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Development and validation of risk prediction algorithm (QThrombosis) to estimate future risk of venous thromboembolism: prospective cohort study

BMJ 2011; 343 doi: (Published 16 August 2011) Cite this as: BMJ 2011;343:d4656
  1. Julia Hippisley-Cox, professor of clinical epidemiology and general practice ,
  2. Carol Coupland, associate professor in medical statistics
  1. 1Division of Primary Care, University Park, Nottingham NG2 7RD, UK
  1. Correspondence to: J Hippisley-Cox juliahippisleycox{at}
  • Accepted 16 June 2011


Objectives To derive and validate a new clinical risk prediction algorithm (QThrombosis, to estimate individual patients’ risk of venous thromboembolism.

Design Prospective open cohort study using routinely collected data from general practices. Cox proportional hazards models used in derivation cohort to derive risk equations evaluated at 1 and 5 years. Measures of calibration and discrimination undertaken in validation cohort.

Setting 564 general practices in England and Wales contributing to the QResearch database.

Participants Patients aged 25-84 years, with no record of pregnancy in the preceding 12 months or any previous venous thromboembolism, and not prescribed oral anticoagulation at baseline: 2 314 701 in derivation cohort and 1 240 602 in validation cohort.

Outcomes Incident cases of venous thromboembolism, either deep vein thrombosis or pulmonary embolism, recorded in primary care records or linked cause of death records.

Results The derivation cohort included 14 756 incident cases of venous thromboembolism from 10 095 199 person years of observation (rate of 14.6 per 10 000 person years). The validation cohort included 6913 incident cases from 4 632 694 person years of observation (14.9 per 10 000 person years). Independent predictors included in the final model for men and women were age, body mass index, smoking status, varicose veins, congestive cardiac failure, chronic renal disease, cancer, chronic obstructive pulmonary disease, inflammatory bowel disease, hospital admission in past six months, and current prescriptions for antipsychotic drugs. We also included oral contraceptives, tamoxifen, and hormone replacement therapy in the final model for women. The risk prediction equation explained 33% of the variation in women and 34% in men in the validation cohort evaluated at 5 years The D statistic was 1.43 for women and 1.45 for men. The receiver operating curve statistic was 0.75 for both sexes. The model was well calibrated.

Conclusions We have developed and validated a new risk prediction model that quantifies absolute risk of thrombosis at 1 and 5 years. It can help identify patients at high risk of venous thromboembolism for prevention. The algorithm is based on simple clinical variables which the patient is likely to know or which are routinely recorded in general practice records. The algorithm could be integrated into general practice clinical computer systems and used to risk assess patients before hospital admission or starting medication which might increase the risk of venous thromboembolism.


  • We thank the general practices who contributed to the QResearch database, and EMIS for their expertise in establishing, developing, and supporting the database.

  • Funding: There was no external funding for the majority of the work undertaken on this project, which JHC and CC undertook in their ClinRisk roles. This analysis arose from a study of risks and benefits of hormone replacement therapy which was originally funded by David Stables (medical director of EMIS) in 2008, since venous thromboembolism was one of the outcomes.

  • Competing interests: All authors have completed the Unified Competing Interest form at (available on request from the corresponding author) and declare: no support from any additional organisation for the submitted work; JHC is professor of clinical epidemiology at the University of Nottingham and unpaid director of QResearch, a not-for-profit organisation which is a joint partnership between the University of Nottingham and EMIS (commercial IT supplier for 60% of general practices in the UK). JHC is also a paid director of ClinRisk Limited, which produces open and closed source software to ensure the reliable and updatable implementation of clinical risk algorithms within clinical computer systems to help improve patient care. CC is associate professor of medical statistics at the University of Nottingham and a paid consultant statistician for ClinRisk Limited; no other relationships or activities that could appear to have influenced the submitted work.

  • Ethical approval: The project has been independently reviewed in accordance with the QResearch agreement with Trent multicentre ethics committee.

  • 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.

  • Data sharing: The algorithms presented in this study will be released as open source software under the GNU Lesser General Public Licence version 3. The open source software allows use by anyone without charge under the terms of the GNU lesser General Public License version 3. Closed source software can be licensed at a fee.

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