Ingrid Pabinger1, Nick van Es2, Georg Heinze3, Florian Posch4, Julia Riedl5, Eva-Maria Reitter5, Marcello Di Nisio6, Gabriela Cesarman-Maus7, Noémie Kraaijpoel2, Christoph Carl Zielinski8, Harry Roger Büller2, Cihan Ay5. 1. Clinical Division of Haematology and Haemostaseology, Medical University of Vienna, Vienna, Austria. Electronic address: ingrid.pabinger@meduniwien.ac.at. 2. Department of Vascular Medicine, Academic Medical Centre Amsterdam, University of Amsterdam, Amsterdam, Netherlands. 3. Department of Medicine I, and Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria. 4. Clinical Division of Haematology and Haemostaseology, Medical University of Vienna, Vienna, Austria; Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. 5. Clinical Division of Haematology and Haemostaseology, Medical University of Vienna, Vienna, Austria. 6. Department of Medicine and Ageing Sciences, G D'Annunzio University, Chieti, Italy. 7. Department of Hematology, National Cancer Institute Mexico, Mexico City, Mexico. 8. Clinical Division of Oncology, Medical University of Vienna, Vienna, Austria.
Abstract
BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: Austrian Science Fund, Austrian National Bank Memorial Fund, and participating hospitals.
BACKGROUND: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. METHODS: 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 CancerPatients 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: Austrian Science Fund, Austrian National Bank Memorial Fund, and participating hospitals.
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