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Validated competing event model for the stage I-II endometrial cancer population.
- Author(s): Carmona, Ruben;
- Gulaya, Sachin;
- Murphy, James D;
- Rose, Brent S;
- Wu, John;
- Noticewala, Sonal;
- McHale, Michael T;
- Yashar, Catheryn M;
- Vaida, Florin;
- Mell, Loren K
- et al.
Published Web Locationhttps://doi.org/10.1016/j.ijrobp.2014.03.047
Purpose/objectives(s)Early-stage endometrial cancer patients are at higher risk of noncancer mortality than of cancer mortality. Competing event models incorporating comorbidity could help identify women most likely to benefit from treatment intensification.
Methods and materials67,397 women with stage I-II endometrioid adenocarcinoma after total hysterectomy diagnosed from 1988 to 2009 were identified in Surveillance, Epidemiology, and End Results (SEER) and linked SEER-Medicare databases. Using demographic and clinical information, including comorbidity, we sought to develop and validate a risk score to predict the incidence of competing mortality.
ResultsIn the validation cohort, increasing competing mortality risk score was associated with increased risk of noncancer mortality (subdistribution hazard ratio [SDHR], 1.92; 95% confidence interval [CI], 1.60-2.30) and decreased risk of endometrial cancer mortality (SDHR, 0.61; 95% CI, 0.55-0.78). Controlling for other variables, Charlson Comorbidity Index (CCI) = 1 (SDHR, 1.62; 95% CI, 1.45-1.82) and CCI >1 (SDHR, 3.31; 95% CI, 2.74-4.01) were associated with increased risk of noncancer mortality. The 10-year cumulative incidences of competing mortality within low-, medium-, and high-risk strata were 27.3% (95% CI, 25.2%-29.4%), 34.6% (95% CI, 32.5%-36.7%), and 50.3% (95% CI, 48.2%-52.6%), respectively. With increasing competing mortality risk score, we observed a significant decline in omega (ω), indicating a diminishing likelihood of benefit from treatment intensification.
ConclusionComorbidity and other factors influence the risk of competing mortality among patients with early-stage endometrial cancer. Competing event models could improve our ability to identify patients likely to benefit from treatment intensification.
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