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ORIGINAL ARTICLE VENOUS DISEASE Editor’s choice • Free access
International Angiology 2019 June;38(3):173-84
DOI: 10.23736/S0392-9590.19.04110-5
Copyright © 2019 EDIZIONI MINERVA MEDICA
language: English
Prediction of early mortality in patients with cancer-associated thrombosis in the RIETE Database
Harry E. FUENTES 1 ✉, Alfonso J. TAFUR 2, 3, Joseph A. CAPRINI 3, 4, Adriano ALATRI 5, Javier TRUJILLO-SANTOS 6, Dominique FARGE-BANCEL 7, Vladimir ROSA 8, Llorenç FONT 9, Alicia VILASECA 10, Manuel MONREAL 11 RIETE Investigators 
1 Department of Hematology Oncology, Mayo Clinic, Rochester, MN, USA; 2 Division of Vascular Medicine, Department of Internal Medicine Cardiology, NorthShore University HealthSystem, Evanston, IL, USA; 3 Pritzker School of Medicine, University of Chicago, Chicago, IL, USA; 4 NorthShore University, HealthSystem-Emeritus, Evanston, IL, USA; 5 Division of Angiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland; 6 Department of Internal Medicine, Hospital General Universitario Santa Lucía, Murcia, Spain; 7 Department of Internal Medicine and Pathology, Saint-Louis Hospital, Paris, France; 8 Department of Internal Medicine, Virgen de Arrixaca University Hospital, Murcia, Spain; 9 Department of Hematology, Tortosa Verge de la Cinta Hospital, Tarragona, Spain; 10 Department of Hematology and Hemostasis, San Camilo Clinic, Buenos Aires, Argentina; 11 Department of Internal Medicine, Germans Trias i Pujol University Hospital, Badalona, Barcelona, Spain
BACKGROUND: Proper risk stratification of patients for early mortality after cancer-associated thrombosis may lead to personalized anticoagulation protocols. Therefore, we aimed to derive and validate a scoring system to predict early mortality in this population. To this end, we selected patients with active cancer and thrombosis from the Computerized Registry of Patients with Venous Thromboembolism database.
METHODS: The main outcome was all cause mortality within the month following a thrombotic event. We used a simple random selection to split are data in a derivation and a validation cohort. In the derivation cohort, we used recursive partitioning and binary logistic regression to identify groups at risk and to determine the likelihood of the primary outcome. The risk score was developed based on odds ratios from the final multivariate model, and then tested in the validation cohort.
RESULTS: In 10,025 eligible patients, we identified 6 predictors of 30-day mortality: leukocytosis ≥11.5x109/L; platelet count ≤160x109/L, metastasis, recent immobility, initial presentation as pulmonary embolism and Body Mass Index <18.5. The model divided the population into 3 risk categories: low (score 0-3), moderate (score 4-6), and high (score ≥7). The AUC for the overall score was 0.74, and using a cutoff ≥7 points, the model had a negative predictive value of 94.4%, a positive predictive value of 23.1%, a sensitivity of 73.3%, and a specificity of 64.6% in the validation cohort.
CONCLUSIONS: Our validated risk model may assist physicians in the selection of patients for outpatient management, and perhaps anticoagulant, considering expanding anticoagulation options.
KEY WORDS: Mortality, premature; Thrombosis; Neoplasms
