The overall goal of this project is to improve the management of lung cancer patients with comorbidities. Lung cancer is the leading cause of cancer death in the US. Most patients have serious comorbidities (such as chronic pulmonary disease, cardiac disease, and chronic kidney disease), related to smoking and aging (mean age at diagnosis is 70 years). Up to 30% of lung cancer cases are diagnosed at a loco-regional stage, can be treated with a curative intent, and may experience relatively good long-term survival. However, the risk/benefit ratio of cancer therapies can be substantially altered in patients with comorbidities because of differences in toxicity, functional status, life expectancy, and quality of life. Unfortunately, patients with comorbidities are consistently excluded from randomized controlled trials (RCTs) generating an important gap in knowledge regarding their management. Lack of data relevant to patients with comorbidities has profound negative impacts including undertreatment, increased morbidity, and decreased survival. Thus, optimizing the management of these patients is a major public health priority. In this study, we will use simulation modeling, an approach complementary to RCTs, to determine the optimal treatment of early stage lung cancer patients with comorbidities.
The Specific Aims are to: 1) enhance and validate the Lung Cancer Policy Model (LCPM) to simulate the management and subsequent outcomes of patients with early stage lung cancer and specific comorbidities; 2) determine the optimal management and indications for lobectomy, elective limited resection, stereotactic body radiotherapy, and other treatments in stage I NSCLC patients with chronic lung and heart disease as well as by overall burden of comorbidities; 3) determine the optimal indications for adjuvant chemotherapy in patients with stage II and IIIA NSCLC and chronic lung, heart, or renal disease and by overall burden of comorbidities; and 4) compare outcomes following different treatment strategies (surgery, chemotherapy, or chemoradiotherapy) for patients with limited-stage SCLC and chronic lung, heart, or renal disease. To achieve these Aims, we will use an enhanced version of the LCPM, a well validated mathematical model of lung cancer progression.
In Aim 1, we will use data from several population-based registries to substantially enhance, calibrate, and validate the LCPM by incorporating functional status, frailty, treatments, complications of surgery and chemotherapy toxicity, outcomes, survival and quality of life of patients with comorbidities. Then, we will assess the optimal management, in terms of reducing toxicity and maximizing survival and quality of life, of patients with early stage lung cancer. Our study is innovative in applying modeling approaches, mostly used to evaluate cancer screening, to the optimization of lung cancer therapies. The results of the study will directly inform the management of large numbers of lung cancer patients with comorbidities, a vulnerable and understudied group that currently experience substantially worse outcomes.
The objective of this study is to improve the management and outcomes of patients with localized lung cancer and comorbid illnesses. Towards this end, we will use the Lung Cancer Policy Model to perform comparative effective simulation analyses tailored to this population. Our results will identify the optimal management, in terms of reducing toxicity and maximizing survival and quality of life, for patients with early stage lung cancer and comorbidities including chronic pulmonary, cardiac, and renal disease.
