Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP)
Background - Recurrent venous thromboembolism (VTE) is common. Current guidelines suggest that patients with unprovoked VTE should continue anticoagulants unless they have a high bleeding risk, whereas all others can stop. Prediction models may refine this dichotomous distinction, but existing model...
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Public Library of Science
2019
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Online Access: | https://hdl.handle.net/10037/17705 https://doi.org/10.1371/journal.pmed.1002883 |
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University of Tromsø: Munin Open Research Archive |
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English |
topic |
VDP::Medical disciplines: 700 VDP::Medisinske Fag: 700 |
spellingShingle |
VDP::Medical disciplines: 700 VDP::Medisinske Fag: 700 Timp, Jasmijn F. Brækkan, Sigrid Kufaas Lijfering, Willem M. van Hylckama Vlieg, Astrid Hansen, John-Bjarne Rosendaal, Frits Richard le Cessie, Saskia Cannegieter, Suzanne C. Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
topic_facet |
VDP::Medical disciplines: 700 VDP::Medisinske Fag: 700 |
description |
Background - Recurrent venous thromboembolism (VTE) is common. Current guidelines suggest that patients with unprovoked VTE should continue anticoagulants unless they have a high bleeding risk, whereas all others can stop. Prediction models may refine this dichotomous distinction, but existing models apply only to patients with unprovoked first thrombosis. We aimed to develop a prediction model for all patients with first VTE, either provoked or unprovoked. Methods and findings - Data were used from two population-based cohorts of patients with first VTE from the Netherlands (Multiple Environment and Genetic Assessment of Risk Factors for Venous Thrombosis [MEGA] follow-up study, performed from 1994 to 2009; model derivation; n = 3,750) and from Norway (Tromsø study, performed from 1999 to 2016; model validation; n = 663). Four versions of a VTE prediction model were developed: model A (clinical, laboratory, and genetic variables), model B (clinical variables and fewer laboratory markers), model C (clinical and genetic factors), and model D (clinical variables only). The outcome measure was recurrent VTE. To determine the discriminatory power, Harrell’s C-statistic was calculated. A prognostic score was assessed for each patient. Kaplan-Meier plots for the observed recurrence risks were created in quintiles of the prognostic scores. For each patient, the 2-year predicted recurrence risk was calculated. Models C and D were validated in the Tromsø study. During 19,201 person-years of follow-up (median duration 5.7 years) in the MEGA study, 507 recurrences occurred. Model A had the highest predictive capability, with a C-statistic of 0.73 (95% CI 0.71–0.76). The discriminative performance was somewhat lower in the other models, with C-statistics of 0.72 for model B, 0.70 for model C, and 0.69 for model D. Internal validation showed a minimal degree of optimism bias. Models C and D were externally validated, with C-statistics of 0.64 (95% CI 0.62–0.66) and 0.65 (95% CI 0.63–0.66), respectively. According to model C, in 2,592 patients with provoked first events, 367 (15%) patients had a predicted 2-year risk of >10%, whereas in 1,082 patients whose first event was unprovoked, 484 (45%) had a predicted 2-year risk of <10%. A limitation of both cohorts is that laboratory measurements were missing in a substantial proportion of patients, which therefore were imputed. Conclusions - The prediction model we propose applies to patients with provoked or unprovoked first VTE—except for patients with (a history of) cancer—allows refined risk stratification, and is easily usable. For optimal individualized treatment, a management study in which bleeding risks are also taken into account is necessary. |
format |
Article in Journal/Newspaper |
author |
Timp, Jasmijn F. Brækkan, Sigrid Kufaas Lijfering, Willem M. van Hylckama Vlieg, Astrid Hansen, John-Bjarne Rosendaal, Frits Richard le Cessie, Saskia Cannegieter, Suzanne C. |
author_facet |
Timp, Jasmijn F. Brækkan, Sigrid Kufaas Lijfering, Willem M. van Hylckama Vlieg, Astrid Hansen, John-Bjarne Rosendaal, Frits Richard le Cessie, Saskia Cannegieter, Suzanne C. |
author_sort |
Timp, Jasmijn F. |
title |
Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
title_short |
Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
title_full |
Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
title_fullStr |
Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
title_full_unstemmed |
Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) |
title_sort |
prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: the leiden thrombosis recurrence risk prediction model (l-trrip) |
publisher |
Public Library of Science |
publishDate |
2019 |
url |
https://hdl.handle.net/10037/17705 https://doi.org/10.1371/journal.pmed.1002883 |
long_lat |
ENVELOPE(-45.900,-45.900,-60.633,-60.633) |
geographic |
Meier Norway Tromsø |
geographic_facet |
Meier Norway Tromsø |
genre |
Tromsø |
genre_facet |
Tromsø |
op_relation |
Nature Methods Timp JF, Brækkan SK, Lijfering WM, van Hylckama Vlieg A, Hansen JB, Rosendaal FR, le Cessie S, Cannegieter SC. Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP). Nature Methods. 2019;16:e1002883(10):1-22 FRIDAID 1743446 doi:10.1371/journal.pmed.1002883 1548-7091 1548-7105 https://hdl.handle.net/10037/17705 |
op_rights |
openAccess Copyright 2019 The Author(s) |
op_doi |
https://doi.org/10.1371/journal.pmed.1002883 |
container_title |
PLOS Medicine |
container_volume |
16 |
container_issue |
10 |
container_start_page |
e1002883 |
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1766219506397478912 |
spelling |
ftunivtroemsoe:oai:munin.uit.no:10037/17705 2023-05-15T18:34:39+02:00 Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP) Timp, Jasmijn F. Brækkan, Sigrid Kufaas Lijfering, Willem M. van Hylckama Vlieg, Astrid Hansen, John-Bjarne Rosendaal, Frits Richard le Cessie, Saskia Cannegieter, Suzanne C. 2019-10-11 https://hdl.handle.net/10037/17705 https://doi.org/10.1371/journal.pmed.1002883 eng eng Public Library of Science Nature Methods Timp JF, Brækkan SK, Lijfering WM, van Hylckama Vlieg A, Hansen JB, Rosendaal FR, le Cessie S, Cannegieter SC. Prediction of recurrent venous thrombosis in all patients with a first venous thrombotic event: The Leiden Thrombosis Recurrence Risk Prediction model (L-TRRiP). Nature Methods. 2019;16:e1002883(10):1-22 FRIDAID 1743446 doi:10.1371/journal.pmed.1002883 1548-7091 1548-7105 https://hdl.handle.net/10037/17705 openAccess Copyright 2019 The Author(s) VDP::Medical disciplines: 700 VDP::Medisinske Fag: 700 Journal article Tidsskriftartikkel Peer reviewed publishedVersion 2019 ftunivtroemsoe https://doi.org/10.1371/journal.pmed.1002883 2021-06-25T17:57:06Z Background - Recurrent venous thromboembolism (VTE) is common. Current guidelines suggest that patients with unprovoked VTE should continue anticoagulants unless they have a high bleeding risk, whereas all others can stop. Prediction models may refine this dichotomous distinction, but existing models apply only to patients with unprovoked first thrombosis. We aimed to develop a prediction model for all patients with first VTE, either provoked or unprovoked. Methods and findings - Data were used from two population-based cohorts of patients with first VTE from the Netherlands (Multiple Environment and Genetic Assessment of Risk Factors for Venous Thrombosis [MEGA] follow-up study, performed from 1994 to 2009; model derivation; n = 3,750) and from Norway (Tromsø study, performed from 1999 to 2016; model validation; n = 663). Four versions of a VTE prediction model were developed: model A (clinical, laboratory, and genetic variables), model B (clinical variables and fewer laboratory markers), model C (clinical and genetic factors), and model D (clinical variables only). The outcome measure was recurrent VTE. To determine the discriminatory power, Harrell’s C-statistic was calculated. A prognostic score was assessed for each patient. Kaplan-Meier plots for the observed recurrence risks were created in quintiles of the prognostic scores. For each patient, the 2-year predicted recurrence risk was calculated. Models C and D were validated in the Tromsø study. During 19,201 person-years of follow-up (median duration 5.7 years) in the MEGA study, 507 recurrences occurred. Model A had the highest predictive capability, with a C-statistic of 0.73 (95% CI 0.71–0.76). The discriminative performance was somewhat lower in the other models, with C-statistics of 0.72 for model B, 0.70 for model C, and 0.69 for model D. Internal validation showed a minimal degree of optimism bias. Models C and D were externally validated, with C-statistics of 0.64 (95% CI 0.62–0.66) and 0.65 (95% CI 0.63–0.66), respectively. According to model C, in 2,592 patients with provoked first events, 367 (15%) patients had a predicted 2-year risk of >10%, whereas in 1,082 patients whose first event was unprovoked, 484 (45%) had a predicted 2-year risk of <10%. A limitation of both cohorts is that laboratory measurements were missing in a substantial proportion of patients, which therefore were imputed. Conclusions - The prediction model we propose applies to patients with provoked or unprovoked first VTE—except for patients with (a history of) cancer—allows refined risk stratification, and is easily usable. For optimal individualized treatment, a management study in which bleeding risks are also taken into account is necessary. Article in Journal/Newspaper Tromsø University of Tromsø: Munin Open Research Archive Meier ENVELOPE(-45.900,-45.900,-60.633,-60.633) Norway Tromsø PLOS Medicine 16 10 e1002883 |