ArticleJournal of cardiovascular translational research2016
Improvement in Cardiovascular Risk Prediction with Electronic Health Records.
Article in Journal of cardiovascular translational research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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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
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of risk factors associated with depression and anxiety in cancer patients.PloS one · 2024Pooled it
- Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis.BMC medicine · 2019Pooled it
- Evaluating the Impact of a Lifestyle-Focused Approach on Cardiovascular Risk in Inpatients With Mental Illness: Analyzing Effectiveness and Fidelity in a Stepped Wedge Cluster Randomized Trial (MULTI+ Study V).Psychiatric research and clinical practice · 2025Article
- Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach.American journal of preventive cardiology · 2024Article
- Replicability and reproducibility of predictive models for diagnosis of depression among young adults using Electronic Health Records.Diagnostic and prognostic research · 2023Article
- Evidence supporting the choice of a new cardiovascular risk equation for Australia.The Medical journal of Australia · 2023Review
- The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples.The Lancet regional health. Europe · 2022Article
- Contextualizing National Policies Regulating Access to Low-Dose Aspirin in America and Europe Using the Full Report of a Transatlantic Patient Survey of Aspirin in Preventive Cardiology.Journal of the American Heart Association · 2022Article
- Accurate Prediction of Stroke for Hypertensive Patients Based on Medical Big Data and Machine Learning Algorithms: Retrospective Study.JMIR medical informatics · 2021Article
- Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study.BMJ (Clinical research ed.) · 2021Article
- Cost-Effectiveness of Initiating Pharmacological Treatment in Stage One Hypertension Based on 10-Year Cardiovascular Disease Risk: A Markov Modeling Study.Hypertension (Dallas, Tex. : 1979) · 2021Article
- Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.JMIR medical informatics · 2021Article
- Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.BMJ (Clinical research ed.) · 2020Article
- Population-level changes in lifestyle risk factors for cardiovascular disease in the Heart of New Ulm Project.Preventive medicine reports · 2019Article
- Discrimination between Obstructive Coronary Artery Disease and Cardiac Syndrome X in Women with Typical Angina and Positive Exercise Test; Utility of Cardiovascular Risk Calculators.Medicina (Kaunas, Lithuania) · 2019Article
- Factors associated with exacerbations among adults with asthma according to electronic health record data.Asthma research and practice · 2019Article
- Article
- Approaches to Link Geospatially Varying Social, Economic, and Environmental Factors with Electronic Health Record Data to Better Understand Asthma Exacerbations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Subclinical parameters of arterial stiffness and arteriosclerosis correlate with QRISK3 in systemic lupus erythematosus.PloS one · 2018Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The aim of this study was to compare the QRISKII, an electronic health data-based risk score, to the Framingham Risk Score (FRS) and atherosclerotic cardiovascular disease (ASCVD) score. Risk estimates were calculated for a cohort of 8783 patients, and the patients were followed up from November 29, 2012, through June 1, 2015, for a cardiovascular disease (CVD) event. During follow-up, 246 men and 247 women had a CVD event. Cohen's kappa statistic for the comparison of the QRISKII and FRS was 0.22 for men and 0.23 for women, with the QRISKII classifying more patients in the higher-risk groups. The QRISKII and ASCVD were more similar with kappa statistics of 0.49 for men and 0.51 for women. The QRISKII shows increased discrimination with area under the curve (AUC) statistics of 0.65 and 0.71, respectively, compared to the FRS (0.59 and 0.66) and ASCVD (0.63 and 0.69). These results demonstrate that incorporating additional data from the electronic health record (EHR) may improve CVD risk stratification.
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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.