Development and validation of risk prediction algorithm (QThrombosis) to estimate future risk of venous...

To derive and validate a new clinical risk prediction algorithm (QThrombosis, www.qthrombosis.org) to estimate individual patients' risk of venous thromboembolism.

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.

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

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.

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.

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.