, and autoimmune conditions such as lupus and rheumatoid arthritis also modify risk trajectories. The review will systematically catalogue which of these variables appear in published models and which remain absent.

Methodologically, the team will extract detailed data on each model’s predictors, performance metrics, validation status and clinical usability. Performance in prediction modeling is typically assessed with measures such as the C-statistic, which quantifies discrimination, meaning how well the model separates women who will develop cardiovascular disease from those who will not, and calibration, which measures whether predicted probabilities match observed event rates across the risk spectrum. A model can discriminate well yet still mislead clinicians if it systematically overestimates or underestimates risk in a given population, so calibration is increasingly viewed as the more clinically meaningful yardstick.

Validation is where many prediction models falter. A model trained on one cohort often performs poorly when transported to another population with different baseline risk, ethnicity profiles or healthcare contexts. External validation, in which the model is tested on data not used in its development, is considered essential before clinical deployment, yet studies suggest a large fraction of published models are never externally validated at all. The review will document how often validation occurs and where, giving clinicians and guideline developers a clearer picture of which tools rest on solid footing.

To assess the methodological quality of included studies, the researchers will use PROBAST+AI, a risk-of-bias tool specifically designed for prediction model studies and extended to accommodate artificial intelligence approaches. This is a timely choice. Machine learning models for cardiovascular risk have proliferated rapidly, and while some demonstrate impressive internal performance, concerns about overfitting, inadequate reporting and poor generalizability are widespread. PROBAST+AI evaluates domains such as participant selection, predictors, outcomes and sample size, providing a structured way to separate rigorous work from statistically fragile claims.

The synthesis plan is descriptive by default, meaning the team will summarize findings narratively, tabulating models, predictors and performance figures across studies. However, the protocol leaves the door open to quantitative synthesis, such as meta-analysis of C-statistics or calibration measures, when studies prove sufficiently comparable in population, outcome definition and follow-up duration. This pragmatic approach acknowledges the notorious heterogeneity of prediction modeling literature, where outcome definitions, time horizons and predictor sets vary so widely that pooling is often inappropriate.

Registration of the protocol in PROSPERO under identifier CRD420261420766 adds an important safeguard against selective reporting. By committing in advance to their search strategy, inclusion criteria and analysis plan, the authors reduce the risk of hindsight-driven decisions that could bias the review’s conclusions. This practice aligns with broader movements toward reproducibility in health research and gives readers confidence that the eventual findings will reflect the evidence rather than the reviewers’ preferences.

The anticipated impact extends beyond academic interest. Guidelines from major cardiology societies increasingly call for sex-specific risk assessment, and some recent tools, such as the American Heart Association’s PREVENT equations, have begun incorporating pregnancy history and other female-specific variables. A comprehensive, updated inventory of available models, their predictors and their validated performance will help guideline committees choose the best instruments, help researchers identify gaps where no adequately validated model exists, and ultimately help clinicians tailor prevention to the women they treat. As the review’s authors conclude, the work will provide an updated synthesis of cardiovascular risk prediction models in women and clarify exactly how female-specific risk factors are being woven into contemporary risk assessment, a small methodological effort with the potential to reshape preventive cardiology for half the population.

Subject of Research: Systematic review of cardiovascular risk prediction models for women incorporating female-specific risk factors

Article Title: Cardiovascular risk prediction models for women: A systematic review protocol

Article References: Frank, C., Tehfe, H., Ghanem, C., Choo-Foo, J., Hakimjavadi, R., Jalal, H., Shorr, R., & Kobewka, D. (2026). Cardiovascular risk prediction models for women: A systematic review protocol. PLOS One, 21(10), e0360860. https://doi.org/10.1371/journal.pone.0360860

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360860

Keywords: cardiovascular disease, risk prediction models, women's health, systematic review, PRISMA-P, PROBAST+AI, female-specific risk factors, preventive cardiology, PROSPERO, machine learning, model validation, PLOS One