---
title: "Cardiovascular risk prediction models in women: protocol for an updated systematic review"
id: "plos-one-7-cardiovascular-risk-prediction-models-for-women-a-systematic-review-protocol"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-7-cardiovascular-risk-prediction-models-for-women-a-systematic-review-protocol"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360860"
published_at: "2026-10-15T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Cardiovascular risk prediction models in women: protocol for an updated systematic review
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-7-cardiovascular-risk-prediction-models-for-women-a-systematic-review-protocol
- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360860)
- **Published At:** 2026-10-15T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Purpose: The review will identify and summarize published multivariable **cardiovascular disease** risk prediction models developed for women or incorporating female-specific predictors, and will evaluate model performance and clinical usability. - Timeframe and scope: Investigators will update the Baart et al. search by screening MEDLINE and EMBASE from July 27, 2017 to May 2026; only studies found in that interval will be included and earlier Baart et al. studies will not be re-searched. - Eligible studies: Development studies in female-only cohorts, general-population models that include sex as predictor or effect modifier, external validation in female-only or sex-stratified samples, and incremental value studies assessing predictors such as pre-eclampsia, gestational diabetes, preterm delivery, premature menopause and related conditions. - Outcomes and models: The review will consider models predicting fatal and non-fatal arterial cardiovascular outcomes (coronary heart disease, myocardial infarction, stroke, cardiovascular death, heart failure). Machine learning and AI-derived prediction models are eligible if they meet criteria. - Data items to be extracted: Study design, population, outcome definition, modelling method, predictors, handling of missing data, validation procedures, performance metrics (discrimination, calibration, reclassification), and clinical usability (predictor count, availability of equations or online calculators). For incremental studies, which predictors added value will be recorded. - Risk of bias and tools: The team will use **PROBAST+AI** for risk of bias assessment; screening and extraction are planned independently and in duplicate using Covidence and a CHARMS-based extraction sheet. - Synthesis plan: Descriptive synthesis will summarize model characteristics; quantitative meta-analysis will be considered only when at least three external validation studies evaluate the same model with comparable outcomes and horizons; pooled c-statistics and calibration measures will be estimated using random-effects models when appropriate. - Analysis caveats: The protocol notes potential heterogeneity in populations, outcomes and performance metrics, limited database scope (MEDLINE, EMBASE only), and that quantitative synthesis may be infeasible for many models. - Timeline and transparency: Protocol registered in **PROSPERO** (CRD420261420766); literature screening and data extraction scheduled to complete by January 2027, with manuscript preparation by May 2027. - Unanswered questions: The protocol will assess whether incorporating **female-specific** risk factors provides incremental predictive value beyond traditional predictors; the degree of incremental value remains uncertain and is a central research question.
## Clinical Analysis & Structured Key Points
Cardiovascular risk prediction models for women: A systematic review protocol | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Abstract Purpose Cardiovascular disease remains a leading cause of morbidity and mortality in women. Cardiovascular risk prediction models are widely used to guide preventative management, although many do not incorporate female-specific risk factors. The aim of this review is to identify published cardiovascular risk prediction models for women or those which include female-specific predictors and to summarize the predictors and model performance. Methods The protocol was developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) guidelines. The protocol has been registered in PROSPERO (CRD420261420766). An update of the systematic review by Baart et al. will be performed by searching MEDLINE and EMBASE from July 27 2017 to May 2026 to identify primary articles reporting on the development and/or validation of models predicting incident cardiovascular disease in women. Data on model predictors, performance, validation and clinical usability will be extracted. Risk of bias will be assessed using PROBAST+AI. Findings will be synthesized descriptively although quantitative synthesis will be considered when sufficiently comparable studies are available. Conclusions This systematic review will provide an updated synthesis of cardiovascular risk prediction models in women and summarize how female-specific risk factors are incorporated into contemporary risk assessment. Citation: Frank C, Tehfe H, Ghanem C, Choo-Foo J, Hakimjavadi R, Jalal H, et al. (2026) Cardiovascular risk prediction models for women: A systematic review protocol. PLoS One 21(10): e0360860. https://doi.org/10.1371/journal.pone.0360860 Editor: Athanasios G. Pantelis, Athens Medical Group, Psychiko Clinic, GREECE Received: July 6, 2026; Accepted: September 29, 2026; Published: October 15, 2026 Copyright: © 2026 Frank et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion. Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. Background Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality [ 1 ]. It is estimated that up to 75% of premature CVD is preventable if early risk factor identification and modification is offered [ 1 – 3 ]. Well-established sex differences exist in CVD, including variability in clinical symptoms, pathophysiology and prognosis [ 4 – 6 ]. Notably, women experience inferior outcomes after cardiac events possibly due to under-representation in research and lack of consideration of sex-specific risk enhancers in clinical assessment, highlighting the importance of improving cardiovascular risk recognition in this population [ 7 ]. Cardiovascular risk prediction models are an important component of primary prevention and inform decisions regarding management and intensity of follow-up. However, their clinical utility depends on accurate risk estimation in the populations to which they are applied [ 8 ]. While many traditional risk factors are shared between sexes, there are several well established female-specific variables known to contribute to increased CVD risk including early menopause as well as pregnancy complications such as hypertensive disorders of pregnancy or preterm birth [ 9 – 10 ]. Studies suggest that general population risk estimates may not fully capture cardiovascular risk in young women, particularly in those with a history of adverse pregnancy outcomes (APO) [ 11 ]. Recognizing APOs as significant risk factors for future cardiovascular disease allows for timely identification and engagement in preventative strategies, particularly during the postpartum period when individuals are well connected to the healthcare system. Even modest risk reductions, when applied early in individuals with a long remaining life expectancy, can accrue over time and translate to meaningful reductions in cardiovascular events [ 12 ]. Over the past two decades, numerous prediction models have been developed to identify individuals at high risk for CVD and to help inform clinical decisions. Despite increasing recognition of sex-specific risk enhancers, many widely used models remain uniform between sexes and do not incorporate female specific predictors [ 13 ]. Determining whether female-specific predictors provide incremental prognostic value beyond established risk factors may further provide opportunities to improve care. Baart et al. conducted a systematic review of cardiovascular risk prediction models for women with the literature search concluding in 2017. Since that time, recognition of female-specific risk factors has increased alongside an emergence in model development and thus an updated review is warranted to evaluate contemporary evidence and examine the incremental predictive value of female-specific risk factors. The aim of this review is to identify and characterize cardiovascular risk prediction models in women and evaluate the incremental value of female-specific predictors. We will summarize the model predictors, validation, performance and clinical usability for prediction of fatal and non-fatal cardiovascular outcomes. Methods We will perform a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) guidelines ( S1 Table ). The protocol is registered on PROSPERO (ID: CRD420261420766). Any important amendments to this protocol will be updated in PROSPERO and described in the final publication. This review received no funding and had no sponsor and thus no funder or sponsor had any role in the design of the protocol. This review will update the search by Baart et al published in 2019. The present review will include only studies identified by the updated search period and will not incorporate studies included in the original Baart et al review [ 13 ]. We will search Medline and EMBASE from July 27 2017 until May 2026 to identify primary articles reporting the development and/or validation of models predicting incident cardiovascular disease in women. As this review is designed as an update of Baart et al., we will retain the same databases used in the original review to ensure continuity of the search strategy while maintaining broad coverage of the biomedical and cardiovascular literature. Eligibility criteria We will include articles which report on at least one multivariable prediction model, tool or score for individual risk estimation of future CVD outcome in women. Models will be included if they report on: 1) models developed in female-only cohorts, 2) general population models in which sex is included as a predictor or as an effect modifier, 3) studies that externally validate existing models (validation papers) in female-only or sex-stratified populations and 4) studies that investigate the added value of female-specific predictors to existing models (incremental value papers) including but not limited to: premature menopause or menarche, polycystic ovary syndrome, pre-eclampsia, gestational diabetes, gestational hypertension, preterm delivery, fetal growth restriction, endometriosis or perinatal depression. Prediction models using statistical methods, machine learning or artificial-intelligence approaches will be eligible if they meet all other inclusion criteria. While resulting in a broader review, general population models will be included if they explicitly incorporate sex as a predictor or effect modifier, or if they are validated in a female-only or sex-stratified population), as these models provide relevant information on how sex is incorporated into cardiovascular risk estimation and how established models perform when applied specifically to women. Eligible studies will be classified into the following groups: Development studies of CVD prediction models in female-only cohorts (will be defined as female specific models) Development studies of CVD prediction models in general population but include sex as predictor or effect modifier (will be defined as sex predictor or sex modifier models) Validation studies where performance of an existing model is verified in female only or sex-stratified populations (will be defined as validation papers) Incremental value studies which evaluate the addition of female-specific predictors to existing CVD risk models (will be defined as incremental value papers) Articles will be included if they report models for predicting any fatal or non-fatal arterial CVD event including coronary heart disease, myocardial infarction, stroke, cardiovascular death or congestive heart failure. Articles must be in English or French and report original research. We will exclude articles describing models for predicting risk of venous disease, validation articles with a cross sectional study design or articles describing models developed from or validated exclusively in specific diseased populations such as patients with diabetes, chronic kidney disease or HIV. However, studies conducted in female-specific or obstetric populations (such as those with hypertensive disorders of pregnancy) will remain eligible for inclusion. Information sources The search strategy from Baart et al will be updated in collaboration with a medical librarian ( S1 Text ). We will perform an update of their search using Medline and EMBASE from July 27 2017 until May 2026 to identify primary articles reporting on development and/or validation of models predicting incident cardiovascular disease in women. Letters, conference abstracts and conference presentations will be excluded. Reference lists of included studies will be screened to identify any additional eligible articles. No additional electronic databases, trial registries or contact with study authors are planned for this review. Study records Study records will be managed with Covidence Systematic Review software. Articles will be reviewed independently and in duplicate by two authors based on title and abstract followed by full text. Disagreements will be resolved through group consensus. Data extraction Data will be extracted into a pre-specified spreadsheet based on the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) [ 14 ]. Data extraction will be done independently by one author and independently verified by a second author. Disagreements will be resolved by group consensus. Where required information is not reported, data will be recorded as unavailable. We will extract information regarding model development and validation, sample size and number of outcome events. Further information to be extracted includes study design, study population, geographic location, outcome definition, prediction, modelling method, number and type of predictors, methods for handling missing data, handling of data, internal and external validation procedures, model performance measures (discrimination, calibration and reclassification) and measures of clinical usability (number of predictors, availability of model equation and online calculator). For included incremental value papers, we will also extract information on the model that was used and which predictors had incremental value. For those describing external validation, we will also include which model was validated, type of validation and performance metrics. Risk of bias Risk of bias assessment will be completed using the Prediction model Risk of Bias Assessment Tool and Artificial Intelligence (PROBAST+AI) [ 15 ]. Assessments will be completed independently by one reviewer and checked by a second reviewer with disagreements resolved through group consensus. Risk of bias assessments will be used to inform the interpretation of study findings and overall strength of the evidence. Study status The literature search will include studies published between July 27 2017 and May 2026. Early screening of titles/abstracts has been started at time of submission. We anticipate completion of literature screening, data extraction and risk of bias assessment by January 2027 with data synthesis and manuscript preparation completed by May 2027. Data synthesis Results will be summarized using descriptive statistics. Quantitative synthesis will be considered only where at least three external validation studies evaluate the same prediction model using comparable outcomes, prediction horizon and performance measures. The availability of three studies will be considered a minimum criteria and not in itself sufficient for meta-analysis. Where appropriate, pooled c-statistics as well as calibration measures (calibration intercept and slope) will be estimated using random-effects meta-analysis. C-statistics will be transformed to the logit scale prior to pooling. Standard errors will be obtained from reported data. Pooled estimates and corresponding 95% confidence intervals will subsequently be back-transformed to original c-statistic scale for presentation. Statistical heterogeneity will be assessed using the I 2 and τ 2 and a 95% prediction interval will be reported when possible. Where multiple eligible performance estimates are reported from the same study population, the estimate that most corresponds to the outcome and prediction horizon being evaluated in that meta-analysis will be selected. Pooled estimates will be interpreted cautiously given expected differences in populations, outcome definitions and follow-up duration. Where sufficient studies are available, sensitivity analyses based on risk of bias assessments will be considered including analysis excluding studies judged to have significant risk of bias. Where quantitative synthesis is not appropriate, findings will be synthesized narratively by comparing model characteristics, predictors, model performance and measures of clinical usability. Analyses will be performed in R. Formal assessment of publication bias is not planned as quantitative synthesis is anticipated only for a limited number of external validation studies. No formal certainty of evidence framework will be applied. The strength of the evidence will consider clinical and methodological heterogeneity and risk of bias assessment. Discussion This systematic review will present an updated synthesis of cardiovascular risk prediction models with particular attention to model performance and the incremental value of female-specific risk factors. This review will aim to clarify whether contemporary practices adequately capture cardiovascular risk prediction in women and whether incorporation of female-specific factors improves model performance. The review has been designed in accordance with established guidance for systematic reviews of prediction models. Study selection will be performed independently and in duplicate, data extraction will follow the CHARMS framework and risk of bias will be systematically assessed using PROBAST+AI. Where studies are sufficiently comparable, quantitative synthesis will be considered, otherwise, model characteristics will be synthesized narratively. This review is relevant given increasing recognition that several female-specific factors, including adverse pregnancy outcomes, are associated with cardiovascular disease. However, the extent to which these factors contribute additional predictive information beyond traditional risk factors remains uncertain. This study was designed as an update of Baart et al. and as such maintains continuity with the previous search strategy by only searching MEDLINE and EMBASE. While these databases provide broad coverage, this restriction may result in eligible studies being missed and thus reference lists of included studies will be screened to mitigate this limitation. Heterogeneity in study populations, prediction horizons and reported measures of model performance may also limit feasibility of quantitative synthesis. Conclusion This systematic review will provide an updated analysis of the current evidence on cardiovascular risk prediction models for women and clarify whether the inclusion of female-specific risk factors provides predictive value. These findings may inform future model development and identify priorities for research. Supporting information S1 Table. PRISMA-P Checklist. https://doi.org/10.1371/journal.pone.0360860.s001 (DOCX) S1 Text. Example search strategy for MEDLINE. https://doi.org/10.1371/journal.pone.0360860.s002 (DOCX) References 1. Eckel RH, Jakicic JM, Ard JD, de Jesus JM, Miller NH, Hubbard VS, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk: A report of the American College of Cardiology/American heart association task force on practice guidelines. Circulation. 2014;129(Suppl 2):S76–99. View Article Google Scholar 2. Jones O, Ormesher L, Duhig KE, Peacock L, Myers JE. Pre-eclampsia and future cardiovascular disease risk: Assessing British clinicians’ knowledge and practice. Pregnancy Hypertens. 2024;37:101145. pmid:39128383 View Article PubMed/NCBI Google Scholar 3. Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: Systematic review. BMJ. 2016;353:i2416. pmid:27184143 View Article PubMed/NCBI Google Scholar 4. Dean J, Cruz SD, Mehta PK, Merz CNB. Coronary microvascular dysfunction: Sex-specific risk, diagnosis, and therapy. Nat Rev Cardiol. 2015;12(7):406–14. pmid:26011377 View Article PubMed/NCBI Google Scholar 5. Regitz-Zagrosek V, Kararigas G. Mechanistic pathways of sex differences in cardiovascular disease. Physiol Rev. 2017;97(1):1–37. pmid:27807199 View Article PubMed/NCBI Google Scholar 6. Ventura-Clapier R, Dworatzek E, Seeland U, Kararigas G, Arnal J-F, Brunelleschi S, et al. Sex in basic research: concepts in the cardiovascular field. Cardiovasc Res. 2017;113(7):711–24. pmid:28472454 View Article PubMed/NCBI Google Scholar 7. Venetkoski M, Joensuu J, Gissler M, Ylikorkala O, Mikkola TS, Savolainen-Peltonen H. Pre-eclampsia and cardiovascular risk: A long-term nationwide cohort study on over 120 000 Finnish women. BMJ Open. 2022;12(12):e064736. pmid:36549728 View Article PubMed/NCBI Google Scholar 8. Khan SS, Greenland P, Hayman LL, Khera R, Navar AM, Pencina MJ, et al. Criteria to assess the predictive and clinical utility of novel models, biomarkers, and tools for risk of cardiovascular disease: A scientific statement from the American heart association. Circulation. 2026;153(11):e953–70. pmid:41669831 View Article PubMed/NCBI Google Scholar 9. Sukmanee J, Liabsuetrakul T. Risk of future cardiovascular diseases in different years postpartum after hypertensive disorders of pregnancy: A systematic review and meta-analysis. Medicine (Baltimore). 2022;101(30):e29646. pmid:35905265 View Article PubMed/NCBI Google Scholar 10. Tan YY, Gast GCM, van der Schouw YT. Gender differences in risk factors for coronary heart disease. Maturitas. 2010;65(2):149–60. View Article Google Scholar 11. Wambua S, Crowe FL, Thangaratinam S, O’Reilly D, McCowan C, Brophy S, et al. Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors. BMC Med. 2025;23(1):508. pmid:40883735 View Article PubMed/NCBI Google Scholar 12. Pencina MJ, Pencina KM, Lloyd-Jones D, C
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