Rates of downstream procedures and complications associated with lung cancer screening in routine clinical practice: A retrospective cohort study
Abstract
Background: Lung cancer screening (LCS) using low-dose computed tomography (LDCT) reduces lung cancer mortality but can lead to downstream procedures, complications, and other potential harms. Estimates of these events outside of the National Lung Screening Trial (NLST) have been variable and lacked evaluation by screening result that allows for more direct comparison to trials. Objective: Identify rates of downstream procedures and complications associated with LCS Design: Retrospective cohort study Setting: Five U.S. healthcare systems Patients: Individuals who completed a baseline LDCT for LCS from 2014–2018 Measurements: Outcomes included downstream imaging, invasive diagnostic procedures, and procedural complications. For each, we calculated absolute rates overall and stratified by screening result and by lung cancer detection, and calculated positive (PPV) and negative predictive value (NPV). Results: Among the 9,266 screened patients, 1,472 (15.9%) had a positive baseline LDCT, of which 140 (9.5%) were diagnosed with lung cancer within 12 months [PPV=9.5% (95% CI: 8.0, 11.0); NPV=99.8% (95% CI: 99.7, 99.9); sensitivity=92.7% (95% CI: 88.6, 96.9); specificity=84.4% (95% CI: 83.7, 85.2)]. Absolute rates of downstream imaging and invasive procedures in screened patients were 31.9% and 2.8%, respectively. In patients undergoing invasive procedures after positive results, complications were substantially higher than those observed in NLST (30.6% vs 17.7% for any…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 9,266 participants or records.
Read the paper
Related studies
- In silico toxicology protocols
- QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2's Chemisorption and Diffusion in Mg-MOF-74
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
- The computational power of the human brain
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.