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Derivation and validation of QStroke score for predicting risk of ischaemic stroke in primary care and comparison with other risk scores: a prospective open cohort study

BMJ 2013; 346 doi: (Published 02 May 2013) Cite this as: BMJ 2013;346:f2573
  1. Julia Hippisley-Cox, professor of clinical epidemiology and general practice1,
  2. Carol Coupland, associate professor and reader in medical statistics1,
  3. Peter Brindle, research and development programme director2
  1. 1Division of Primary Care, University Park, Nottingham NG2 7RD, UK
  2. 2Avon Primary Care Research Collaborative, Bristol Clinical Commissioning Group, Bristol BS1 3NX, UK
  1. Correspondence to: J Hippisley-Cox Julia.hippisley-cox{at}
  • Accepted 22 March 2013


Objective To develop and validate a risk algorithm (QStroke) to estimate risk of stroke or transient ischaemic attack in patients without prior stroke or transient ischaemic attack at baseline; to compare (a) QStroke with CHADS2 and CHA2DS2VASc scores in patients with atrial fibrillation and (b) the performance of QStroke with the Framingham stroke score in the full population free of stroke or transient ischaemic attack.

Design Prospective open cohort study using routinely collected data from general practice during the study period 1 January 1998 to 1 August 2012.

Setting 451 general practices in England and Wales contributing to the national QResearch database to develop the algorithm and 225 different QResearch practices to validate the algorithm.

Participants 3.5 million patients aged 25-84 years with 24.8 million person years in the derivation cohort who experienced 77 578 stroke events. For the validation cohort, we identified 1.9 million patients aged 25-84 years with 12.7 million person years who experienced 38 404 stroke events. We excluded patients with a prior diagnosis of stroke or transient ischaemic attack and those prescribed oral anticoagulants at study entry.

Main outcome measures Incident diagnosis of stroke or transient ischaemic attack recorded in general practice records or linked death certificates during follow-up.

Risk factors Self assigned ethnicity, age, sex, smoking status, systolic blood pressure, ratio of total serum cholesterol to high density lipoprotein cholesterol concentrations, body mass index, family history of coronary heart disease in first degree relative under 60 years, Townsend deprivation score, treated hypertension, type 1 diabetes, type 2 diabetes, renal disease, rheumatoid arthritis, coronary heart disease, congestive cardiac failure, valvular heart disease, and atrial fibrillation

Results The QStroke algorithm explained 57% of the variation in women and 55% in men without a prior stroke. The D statistic for QStroke was 2.4 in women and 2.3 in men. QStroke had improved performance on all measures of discrimination and calibration compared with the Framingham score in patients without a prior stroke. Among patients with atrial fibrillation, levels of discrimination were lower, but QStroke had some improved performance on all measures of discrimination compared with CHADS2 and CHA2DS2VASc.

Conclusion QStroke provides a valid measure of absolute stroke risk in the general population of patients free of stroke or transient ischaemic attack as shown by its performance in a separate validation cohort. QStroke also shows some improvement on current risk scoring methods, CHADS2 and CHA2DS2VASc, for the subset of patients with atrial fibrillation for whom anticoagulation may be required. Further research is needed to evaluate the cost effectiveness of using these algorithms in primary care.


  • We acknowledge the contribution of EMIS practices that contribute to QResearch and the University of Nottingham and EMIS for expertise in establishing, developing, and supporting the database.

  • Contributors: JHC initiated the study; undertook the literature review, data extraction, data manipulation, and primary data analysis; and wrote the first draft of the paper. CC contributed to the design, analysis, interpretation, and drafting of the paper. PB contributed to the development of core ideas, the analysis plan, the interpretation of the results, and the drafting of the paper.

  • Funding: None.

  • Competing interests: All authors have completed the ICMJE uniform disclosure form at (available on request from the corresponding author) and declare: JHC is professor of clinical epidemiology at the University of Nottingham and co-director of QResearch, a not-for-profit organisation which is a joint partnership between the University of Nottingham and EMIS (commercial supplier of IT for 60% of general practices in the UK). JHC is also director of ClinRisk, which produces open and closed source software to ensure reliable and updatable implementation of clinical risk algorithms within clinical computer systems. CC is associate professor of Medical Statistics at the University of Nottingham and a consultant statistician for ClinRisk. This work and any views expressed within it are solely those of the authors and not of any affiliated bodies or organisations. There are no other relationships or activities that could appear to have influenced the submitted work.

  • Approvals: The project was approved in accordance with the QResearch agreement with Trent Multi-Centre Research Ethics Committee.

  • Data sharing: The patient level data from the QResearch are specifically licensed according to its governance framework. See for further details. The QStroke algorithm will be published as open source software under the GNU Lesser Public License.

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