Improving Smoking History Documentation to Facilitate Lung Cancer Screening Utilization
Lin, Y.; Ding, R.; Tabatabaei, S. M. H.; Tupper, H. I.; Moghanaki, D.; Schussel, B. H.; Aberle, D. R.; Hsu, W.; Prosper, A. E.
Show abstract
ObjectivesLung cancer screening (LCS) is the only screening test incorporating behavioral risk factors into eligibility determination. However, collecting necessary smoking history data has been challenging, limiting screening uptake. In this study, we evaluated how a program coordinators detailed shared decision-making (SDM) impacted smoking data reliability. MethodsPatients who underwent a baseline screening low-dose CT between July 31, 2013, and August 25, 2023, were stratified into pre- and post-intervention cohorts. The intervention was a comprehensive pre-CT smoking history assessment with SDM by an LCS program coordinator, implemented on July 31, 2017. We compared the completeness and concordance of smoking history data between clinician and patient self-report. ResultsAmong 3795 patients, 670 (18%) were pre- and 3125 (82%) were post-intervention. Having a coordinator reduced missing smoking data (p<0.001), but did not eliminate it. Both groups showed high concordance between clinician-documented and self-reported smoking status (pre: kappa=0.84, 95% confidence interval [CI] 0.79-0.89; post: kappa=0.84, 95% CI 0.83-0.86). Correlations strengthened for smoking duration (rho=0.71 vs. 0.65, p=0.026) and years since quitting (rho=0.83 vs. 0.80, p=0.21) after involving a coordinator. Correlations for smoking intensity and pack years remained fair (rho<0.6). LCS eligibility based on self-reported smoking history increased from 46.0% (308/670) pre- to 64.1% (2003/3125) post-intervention, below the 100% eligibility using clinician-documented history. ConclusionsSmoking data reliability improved after a dedicated LCS program coordinator implemented a smoking history assessment. Meanwhile, challenges remained with the ascertainment of total pack-years. Detailed probing and patient education may be insufficient to overcome challenges in assessing smoking intensity.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Missing data in the medical record for oncology patients: prevalence and association with outcomes 92%
- Evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax on chest radiograph 90%
- Low adherence to existing model reporting guidelines by commonly used clinical prediction models 89%
Similar papers in this journal
- A Normal Forced Vital Capacity Does Not Reliably or Equitably Exclude Restriction 92%
- Multiple breath-washout for pulmonary function assessment in young childhood cancer survivors: a multicenter study 89%
- Risk of Atherosclerotic Cardiovascular Disease Hospitalizations after COPD Hospitalization among Older Adults 88%
Similar papers in this journal
- E-cigarette use and Respiratory Symptoms in Residents of the United States: A BRFSS Report 92%
- Classification performance bias between training and test sets in a limited mammography dataset 91%
- Assessment of Sociodemographic Factors Associated with Time to Self-reported COVID-19 Infection Among a Large Multi-Center Prospective Cohort Population in the Southeastern United States 91%
Similar papers in this journal
- Immediate, remote smoking cessation intervention in participants undergoing a targeted lung health check: QuLIT2 a randomised controlled trial 93%
- Rethinking Blood Eosinophils for Assessing ICS Response in COPD: A Post-Hoc Analysis from FLAME 88%
- Development and Prospective Validation of a Transparent Deep Learning Algorithm for Predicting Need for Mechanical Ventilation 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.