Significance of pretreatment comorbidities in elderly patients with advanced non-small-cell lung cancer treated with chemotherapy or epidermal growth factor receptor-tyrosine kinase inhibitor

Patterns of Comorbidities in Lung Cancer Patients and Survival

INTRODUCTION

Comorbidities affect diagnosis and treatments in cancer patients. This study explores the prevalence and patterns of comorbidities in non-small cell lung cancer (NSCLC) patients and their association with survival.

MATERIALS AND METHODS

This retrospective population-based cohort study included 1674 incident NSCLC patients. Comorbidities were classified based on the ICD-9-CM system, with 13 disease categories analyzed. Patients with more than two comorbidities were classified into three mutually exclusive and exhaustive latent classes (Latent Class Analysis [LCA]). The optimal number of latent classes was determined by applying the Akaike Information Criterion. Cox regression models were run to assess overall and cancer-specific mortality, adjusting for the comorbidity groups, sex, age, and stage at diagnosis.

RESULTS

In 1674 NSCLC patients, the most prevalent medical conditions were respiratory (35.8%) and cardiovascular (33.5%). The Cox regression showed that even one comorbidity is associated with an increased hazard of overall mortality (HR = 1.33, 95%CI: 1.11-1.59, p = 0.002). LCA-derived Class-1 (cardiovascular-respiratory and endocrine) reported HR = 1.74 (95%CI: 1.39-2.17, p < 0.001), Class-2 (multi-organ) HR = 1.44 (95%CI: 1.18-1.77, p < 0.001), and Class-3 (socio-multifactorial-neuro) HR = 1.62 (95%CI: 1.36-1.93, p < 0.001). Instead, in patients with one comorbidity, NSCLC-specific mortality showed no significant trend towards increased risk (HR = 1.17, 95%CI: 1.00-1.43, p = 0.114). Significant associations emerged between NSCLC-specific mortality and LCA-classes: Class-1: HR = 1.49 (95%CI: 1.20-1.91, p = 0.001); Class-2 HR = 1.25 (95%CI: 1.0-1.57 p = 0.048); and Class-3: HR = 1.23 (95%CI: 1.00-1.48, p = 0.035).

CONCLUSIONS

The adverse impact of comorbidities on NSCLC-specific mortality requires their inclusion as risk factors in cancer treatment and prognosis.

Use of comorbidity indices in patients with any cancer, breast cancer, and human epidermal growth factor receptor-2-positive breast cancer: A systematic review

Objective

To identify comorbidity indices that have been validated in cancer populations, with a focus on breast cancer and human epidermal growth factor receptor-2-positive (HER2+) breast cancer.

Study design and setting

A systematic review of the literature on the use of comorbidity indices in any cancer, breast cancer, and HER2+ breast cancer using Ovid and PubMed.

Results

The final data set comprised 252 articles (252 any cancer, 39 breast cancer, 7 HER2+ breast cancer). The most common cancers assessed were hematologic and breast, and the most common comorbidity index used was the Charlson Comorbidity Index (CCI) or a CCI derivative. Most validity testing of comorbidity indices used predictive validity based on survival outcomes. Hazard ratios for survival outcomes generally found that a higher comorbidity burden (measured by CCI) increased mortality risk in patients with breast cancer. All breast-cancer studies that validated comorbidity indices used CCI-based indices. Only one article validated a comorbidity index in HER2+ breast cancer.

Conclusion

CCI-based indices are the most appropriate indices to use in the general breast-cancer population. There is insufficient validation of any comorbidity index in HER2+ breast cancer to provide a recommendation, indicating a future need to validate these instruments in this population.

Current and Emerging Options in the Management of EGFR Mutation-Positive Non-Small-Cell Lung Cancer: Considerations in the Elderly

The elderly population with cancer is increasing worldwide. Currently, the median age at lung cancer diagnosis is approximately 70 years. Clinicians are increasingly dealing with a population of elderly non-small-cell lung cancer patients characterised by relevant co-morbidities and ageing-related characteristics, making treatment choice more challenging. Robust evidence demonstrated that activating mutations in the epidermal growth factor receptor (EGFR) gene are the best predictor for sensitivity to EGFR tyrosine kinase inhibitors. Nine large phase III trials conducted in both the Asian and Caucasian populations demonstrated that gefitinib, erlotinib and afatinib are superior to standard platinum-based chemotherapy as front-line treatment and subgroup analyses confirmed the superiority of erlotinib or gefitinib over chemotherapy in the second-line setting. Although no large phase III trials have been specifically conducted in EGFR mutation-positive (EGFR (mut+)) elderly non-small-cell lung cancer patients, available data, coming from subgroup analysis, retrospective series or small prospective phase II trials, replicated in the elderly the results observed in the general population, thus suggesting that age per se does not represent a criterion for treatment selection. In addition, the favourable toxicity profile of EGFR-tyrosine kinase inhibitors makes these agents the preferred option in such a group of patients, for which concomitant medications are often required.

The impact of comorbidity on cancer survival: a review

Background

A number of studies have shown poorer survival among cancer patients with comorbidity. Several mechanisms may underlie this finding. In this review we summarize the current literature on the association between patient comorbidity and cancer prognosis. Prognostic factors examined include tumor biology, diagnosis, treatment, clinical quality, and adherence.

Methods

All English-language articles published during 2002–2012 on the association between comorbidity and survival among patients with colon cancer, breast cancer, and lung cancer were identified from PubMed, MEDLINE and Embase. Titles and abstracts were reviewed to identify eligible studies and their main results were then extracted.

Results

Our search yielded more than 2,500 articles related to comorbidity and cancer, but few investigated the prognostic impact of comorbidity as a primary aim. Most studies found that cancer patients with comorbidity had poorer survival than those without comorbidity, with 5-year mortality hazard ratios ranging from 1.1 to 5.8. Few studies examined the influence of specific chronic conditions. In general, comorbidity does not appear to be associated with more aggressive types of cancer or other differences in tumor biology. Presence of specific severe comorbidities or psychiatric disorders were found to be associated with delayed cancer diagnosis in some studies, while chronic diseases requiring regular medical visits were associated with earlier cancer detection in others. Another finding was that patients with comorbidity do not receive standard cancer treatments such as surgery, chemotherapy, and radiation therapy as often as patients without comorbidity, and their chance of completing a course of cancer treatment is lower. Postoperative complications and mortality are higher in patients with comorbidity. It is unclear from the literature whether the apparent undertreatment reflects appropriate consideration of greater toxicity risk, poorer clinical quality, patient preferences, or poor adherence among patients with comorbidity.

Conclusion

Despite increasing recognition of the importance of comorbid illnesses among cancer patients, major challenges remain. Both treatment effectiveness and compliance appear compromised among cancer patients with comorbidity. Data on clinical quality is limited.

Treatment outcomes in elderly with advanced-stage non-small cell lung cancer

Purpose

Lung cancer remains the top cause of cancer morbidity and mortality in the world. Although the identification of epidermal growth factor receptor (EGFR) gene mutations could predict efficacy of tyrosine kinase inhibitor (TKI), testing for predictive biomarkers are not always possible due to tissue availability. The overall therapeutic decision remains a clinical one for a significant proportion of elderly patients with advanced stage lung cancer but no known EGFR mutation status. The purpose of this study was to compare the outcome of drug treatment modalities in progression-free survival (PFS) and overall survival (OS) for elderly with advanced-stage non-small cell lung cancer (NSCLC) and to identify clinical parameters that could predict treatment outcome.

Methods

Clinical records of patients aged 70 years or older with advanced-stage NSCLC who have received treatment were reviewed. A group of gender- and histology-matched subjects younger than age 70 years were identified as controls.

Results

Fifty-six elderly patients were included. The median age at diagnosis was 73 years; 60.7 % received only one line of treatment. Baseline performance status (PS) was the only predictor of improved PFS (p = 0.042) and OS (p = 0.002). There was no difference in survival between the upfront chemotherapy and the TKI groups

Conclusions

In elderly with advanced-stage NSCLC without known EGFR mutation status, use of EGFR–TKI and chemotherapy resulted in comparable survival benefits. Age was not predictive of worse treatment outcome. The baseline PS should be taken into consideration in the therapeutic decision in elderly with NSCLC where the EGFR mutation status is not known.

Customizing systemic therapy in patients with advanced non-small cell lung cancer

Lung cancer is the leading cause of cancer deaths worldwide. Standard chemotherapy has been shown to improve quality of life and has a modest influence on overall survival. This modest improvement in survival is partly due to the choice of chemotherapy regimens that have been based on prognostic factors such as age, performance status and comorbidities of the patient. This underlines the importance of developing a more personalized therapy for patients with non-small cell lung cancer. Such an approach may reduce the variation in how individual patients respond to medications by tailoring therapies to their genetic profile. In this review we focus on several aspects of customized therapy, looking not only at patient characteristics but also to tumor histology and specific tumor biomarkers.