SynthesisBMJ open2019
Can risk be predicted? An umbrella systematic review of current risk prediction models for cardiovascular diseases, diabetes and hypertension.
Synthesis in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- A Systematic Review and Meta-Analysis of the Predictive Power of China-PAR Against Cardiovascular Disease.Clinical medicine & research · 2024Pooled it
- American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2022Guideline
- Methodological approaches for assessing certainty of the evidence in umbrella reviews: A scoping review.PloS one · 2022Pooled it
- Clinically Relevant Discordance Between SCORE2 and REGICOR in Spanish Workers: Implications for Cardiovascular Risk Reclassification in Primary Prevention.Medical sciences (Basel, Switzerland) · 2026Article
- Development and Internal Validation of Machine-Learning Models for Short-Term Prediction of Day-to-Day Home Blood Pressure Variability Using IoT-Based Environmental and Activity Data: Protocol for the AURA-BPV Study.Circulation reports · 2026Article
- Advanced machine learning techniques for coronary artery disease risk prediction in early middle-aged adults.BMC medical genomics · 2025Article
- External validation of the American heart association's PREVENT equations in predicting atherosclerotic cardiovascular disease risk among Arab women and men: a retrospective cohort study.BMC cardiovascular disorders · 2025Article
- Prediction of early-onset bipolar using electronic health records.Journal of child psychology and psychiatry, and allied disciplines · 2025Article
- Metabolic syndrome risk prediction in an Australian sample with first-episode psychosis using the psychosis metabolic risk calculator: A validation study.Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists · 2025Article
- Construction of a predictive model for type 2 diabetes mellitus with coexisting hypertension: A cross-sectional study.Medicine · 2025Observational
- Novel Model Predicts Type 2 Diabetes Mellitus Patients Complicated With Metabolic Syndrome Using Retrospective Dataset From First Affiliated Hospital of Shenzhen University, China.International journal of endocrinology · 2025Article
- Optimizing strategies to identify high risk of developing type 2 diabetes.Frontiers in endocrinology · 2023Article
- Countermeasures for Maintaining Cardiovascular Health in Space Missions.Current cardiology reviews · 2023Article
- Cannabinoid Therapeutic Effects in Inflammatory Bowel Diseases: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.Biomedicines · 2022Review
- Cardiovascular Disease Risk Modeling for Astronauts: Making the Leap From Earth to Space.Frontiers in cardiovascular medicine · 2022Review
- Genetically Informed Regression Analysis: Application to Aggression Prediction by Inattention and Hyperactivity in Children and Adults.Behavior genetics · 2021Article
- Bipolar Disorder and Cardiovascular Risk in Rural versus Urban Populations in Colombia: A Comparative Clinical and Epidemiological Evaluation.Annals of global health · 2021Article
- A Prediction Model Based on Noninvasive Indicators to Predict the 8-Year Incidence of Type 2 Diabetes in Patients with Nonalcoholic Fatty Liver Disease: A Population-Based Retrospective Cohort Study.BioMed research international · 2021Article
- Impact of Mitochondrial DNA Mutations on Carotid Intima-Media Thickness in the Novosibirsk Region.Life (Basel, Switzerland) · 2020Article
- Interleukin-6-174G/C polymorphism is associated with a decreased risk of type 2 diabetes in patients with chronic hepatitis C virus.World journal of hepatology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo provide an overview of the currently available risk prediction models (RPMs) for cardiovascular diseases (CVDs), diabetes and hypertension, and to compare their effectiveness in proper recognition of patients at risk of developing these diseases.
designUmbrella systematic review. DATA SOURCES: PubMed, Scopus, Cochrane Library. ELIGIBILITY CRITERIA: Systematic reviews or meta-analysis examining and comparing performances of RPMs for CVDs, hypertension or diabetes in healthy adult (18-65 years old) population, published in English language. DATA EXTRACTION AND SYNTHESIS: Data were extracted according to the following parameters: number of studies included, intervention (RPMs applied/assessed), comparison, performance, validation and outcomes. A narrative synthesis was performed. Data were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. STUDY SELECTION: 3612 studies were identified. After title/abstract screening and removal of duplicate articles, 37 studies met the eligibility criteria. After reading the full text, 13 were deemed relevant for inclusion. Three further papers from the reference lists of these articles were then added. STUDY APPRAISAL: The methodological quality of the included studies was assessed using the AMSTAR tool. RISK OF BIAS IN INDIVIDUAL STUDIES: Risk of Bias evaluation was carried out using the ROBIS tool.
resultsSixteen studies met the inclusion criteria: six focused on diabetes, two on hypertension and eight on CVDs. Globally, prediction models for diabetes and hypertension showed no significant difference in effectiveness. Conversely, some promising differences among prediction tools were highlighted for CVDs. The Ankle-Brachial Index, in association with the Framingham tool, and QRISK scores provided some evidence of a certain superiority compared with Framingham alone. LIMITATIONS: Due to the significant heterogeneity of the studies, it was not possible to perform a meta-analysis. The electronic search was limited to studies in English and to three major international databases (MEDLINE/PubMed, Scopus and Cochrane Library), with additional works derived from the reference list of other studies; grey literature with unpublished documents was not included in the search. Furthermore, no assessment of potential adverse effects of RPMs was carried out.
conclusionsConsistent evidence is available only for CVD prediction: the Framingham score, alone or in combination with the Ankle-Brachial Index, and the QRISK score can be confirmed as the gold standard. Further efforts should not be concentrated on creating new scores, but rather on performing external validation of the existing ones, in particular on high-risk groups. Benefits could be further improved by supplementing existing models with information on lifestyle, personal habits, family and employment history, social network relationships, income and education. PROSPERO REGISTRATION NUMBER: CRD42018088012.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.