Thromb Haemost 2019; 119(11): 1849-1859
DOI: 10.1055/s-0039-1694012
Stroke, Systemic or Venous Thromboembolism
Georg Thieme Verlag KG Stuttgart · New York

Multistate Models: Accurate and Dynamic Methods to Improve Predictions of Thrombotic Risk in Patients with Cancer

Alberto Carmona-Bayonas
1  Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain
,
Paula Jimenez-Fonseca
2  Medical Oncology Department, Hospital Universitario Central de Asturias, Oviedo, Spain
,
Marcelo Garrido
3  Medical Oncology Department, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile
,
Ana Custodio
4  Medical Oncology Department, Hospital Universitario La Paz, Madrid, Spain
,
Raquel Hernandez
5  Medical Oncology Department, Hospital Universitario de Canarias, Tenerife, Spain
,
Alejandra Lacalle
6  Medical Oncology Department, Complejo Hospitalario de Navarra, Pamplona, Spain
,
Juana María Cano
7  Medical Oncology Department, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
,
Gema Aguado
8  Medical Oncology Department, Hospital Universitario Gregorio Marañon, Madrid, Spain
,
Eva Martínez de Castro
9  Medical Oncology Department, Hospital Universitario Marqués de Valdecilla, Santander, Spain
,
Felipe Alvarez Manceñido
10  Pharmacy Department, Hospital Universitario Central de Asturias, Oviedo, Spain
,
Ismael Macias
11  Medical Oncology Department, Hospital Universitario Parc Tauli, Sabadell, Spain
,
Laura Visa
12  Medical Oncology Department, Hospital Universitario El Mar, Barcelona, Spain
,
Marta Martín Richard
13  Medical Oncology Department, Hospital Universitario Santa Creu i Sant Pau, Barcelona, Spain
,
Monserrat Mangas
14  Medical Oncology Department, Hospital Galdakao-Usansolo, Galdakao-Usansolo, Spain
,
Manuel Sánchez Cánovas
1  Hematology and Medical Oncology Department, Hospital General Universitario Morales Meseguer, University of Murcia, IMIB, Murcia, Spain
,
Federico Longo
15  Medical Oncology Department, Hospital Universitario Ramón y Cajal, Madrid, Spain
,
Leticia Iglesias Rey
16  Medical Oncology Department, Complejo Hospitalario de Orense, Orense, Spain
,
Nieves Martínez Lago
17  Medical Oncology Department, Complejo Hospitalario Universitario de A Coruña, La Coruña, Spain
,
Alfonso Martín Carnicero
18  Medical Oncology Department, Hospital San Pedro, Logroño, Spain
,
Ana Sánchez
19  Medical Oncology Department, Hospital Universitario Doce de Octubre, Madrid, Spain
,
Aitor Azkárate
20  Medical Oncology Department, Hospital Universitario Son Espases, Mallorca, Spain
,
María Luisa Limón
21  Medical Oncology Department, Hospital Universitario Virgen del Rocío, Sevilla, Spain
,
Carolina Hernández Pérez
22  Medical Oncology Department, Hospital Universitario Nuestra Señora de la Candelaria, Tenerife, Spain
,
Avinash Ramchandani
23  Medical Oncology Department, Hospital Universitario Insular de Gran Canaria, Las Palmas de Gran Canaria, Spain
,
Paola Pimentel
24  Medical Oncology Department, Hospital Santa Lucia, Cartagena, Spain
,
Paula Cerdá
25  Medical Oncology Department, Centro Médico Teknon, Barcelona, Spain
,
Raquel Serrano
26  Medical Oncology Department, Hospital Universitario Reina Sofía, Córdoba, Spain
,
Aitziber Gil-Negrete
27  Medical Oncology Department, Hospital Universitario Donostia, San Sebastián, Spain
,
Miguel Marín
28  Medical Oncology Department, Hospital Universitario Virgen de la Arrixaca, Murcia, Spain
,
Alicia Hurtado
29  Medical Oncology Department, Hospital Universitario Fundación Alcorcón, Madrid, Spain
,
Rodrigo Sánchez Bayona
30  Medical Oncology Department, Clínica Universidad de Navarra, Pamplona, Spain
,
Javier Gallego
31  Medical Oncology Department, Hospital General Universitario de Elche, Elche, Spain
› Author Affiliations
Funding None.
Further Information

Publication History

13 April 2019

21 June 2019

Publication Date:
28 August 2019 (online)

Abstract

Research into cancer-associated thrombosis (CAT) entails managing dynamic data that pose an analytical challenge. Thus, methods that assume proportional hazards to investigate prognosis entail a risk of misinterpreting or overlooking key traits or time-varying effects. We examined the AGAMENON registry, which collects data from 2,129 patients with advanced gastric cancer. An accelerated failure time (AFT) multistate model and flexible competing risks regression were used to scrutinize the time-varying effect of CAT, as well as to estimate how covariates dynamically predict cumulative incidence. The AFT model revealed that thrombosis shortened progression-free survival and overall survival with adjusted time ratios of 0.72 and 0.56, respectively. Nevertheless, its prognostic effect was nonproportional and disappeared over time if the subject managed to survive long enough. CAT that occurred later had a more pronounced prognostic effect. In the flexible competing risks model, multiple covariates were seen to have significant time-varying effects on the cumulative incidence of CAT (Khorana score, secondary thromboprophylaxis, high tumor burden, and cisplatin-containing regimen), whereas other predictors exerted a constant effect (signet ring cells and primary thromboprophylaxis). The model that assumes proportional hazards was incapable of capturing the effect of these covariates and predicted the cumulative incidence in a biased way. This study evinces that flexible and multistate models are a useful and innovative method to describe the dynamic effect of variables associated with CAT and should be more widely used.

Ethical Approval

All procedures followed were in accordance with the ethical standards of the (institutional and national) committee responsible for human experimentation and with the Helsinki Declaration of 1964 and later versions. Informed consent was obtained from all patients before they were included in the study.


Supplementary Material