Format

Send to

Choose Destination
Int Angiol. 2019 Jun;38(3):173-184. doi: 10.23736/S0392-9590.19.04110-5. Epub 2019 May 16.

Prediction of early mortality in patients with cancer-associated thrombosis in the RIETE Database.

Collaborators (168)

Adarraga MD, Aibar MÁ, Aibar J, Amado C, Aranda C, Arcelus JI, Ballaz A, Barba R, Barillari G, Barrón M, Barrón-Andrés B, Bascuñana J, Benzidia I, Bertoletti L, Blanco-Molina Á, Bilora F, Bortoluzzi C, Bosevski M, Bounameaux H, Braester A, Brandolin B, Brenner B, Bucherini E, Bui HM, Bura-Riviere A, Camón AM, Caprini J, Carrasco C, Castro J, Cesta A, Ciammaichella M, Cruz JA, de Ancos C, Del Toro J, Demelo P, Dentali F, Del Carmen Díaz-Pedroche M, Díaz-Peromingo JA, Di Micco P, Di Pangrazio M, Ellis M, Encabo M, Falgá C, Falvo N, Farfán AI, Farge-Bancel D, Fernández-Capitán C, Fidalgo MÁ, Font C, Font L, Fresa M, Furest I, García MA, García-Bragado F, García-Morillo M, García-Raso A, Gavín O, Gil-Díaz A, Gómez V, Gómez-Cuervo C, González-Martínez J, Grandone E, Grau E, Guijarro R, Gutiérrez J, Gutiérrez P, Hernández-Blasco L, Hij A, Hirmerova J, Imbalzano E, Jara-Palomares L, Jaras MJ, Jiménez D, Jou I, Dolores Joya M, Krstevski G, Lessiani G, Lima J, Llamas P, Lobo JL, López-Jiménez L, López-Miguel P, López-Nuñez JJ, López-Reyes R, Bosco López-Sáez J, Lorente MA, Lorenzo A, Loring M, Lumbierres M, Mahé I, Maida R, Malý R, Manrique-Abos I, Javier Marchena P, Martín-Fernández M, Martín-Guerra JM, Martín-Romero M, Mastroiacovo D, Mazzolai L, Mellado M, Merah A, Monreal M, Del Valle Morales M, Moustafa F, Ney B, Nieto JA, Núñez MJ, Del Carmen Olivares M, Otalora S, Otero R, Pace F, Parisi R, Pedrajas JM, Pellejero G, Pérez-Ductor C, Pérez-Jacoiste A, Pérez-Rus G, Peris ML, Pesavento R, Pesce ML, Porras JA, Prandoni P, Quintavalla R, Riesco D, Rivas A, Rocci A, Rodríguez-Dávila MÁ, Rodríguez-Hernández A, Rosa V, Rubio CM, Ruiz-Alcaraz S, Ruiz-Artacho P, Ruiz-Ruiz J, Ruiz-Sada P, Ruiz-Torregrosa P, Sahuquillo JC, Sala-Sainz C, Salgado E, Sampériz Á, Sánchez-Muñoz-Torrero JF, Sancho T, Dhayana Sanoja I, Siniscalchi C, Skride A, Soler S, Sotgiu P, Soto MJ, Suriñach JM, Tafur A, Tiraferri E, Tonello D, Torres MI, Trujillo-Santos J, Tufano A, Tzoran I, Uresandi F, Usandizaga E, Valle R, Vanassche T, Vandenbriele C, Vázquez FJ, Verhamme P, Vilaseca A, Villalobos A, Visonà A, Vo Hong N, Hyung Bok Yoo H, Zdraveska M.

Author information

1
Department of Hematology Oncology, Mayo Clinic, Rochester, MN, USA - fuentes-bayne.harry@mayo.edu.
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.

Abstract

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.

PMID:
31112023
DOI:
10.23736/S0392-9590.19.04110-5
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Minerva Medica
Loading ...
Support Center