PB1319 - Management of superficial venous thrombosis based on individual risk profiles: protocol for the development and validation of three prognostic prediction models in large primary care cohorts

Background: Superficial venous thrombosis (SVT) is considered a benign thrombotic condition in most patients. However, it can also cause serious complications such as clot progression to deep venous thrombosis (DVT) and pulmonary embolism (PE). Although most SVT patients are encountered in primary healthcare, studies on SVT nearly all were focused on patients seen in the hospital setting.

Aims: To describe the protocol for the development and external validation of three prognostic prediction models for relevant clinical outcomes in SVT patients seen in primary care: (i) prolonged (painful) symptoms within 14 days since SVT diagnosis, (ii) for clot progression to DVT or PE within 45 days, and (iii) for clot recurrence within 12 months.

Methods: Data will be used from four primary care routine healthcare registries from both the Netherlands and the UK; one will be used for the development of the prediction models and the remaining three will be used as external validation cohorts. The study population will consist of patients ≥18 years with a diagnosis of SVT. Selection of SVT cases will be based on a combination of ICPC/READ/Snowmed coding and free text clinical symptoms.

Results: Predictors considered are sex, age, BMI, clinical SVT characteristics, and co-morbidities including: (history of any) cardiovascular disease, diabetes, autoimmune disease, malignancy, thrombophilia, pregnancy and presence of varicose veins. The prediction models will be developed using multivariable logistic regression analysis techniques for model i and ii and for model iii a Cox proportional hazards model will be used. They will be validated by internal-external cross-validation as well as external validation.

Conclusion(s): There are currently no prediction models available for predicting the risk of serious complications for SVT patients presenting in primary care settings. The described prediction models should help identify patients at highest risk for complications and support clinical decision-making for this understudied disorder.