AUTHOR=Yuan Hai-Liang , Zhang Xiang , Li Yan , Guan Qing , Chu Wei-Wei , Yu Hai-Ping , Liu Lian , Zheng Yun-Quan , Lu Jing-Jing
TITLE=A Nomogram for Predicting Risk of Thromboembolism in Gastric Cancer Patients Receiving Chemotherapy
JOURNAL=Frontiers in Oncology
VOLUME=11
YEAR=2021
URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.598116
DOI=10.3389/fonc.2021.598116
ISSN=2234-943X
ABSTRACT=Purpose: The aims of this study were to develop and validate a novel nomogram to predict thromboembolism (TE) in gastric cancer (GC) patients receiving chemotherapy and to test its predictive ability.
Methods: This retrospective study included 544 GC patients who received chemotherapy as the initial treatment at two medical centers. Among the 544 GC patients who received chemotherapy, 275 and 137 patients in the First Affiliated Hospital of Nanchang University from January 2014 to March 2019 were enrolled in the training cohort and the validation cohort, respectively. A total of 132 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University from January 2015 to August 2019 were enrolled in external validation cohorts. The nomogram was based on parameters determined by univariate and multivariate logistic analyses. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and decision curve analysis (DCA). The applicability of the nomogram was internally and independently validated.
Results: The predictors included the Eastern Cooperative Oncology Group Performance Status (ECOG), presence of an active cancer (AC), central venous catheter (CVC), and D-dimer levels. These risk factors are shown on the nomogram and verified. The nomogram demonstrated good discrimination and fine calibration with an AUROC of 0.875 (0.832 in internal validation and 0.807 in independent validation). The DCA revealed that the nomogram had a high clinical application value.
Conclusions: We propose the nomogram for predicting TE in patients with GC receiving chemotherapy, which can help in making timely personalized clinical decisions for different risk populations.