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Ensor, Joie; Riley, Richard D.; Moore, David; Snell, Kym I. E.; Bayliss, Susan; Fitzmaurice, David A. (2016)
Publisher: BMJ
Journal: BMJ Open
Languages: English
Types: Article
Subjects: RC, Prediction model, Prognostic model, Recurrence risk, 1694, 1683, Evidence Based Practice, 1506, Venous thrombosis, Research, RA
OBJECTIVES: To review studies developing or validating a prognostic model for individual venous thromboembolism (VTE) recurrence risk following cessation of therapy for a first unprovoked VTE. Prediction of recurrence risk is crucial to informing patient prognosis and treatment decisions. The review aims to determine whether reliable prognostic models exist and, if not, what further research is needed within the field. DESIGN: Bibliographic databases (including MEDLINE, EMBASE and the Cochrane Library) were searched using index terms relating to the clinical field and prognosis. Screening of titles, abstracts and subsequently full texts was conducted by 2 reviewers independently using predefined criteria. Quality assessment and critical appraisal of included full texts was based on an early version of the PROBAST (Prediction study Risk Of Bias Assessment Tool) for risk of bias and applicability in prognostic model studies. SETTING: Studies in any setting were included. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome for the review was the predictive accuracy of identified prognostic models in relation to VTE recurrence risk. RESULTS: 3 unique prognostic models were identified including the HERDOO2 score, Vienna prediction model and DASH score. Quality assessment highlighted the Vienna, and DASH models were developed with generally strong methodology, but the HERDOO2 model had many methodological concerns. Further, all models were considered at least at moderate risk of bias, primarily due to the need for further external validation before use in practice. CONCLUSIONS: Although the Vienna model shows the most promise (based on strong development methodology, applicability and having some external validation), none of the models can be considered ready for use until further, external and robust validation is performed in new data. Any new models should consider the inclusion of predictors found to be consistently important in existing models (sex, site of index event, D-dimer), and take heed of several methodological issues identified through this review. PROSPERO REGISTRATION NUMBER: CRD42013003494.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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