The influence of marital status at diagnosis on survival of adult patients with mantle cell lymphoma

Purpose Marital status has been reported to influence the survival outcomes of various cancers, but its impact on patients with mantle cell lymphoma (MCL) remains unclear. This study aimed to assess the influence of marital status at diagnosis on overall survival (OS) and cancer-specific survival (CSS) in patients with MCL. Methods The study utilized data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER)-18 databases, including 6437 eligible individuals diagnosed with MCL from 2000 to 2018. A 1:1 propensity matching method (PSM) minimized confounding factor. Univariate and multivariate analyses determined hazard ratios (HR). Stratified hazard models were developed for married and unmarried statuses across time intervals. Results Married patients exhibited better 5-year OS and CSS rates compared to unmarried patients (54.2% vs. 39.7%, log-rank p < 0.001; 62.6% vs. 49.3%, log-rank p < 0.001). Multivariate analysis indicated that being unmarried was an independent risk factor for OS (adjusted HR 1.420, 95% CI 1.329-1.517) and CSS (adjusted HR 1.388, 95% CI 1.286-1.498). After PSM, being unmarried remained an independent risk factor for both OS and CSS. Among unmarried patients, widowed individuals exhibited the poorest survival outcomes compared to patients with other marital statuses, with 5-year OS and CSS rates of 28.5% and 41.0%, respectively. Furthermore, in the 10-year OS and CSS hazard model for widowed individuals had a significantly higher risk of mortality, with the probability of overall and cancer-specific mortality increased by 1.7-fold and 1.6-fold, respectively. Conclusion Marital status at diagnosis is an independent prognostic factor for MCL patients, with widowed individuals showing worse OS and CSS than those who are married, single, or divorced/separated. Adequate psychological and social support for widowed patients is crucial for improving outcomes in this patient population.