Survival outcomes following treatment delays among patients with early-stage female cancers: a nationwide study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) severely hindered the timely receipt of health care for patients with cancer, especially female patients. Depression and anxiety were more pronounced in female patients than their male counterparts with cancer during treatment wait-time intervals. Herein, investigating the impact of treatment delays on the survival outcomes of female patients with early-stage cancers can enhance the rational and precise clinical decisions of physicians. METHODS: We analyzed five types of cancers in women from the Surveillance, Epidemiology, and End Results (SEER) program between Jan 2010 and Dec 2015. Univariate and multivariate Cox regression analyses were used to determine the impacts of treatment delays on the overall survival (OS) and cancer-specific survival (CSS) of the patients. RESULTS: A total of 241,661 females with early-stage cancer were analyzed (12,617 cases of non-small cell lung cancer (NSCLC), 166,051 cases of infiltrating breast cancer, 31,096 cases of differentiated thyroid cancer, 23,550 cases of colorectal cancer, and 8347 cases of cervical cancer). Worse OS rates were observed in patients with treatment delays ≥ 3 months in stage I NSCLC (adjustedHazard ratio (HR) = 1.11, 95% Confidence Interval (CI): 1.01-1.23, p = 0.044) and stage I infiltrating breast cancer (adjustedHR = 1.23, 95% CI 1.11-1.37, p < 0.001). When the treatment delay intervals were analyzed as continuous variables, similar results were observed in patients with stage I NSCLC (adjustedHR = 1.04, 95% CI 1.01-1.06, p = 0.010) and in those with stage I breast cancer (adjustedHR = 1.03, 95% CI 1.00-1.06, p = 0.029). However, treatment delays did not reduce the OS of patients with differentiated thyroid cancer, cervical cancer, or colorectal cancer in the early-stage. Only intermediate treatment delays impaired the CSS of patients with cervical cancer in stage I (adjustedHR = 1.31, 95% CI 1.02-1.68, p = 0.032). CONCLUSION: After adjusting for confounders, the prolonged time from diagnosis to the initiation of treatment (< 6 months) showed limited negative effects on the survival of most of the patients with early-stage female cancers. Whether our findings serve as evidence supporting the treatment deferral decisions of clinicians for patients with different cancers in resource-limited situations needs further validation.

Association of active immunotherapy with outcomes in cancer patients with COVID-19: a systematic review and meta-analysis.

BACKGROUND: During the COVID-19 pandemic, there are growing concerns about the safety of administering immunotherapy in cancer patients with COVID-19. However, current clinical guidelines provided no clear recommendation. METHODS: Studies were searched and retrieved from electronic databases. The meta-analysis was performed by employing the generic inverse-variance method. A random-effects model was used to calculate the unadjusted odds ratios (ORs) and adjusted ORs with the corresponding 95% CIs. RESULTS: This meta-analysis included 20 articles with 6,042 cancer patients diagnosed with COVID-19. According to the univariate analysis, the acceptance of immunotherapy within 30 days before COVID-19 diagnosis did not increase the mortality of cancer patients (OR: 0.92; 95% CI: 0.68-1.25; P=0.61). Moreover, after adjusting for confounders, the adjusted OR for mortality was 0.51, with borderline significance (95% CI: 0.25-1.01; P=0.053). Similarly, the univariate analysis showed that the acceptance of immunotherapy within 30 days before COVID-19 diagnosis did not increase the risk of severe/critical disease in cancer patients (OR: 1.07; 95% CI: 0.78-1.47; P=0.66). No significant between-study heterogeneity was found in these analyses. CONCLUSIONS: Accepting immunotherapy within 30 days before the diagnosis of COVID-19 was not significantly associated with a higher risk of mortality or severe/critical disease of infected cancer patients. Further prospectively designed studies with large sample sizes are required to evaluate the present results.

Associations of sleep and circadian phenotypes with COVID-19 susceptibility and hospitalization: an observational cohort study based on the UK Biobank and a two-sample Mendelian randomization study.

STUDY OBJECTIVES: Sleep and circadian phenotypes are associated with several diseases. The present study aimed to investigate whether sleep and circadian phenotypes were causally linked with coronavirus disease 2019 (COVID-19)-related outcomes. METHODS: Habitual sleep duration, insomnia, excessive daytime sleepiness, daytime napping, and chronotype were selected as exposures. Key outcomes included positivity and hospitalization for COVID-19. In the observation cohort study, multivariable risk ratios (RRs) and their 95% confidence intervals (CIs) were calculated. Two-sample Mendelian randomization (MR) analyses were conducted to estimate the causal effects of the significant findings in the observation analyses. Odds ratios (ORs) and the corresponding 95% CIs were calculated and compared using the inverse variance weighting, weighted median, and MR-Egger methods. RESULTS: In the UK Biobank cohort study, both often excessive daytime sleepiness and sometimes daytime napping were associated with hospitalized COVID-19 (excessive daytime sleepiness [often vs. never]: RR = 1.24, 95% CI = 1.02-1.5; daytime napping [sometimes vs. never]: RR = 1.12, 95% CI = 1.02-1.22). In addition, sometimes daytime napping was also associated with an increased risk of COVID-19 susceptibility (sometimes vs. never: RR = 1.04, 95% CI = 1.01-1.28). In the MR analyses, excessive daytime sleepiness was found to increase the risk of hospitalized COVID-19 (MR IVW method: OR = 4.53, 95% CI = 1.04-19.82), whereas little evidence supported a causal link between daytime napping and COVID-19 outcomes. CONCLUSIONS: Observational and genetic evidence supports a potential causal link between excessive daytime sleepiness and an increased risk of COVID-19 hospitalization, suggesting that interventions targeting excessive daytime sleepiness symptoms might decrease severe COVID-19 rate.