Risk-Based Lung Cancer Detection in COPD Patients
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
Age50+
SponsorVejle Hospital
About this trial
The goal of the interventional study is to evaluate whether risk-based stratification using the PLCOm2012 model and a machine learning (ML) model can identify patients with chronic obstructive pulmonary disease (COPD) who are at high risk of developing lung cancer and may benefit from low-dose computed tomography (LDCT) screening. The study population includes adults aged 50 years and older with COPD and a history of smoking attending an outpatient clinic.
The main question it aims to answer are:
\- What is the incidence of histopathologically confirmed lung cancer following risk-based stratification?
Participants will:
* Undergo lung cancer risk assessment using the PLCOm2012 model and an ML-based model based on clinical and laboratory data * Be referred for LDCT if classified as high-risk * Continue standard care if classified as low-risk * Be followed through electronic health records for up to six years to assess outcomes including lung cancer incidence, adherence to LDCT, time to imaging, healthcare utilization, costs, and safety
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Diagnosed with COPD
Age ≥ 50 years
Attendance at the outpatient Respiratory Medicine clinic, Vejle Hospital, University Hospital of Southern Denmark
Smoking history (current or former smoker)
Disqualifiers
Previous diagnosis of lung cancer within the last five years.
Active treatment for cancer within the last 12 months except non-melanoma skin cancer and carcinoma in situ cervicis uteri.
Invasive methods to verify potential lung cancer not an option.
Inability to provide informed consent.
Trial design
Design model
Single group
Treatments tested in this trial
Risk-based prediction models
Other interventionPatients will be stratified into high-risk and low-risk groups using both the Lung Cancer Risk Prediction Calculator for smokers (PLCOm2012) and an in-house developed machine learning model based on sex, age, smoking status, and laboratory data from routine blood sample analyses.
Treatment groups
1,000 Participants
are divided into 1 treatment groupGroup A: COPD patients undergoing lung cancer risk assessmentExperimental treatment 1 intervention
Trial outcomes
Primary outcomes
1
Number of histopathologically confirmed lung cancers
Time frame
From risk assessment to end of follow-up at 6 yearsSecondary outcomes
1
Proportion of high-risk patients who undergo LDCT after referral
Time frame
From risk assessment to LDCT completion at 1 year2
Number of COPD patients requiring risk assessment and LDCT screening to detect one case of lung cancer
Time frame
From risk assessment to end of follow-up at 6 years3
Time from risk assessment to completion of LDCT
Time frame
From risk assessment to LDCT completion at 1 year4
Number of diagnostic procedures per patient stratified by risk group
Time frame
From risk assessment to end of follow-up at 6 yearsSponsors and contacts
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Source ClinicalTrials.gov