Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2

By Julia Hippisley-Cox, Carol Coupland, Y. Vinogradova, J. Robson, Rubin Minhas, Aziz Sheikh, P. Brindle. British medical journal, 2008

1,449 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Public Health, Environmental and Occupational Health, Surgery

View Paper: https://www.semanticscholar.org/paper/75bda7d1858bdcfaa2a5e358b5bff9f1f53902ad

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Citations in the corpus

Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “ML is becoming the transformational force in healthcare (89) for guiding individual and population health with several computational benefits, which includes realtime patient monitoring; disease patterns analysis (90); disease diagnosis and prescription of medicines; patientcentric care provision wi”
2016 European Guidelines on cardiovascular disease prevention in clinical practice (2016, background): “Traditionally most systems estimated CAD risk only; however, more recently a number of risk estimation systems have changed to estimate the risk of all CVDs.(44,47,50,58) The choice of CV mortality rather than total (fatal plus non-fatal) events was deliberate, although not universally popular.”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration (2015, background): “stated in journal articles (for example, QRISK2 [139]).”
2016 European Guidelines on cardiovascular disease prevention in clinical practice (2017, neutral): “mation model from the Scottish Intercollegiate Guidelines Network) [53], Q-Risk [54, 55], PROCAM (Prospective Cardiovascular Munster Study) [56],”
Artificial intelligence, bias and clinical safety (2019, neutral): “Clinical decision support systems (DSS) are in widespread use in medicine and have had most impact providing guidance on the safe prescription of medicines, 12 guideline adherence, simple risk screening 13 or prognostic scoring.”
EULAR evidence-based recommendations for cardiovascular risk management in patients with rheumatoid arthritis and other forms of inflammatory arthritis (2009, background): “The committee composed a research agenda to further optimise CV risk management (box 1). As evidence for AS and PsA is emerging, we will re-evaluate the evidence for AS and PsA and aim to revise the guidelines within 3 years. We acknowledge that our recommendations for the multiplication factor of 1”
Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events (2015, methodology): “Since last decade, risk models that are commonly used in practice such as the ‘QRISK-2’ and the ‘Framingham’ calculator for the risk of cardiovascular disease [4, 5] and the ‘HCM-SCD calculator’ for the risk of sudden cardiac death in patients with hypertrophic”
External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges (2016, methodology): “example, after development of the QRISK2 model using routinely collected data from 355 primary care practices, Hippisley-Cox et al.(5) immediately evaluated the model’s performance”
2016 European Guidelines on cardiovascular disease prevention in clinical practice. (2016, background): “Traditionally most systems estimated CAD risk only; however, more recently a number of risk estimation systems have changed to estimate the risk of all CVDs.(44,47,50,58) The choice of CV mortality rather than total (fatal plus non-fatal) events was deliberate, although not universally popular.”
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015, methodology): “9 million person years of observation) contributing 96709 cardiovascular events from the QRESEARCH database [20].”