Browsing by Author "Berry, Mark"

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    Cardiovascular comorbidities and survival of lung cancer patients: Medicare data based analysis.
    (Lung Cancer, 2017-06-05) Kravchenko, Julia; Berry, Mark; Arbeev, Konstantin; Lyerly, H Kim; Yashin, Anatoly; Akushevich, Igor
    OBJECTIVES: To evaluate the role of cardiovascular disease (CVD) comorbidity in survival of patients with non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: The impact of seven CVDs (at the time of NSCLC diagnosis and during subsequent follow-up) on overall survival was studied for NSCLC patients aged 65+ years using the Surveillance, Epidemiology, and End Results data linked to the U.S. Medicare data, cancer stage- and treatment-specific. Cox regression was applied to evaluate death hazard ratios of CVDs in univariable and multivariable analyses (controlling by age, TNM statuses, and 78 non-CVD comorbidities) and to investigate the effects of 128 different combinations of CVDs on patients' survival. RESULTS: Overall, 95,167 patients with stage I (n=29,836, 31.4%), II (n=5133, 5.4%), IIIA (n=11,884, 12.5%), IIIB (n=18,020, 18.9%), and IV (n=30,294, 31.8%) NSCLC were selected. Most CVDs increased the risk of death for stages I-IIIB patients, but did not significantly impact survival of stage IV patients. The worse survival of patients was associated with comorbid heart failure, myocardial infarction, and cardiac arrhythmias that occurred during a period of follow-up: HRs up to 1.85 (p<0.001), 1.96 (p<0.05), and 1.67 (p<0.001), respectively, varying by stage and treatment. The presence of hyperlipidemia at baseline (HR down to 0.71, p<0.05) was associated with better prognosis. Having multiple co-existing CVDs significantly increased mortality for all treatments, especially for stages I and II patients treated with surgery (HRs up to 2.89, p<0.05) and stages I-IIIB patients treated with chemotherapy (HRs up to 2.59, p<0.001) and chemotherapy and radiotherapy (HRs up to 2.20, p<0.001). CONCLUSION: CVDs impact the survival of NSCLC patients, particularly when multiple co-existing CVDs are present; the impacts vary by stage and treatment. This data should be considered in improving cancer treatment selection process for such potentially challenging patients as the elderly NSCLC patients with CVD comorbidities.
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    Causal effects of time-dependent treatments in older patients with non-small cell lung cancer.
    (PLoS One, 2015) Akushevich, Igor; Arbeev, Konstantin; Kravchenko, Julia; Berry, Mark
    BACKGROUND: Treatment selection for elderly patients with lung cancer must balance the benefits of curative/life-prolonging therapy and the risks of increased mortality due to comorbidities. Lung cancer trials generally exclude patients with comorbidities and current treatment guidelines do not specifically consider comorbidities, so treatment decisions are usually made on subjective individual-case basis. METHODS: Impacts of surgery, radiation, and chemotherapy mono-treatment as well as combined chemo/radiation on one-year overall survival (compared to no-treatment) are studied for stage-specific lung cancer in 65+ y.o. patients. Methods of causal inference such as propensity score with inverse probability weighting (IPW) for time-independent and marginal structural model (MSM) for time-dependent treatments are applied to SEER-Medicare data considering the presence of comorbid diseases. RESULTS: 122,822 patients with stage I (26.8%), II (4.5%), IIIa (11.5%), IIIb (19.9%), and IV (37.4%) lung cancer were selected. Younger age, smaller tumor size, and fewer baseline comorbidities predict better survival. Impacts of radio- and chemotherapy increased and impact of surgery decreased with more advanced cancer stages. The effects of all therapies became weaker after adjustment for selection bias, however, the changes in the effects were minor likely due to the weak selection bias or incompleteness of the list of predictors that impacted treatment choice. MSM provides more realistic estimates of treatment effects than the IPW approach for time-independent treatment. CONCLUSIONS: Causal inference methods provide substantive results on treatment choice and survival of older lung cancer patients with realistic expectations of potential benefits of specific treatments. Applications of these models to specific subsets of patients can aid in the development of practical guidelines that help optimize lung cancer treatment based on individual patient characteristics.