Shi M, Zhai GQ. Models for Predicting Early Death in Patients With Stage IV Esophageal Cancer: A Surveillance, Epidemiology, and End Results-Based Cohort Study.
Cancer Control 2022;
29:10732748211072976. [PMID:
35037487 PMCID:
PMC8777366 DOI:
10.1177/10732748211072976]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background
Despite enormous progress in the stage IV esophageal cancer (EC) treatment,
some patients experience early death after diagnosis. This study aimed to
identify the early death risk factors and construct models for predicting
early death in stage IV EC patients.
Methods
Stage IV EC patients diagnosed between 2010 and 2015 in the Surveillance,
Epidemiology, and End Results (SEER) database were selected. Early death was
defined as death within 3 months of diagnosis, with or without therapy.
Early death risk factors were identified using logistic regression analyses
and further used to construct predictive models. The concordance index
(C-index), calibration curves, and decision curve analyses (DCA) were used
to assess model performance.
Results
Out of 4411 patients enrolled, 1779 died within 3 months. Histologic grade,
therapy, the status of the bone, liver, brain and lung metastasis, marriage,
and insurance were independent factors for early death in stage IV EC
patients. Histologic grade and the status of the bone and liver metastases
were independent factors for early death in both chemoradiotherapy and
untreated groups. Based on these variables, predictive models were
constructed. The C-index was .613 (95% confidence interval (CI),
[.573–.653]) and .635 (95% CI, [.596–.674]) in the chemoradiotherapy and
untreated groups, respectively, while calibration curves and DCA showed
moderate performance.
Conclusions
More than 40% of stage IV EC patients suffered from an early death. The
models could help clinicians discriminate between low and high risks of
early death and strategize individually-tailed therapeutic interventions in
stage IV EC patients.
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