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Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Pabinger, I; van Es, N; Heinze, G; Posch, F; Riedl, J; Reitter, EM; Di Nisio, M; Cesarman-Maus, G; Kraaijpoel, N; Zielinski, CC; Büller, HR; Ay, C.
A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts.
Lancet Haematol. 2018;
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Autor/innen der Med Uni Graz:
Posch Florian
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Abstract:
Venous thromboembolism is a common complication of cancer, but the risk of developing venous thromboembolism varies greatly among individuals and depends on numerous factors, including type of cancer. We aimed to develop and externally validate a clinical prediction model for cancer-associated venous thromboembolism. We used data from the prospective Vienna Cancer and Thrombosis Study (CATS) cohort (n=1423) to select prognostic variables for inclusion in the model. We then validated the model in the prospective Multinational Cohort Study to Identify Cancer Patients at High Risk of Venous Thromboembolism (MICA) cohort (n=832). We calculated c-indices to show how the predicted incidence of objectively confirmed venous thromboembolism at 6 months compared with the cumulative 6-month incidences observed in both cohorts. Two variables were selected for inclusion in the final clinical prediction model: tumour-site risk category (low or intermediate vs high vs very high) and continuous D-dimer concentrations. The multivariable subdistribution hazard ratios were 1·96 (95% CI 1·41-2·72; p=0·0001) for high or very high versus low or intermediate and 1·32 (95% CI 1·12-1·56; p=0·001) per doubling of D-dimer concentration. The cross-validated c-indices of the final model were 0·66 (95% CI 0·63-0·67) in CATS and 0·68 (0·62-0·74) in MICA. The clinical prediction model was adequately calibrated in both cohorts. An externally validated clinical prediction model incorporating only one clinical factor (tumour-site category) and one biomarker (D-dimer) predicted the risk of venous thromboembolism in ambulatory patients with solid cancers. This simple model is a considerable improvement on previous models for predicting cancer-associated venous thromboembolism, and could aid physicians in selection of patients who will likely benefit from thromboprophylaxis. Austrian Science Fund, Austrian National Bank Memorial Fund, and participating hospitals. Copyright © 2018 Elsevier Ltd. All rights reserved.

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