Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study
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Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors. Design Prospective open cohort study. Setting General practices in England providing data for the QResearch database. Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline. Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status. Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records. Results 363 565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell’s C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms. Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, SLE, atypical antipsychotics, severe mental illness, and erectile dysfunction) can help enable doctors to identify those at most risk of heart disease and stroke.
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These calculators estimate 10-year risk of microvascular and macrovascular complications.
“These calculators can be used to predict 10-year risk for microvascular and macrovascular complications.”
From Type 1 diabetes (2018) · cites this paper as [130]
The Results > Validation section reports predicted 10-year cardiovascular disease risk. Methods > Outcomes defines the outcome as coronary heart disease, ischaemic stroke, or transient ischaemic attack. These support prediction of macrovascular complications, but the paper does not establish prediction of microvascular complications. (LLM, conf=0.90)
Experimental — accuracy not yet measuredThe QDiabetes and QRISK3 online tools were built using prospective general practice data from 803,044 people with diabetes, including 44,440 with type 1 diabetes.
“Two examples are the QDiabetes129 and QRISK3130 web calculators that were developed with a prospective general practice dataset of 803 044 people with diabetes (44 440 with type 1 diabetes).”
From Type 1 diabetes (2018) · cites this paper as [130]
The Methods (Study design and data source) confirm that QRISK3 was developed using prospective general-practice data, and the Discussion (Type 1 diabetes) confirms that it can calculate risk for people with type 1 diabetes. But the Results (Study population) report 7,889,803 people in the derivation cohort, not a cohort of 803,044 people with diabetes; the baseline-characteristics table lists 21,677 with type 1 diabetes in that cohort, not 44,440. This paper does not establish the claim about QDiabetes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredResearch has found further non-glycaemic factors that raise complication risk.
“Several studies127–130 have identified additional non-glycaemic risk factors for the development of complications.”
From Type 1 diabetes (2018) · cites this paper as [130]
The Results tables report associations between several non-glycaemic factors and future cardiovascular disease, supporting the claim for that outcome. The paper studies cardiovascular events in a general cohort, however, rather than complications broadly or their development specifically among people with diabetes. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe QRisk3 tool for predicting cardiovascular disease can be used even if the general practitioner does not enter information on blood pressure variability.
“the QRisk3 tool for predicting cardiovascular disease can be used even if the general practitioner does not enter information on blood pressure variability (the standard deviation of repeated readings).60”
From Developing clinical prediction models: a step-by-step guide (2024) · cites this paper as [60]
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- Authors
- Hippisley-Cox, J., Coupland, C., & Brindle, P.
- Affiliations
- University of Nottingham (GB)
- University Hospitals Bristol NHS Foundation Trust (GB)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2017-05-23
- Volume
- 357
- Pages
- j2099
- ISSN
- 1756-1833 (ISSN-L)
- 0959-8138
- PMC
- PMC5441081
- OpenAlex
- W2618596952
- Cited by
- 1,703
- References
- 64
- Citations by year
- 2026: 182 · 2025: 258 · 2024: 263 · 2023: 215 · 2022: 219 · 2021: 211 · 2020: 164 · 2019: 110 · 2018: 65 · 2017: 15
- FWCI
- 90.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- BMJ (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repository@Nottingham (University of Nottingham) (publishedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Keywords
- QRISK3, cardiovascular risk prediction, chronic kidney disease, systolic blood pressure variability, migraine, corticosteroids, systemic lupus erythematosus, atypical antipsychotics, severe mental illness, erectile dysfunction, sex differences in cardiovascular risk, QResearch database, general practice data, discrimination and calibration, incident cardiovascular disease
- MeSH terms
- Adult, Aged, Aged, 80 and over, Algorithms, Atrial Fibrillation, Mental Disorders, Blood Pressure, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Female, United Kingdom, Humans, Erectile Dysfunction, Male, Middle Aged, Prospective Studies, Rheumatic Fever, Risk Factors, Antipsychotic Agents, Proportional Hazards Models, Risk Assessment, Renal Insufficiency, Chronic, General Practice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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