Introduction: Lung cancer is the leading cause of cancer death in the USA and worldwide, and lung cancer screening (LCS) with low-dose CT (LDCT) has the potential to improve lung cancer outcomes. A critical question is whether the ratio of potential benefits to harms found in prior LCS trials applies to an older and potentially sicker population. The Personalised Lung Cancer Screening (PLuS) study will help close this knowledge gap by leveraging real-world data to fully characterise LCS recipients. The principal goal of the PLuS study is to characterise the comorbidity burden of individuals undergoing LCS and quantify the benefits and harms of LCS to enable informed decision-making.
Methods and analysis: PLuS is a multicentre observational study designed to assemble an LCS cohort from the electronic health records of ~40 000 individuals undergoing annual LCS with LDCT from 2016 to 2022. Data will be integrated into a unified repository to (1) examine the burden of multimorbidity by race/ethnicity, socioeconomic status and age; (2) quantify potential benefits and harms; and (3) use the observational data with validated simulation models in the Cancer Intervention and Surveillance Modeling Network (CISNET) to provide LCS outcomes in the real-world US population. We will fit a multivariable logistic regression model to estimate the adjusted ORs of comorbidity, functional limitations and impaired pulmonary function adjusted for relevant covariates. We will also estimate the cumulative risk of LCS outcomes using discrete-time survival models. To our knowledge, this is the first study to combine observational data and simulation models to estimate the long-term impact of LCS with LDCT.
Ethics and dissemination: The study was approved by the Kaiser Permanente Southern California Institutional Review Board and VA Portland Health Care System. The results will be disseminated through publications and presentations at national and international conferences. Safety considerations include protection of patient confidentiality.
Keywords: computed tomography; epidemiology; oncology; radiology & imaging.
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