Burden of Comorbid Conditions Among Individuals Screened for Lung Cancer
Bibliographic record
Abstract
Importance: Screening for lung cancer with low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality in trials that included relatively younger, healthier, and predominantly White populations. The comorbidity profiles among patients undergoing lung cancer screening in practice settings are poorly understood. Objective: To evaluate the comorbidity profiles of patients in the Personalized Lung Cancer Screening (PLuS) cohort as a clinical setting vs the National Lung Screening Trial (NLST) participants in a clinical trial setting. Design, Setting, and Participants: This multicenter cohort study was conducted across 3 health care systems in California, Florida, and South Carolina and included patients who underwent LDCT lung cancer screening between 2016 and 2021. Data were analyzed between January 1, 2016, and December 31, 2021. Exposures: Receipt of the LDCT scan identified through Current Procedural Terminology and Healthcare Common Procedure Coding System codes. Main Outcomes and Measures: Detailed comorbidity data, measures of pulmonary function, and study data abstracted from electronic health records and institutional, Surveillance, Epidemiology, and End Results (SEER), and state registries were compared with self-reported comorbid conditions of participants in the LDCT arm of the NLST. Results: The PLuS cohort (n = 31 795) included 49.0% participants aged 65 years or older vs 26.6% in the NLST cohort (n = 26 723); 23.3% were individuals of racial and ethnic minority groups in the PLuS cohort compared with 8.5% in the NLST. The prevalence of comorbidity was substantially higher in the PLuS cohort than the NLST group, particularly chronic obstructive pulmonary disease (32.7% vs 17.5%), diabetes (24.6% vs 9.7%), and heart disease (15.9% vs 12.9%). Among those in the PLuS cohort, 19.3% had a Charlson Comorbidity Index score of 4 or higher, 18.0% had a frailty index greater than 0.20, 16.9% had a forced expiratory volume in 1 second (FEV-1) lower than 50% of predicted, and almost 5% had an ejection fraction lower than 40%. The prevalence of multimorbidity and frailty was especially high among those in the 75 years or older age group. Conclusions and Relevance: This study found that the PLuS cohort members were older, had greater illness severity, and more racially and ethnically diverse than the NLST participants. Older patients and those with consequential comorbidity likely had different risk-benefit profiles, which may have affected screening outcomes. The high prevalence of multimorbidity, frailty, and impaired cardiopulmonary function in the PLuS cohort suggests that the balance of benefits and harms observed in the NLST group may not translate to the clinical setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".