Faculty Bibliography

IMPORTANCE:Interception therapy requires individuals to undergo treatment to prevent a future medical event, but little is known about preferences of individuals at high risk for lung cancer and whether they would be interested in this type of treatment. OBJECTIVE:To explore preferences of individuals at high risk for lung cancer for potential interception therapies to reduce this risk. DESIGN, SETTING, AND PARTICIPANTS:This survey study used a discrete-choice experiment and included hypothetical lung cancer interception treatments with 4 attributes: reduction in lung cancer risk over 3 years, injection site reaction severity, nonfatal serious infection, and death from serious infection. Respondents were assigned to a baseline lung cancer risk of 6%, 10%, or 16% over 3 years. The discrete-choice experiment was administered online (July 13 to September 6, 2022) to US respondents eligible for lung cancer screening according to US Preventive Services Task Force guidelines. Participants included adults aged 50 to 80 years with at least a 20 pack-year smoking history. Statistical analysis was performed from September to December 2022. MAIN OUTCOMES AND MEASURES:Attribute-level preference weights were estimated, and conditional relative attribute importance, maximum acceptable risks, and minimum acceptable benefits were calculated. Characteristics of respondents who always selected no treatment were also explored. RESULTS:Of the 803 survey respondents, 495 (61.6%) were female, 138 (17.2%) were African American or Black, 55 (6.8%) were Alaska Native, American Indian, or Native American, 44 (5.5%) were Asian or Native Hawaiian or Other Pacific Islander, 104 (13.0%) were Hispanic, Latin American, or Latinx, and 462 (57.5%) were White, Middle Eastern or North African, or a race or ethnicity not listed; and mean (SD) age was 63.0 (7.5) years. Most respondents were willing to accept interception therapy and viewed reduction in lung cancer risk as the most important attribute. Respondents would accept a greater than or equal to a 12.0 percentage point increase in risk of nonfatal serious infection if lung cancer risk was reduced by at least 20.0 percentage points; and a greater than or equal to 1.2 percentage point increase in risk of fatal serious infection if lung cancer risk was reduced by at least 30.0 percentage points. Respondents would require at least a 15.4 (95% CI, 10.6-20.2) percentage point decrease in lung cancer risk to accept a 12.0 percentage point increase in risk of nonfatal serious infection; and at least a 23.1 (95% CI, 16.4-29.8) percentage point decrease in lung cancer risk to accept a 1.2 percentage point increase in risk of death from serious infection. Respondents who were unwilling to accept interception therapy in any question (129 [16.1%]) were more likely to be older and to currently smoke with no prior cessation attempt, and less likely to have been vaccinated against COVID-19 or examined for skin cancer. CONCLUSIONS AND RELEVANCE:In this survey study of individuals at high risk of lung cancer, most respondents were willing to consider interception therapy. These results suggest the importance of benefit-risk assessments for future lung cancer interception treatments.

BACKGROUND:Pulmonary nodules suspicious for lung cancer are frequently diagnosed. Evaluating and optimizing the diagnostic yield of lung nodule biopsy is critical as innovation in bronchoscopy continues to progress. METHODS:This is a retrospective cohort study. Consecutive patients undergoing guided bronchoscopy for suspicious pulmonary nodule(s) between February 2020 and July 2021 were included. The cone-beam computed tomography (CBCT)+ radial endobronchial ultrasound (r-EBUS) group had their procedure using CBCT-derived augmented fluoroscopy along with r-EBUS. The CBCT+ ultrathin bronchoscope (UTB)+r-EBUS group had the same procedure but with the use of an ultrathin bronchoscope. The r-EBUS group underwent r-EBUS guidance without CBCT or augmented fluoroscopy. We used multivariable logistic regression to compare diagnostic yield, adjusting for confounding variables. RESULTS:A total of 116 patients were included. The median pulmonary lesion diameter was 19.5 mm (interquartile range, 15.0 to 27.5 mm), and 91 (78.4%) were in the peripheral half of the lung. Thirty patients (25.9%) underwent CBCT+UTB, 27 (23.3%) CBCT, and 59 (50.9%) r-EBUS alone with unadjusted diagnostic yields of 86.7%, 70.4%, and 42.4%, respectively ( P <0.001). The adjusted diagnostic yields were 85.0% (95% CI, 68.6% to 100%), 68.3% (95% CI, 50.1% to 86.6%), and 44.5% (95% CI, 31.0% to 58.0%), respectively. There was significantly more virtual navigational bronchoscopy use in the r-EBUS group (45.8%) compared with the CBCT+UTB (13.3%) and CBCT (18.5%) groups, respectively. CBCT procedures required dose area product radiation doses of 7602.5 µGym 2 . CONCLUSION/CONCLUSIONS:Compared with the r-EBUS group, CBCT + UTB + r-EBUS was associated with higher navigational success, fewer nondiagnostic biopsy results, and a higher diagnostic yield. CBCT procedures are associated with a considerable radiation dose.

BACKGROUND:The diagnostic workup of individuals suspected of having lung cancer can be complex and protracted because conventional symptoms of lung cancer have low specificity and sensitivity. RESEARCH QUESTION:Among individuals with symptoms of lung cancer, can a blood-based approach to analyze cell-free DNA (cfDNA) fragmentation (the DNA evaluation of fragments for early interception [DELFI] score) enhance evaluation for the possible presence of lung cancer? STUDY DESIGN AND METHODS:Adults were referred to Bispebjerg Hospital (Copenhagen, Denmark) for diagnostic evaluation of initial imaging anomalies and symptoms consistent with lung cancer. Numbers and types of symptoms were extracted from medical records. cfDNA from plasma samples obtained at the prediagnostic visit was isolated, sequenced, and analyzed for genome-wide cfDNA fragmentation patterns. The relationships among clinical presentation, cancer status, and DELFI score were examined. RESULTS:A total of 296 individuals were analyzed. Median DELFI scores were higher for those with lung cancer (n = 98) than those without cancer (n = 198; 0.94 vs 0.19; P < .001). In a multivariate model adjusted for age, smoking history, and presenting symptoms, the addition of the DELFI score improved the prediction of lung cancer for those who demonstrated symptoms (area under the receiver operating characteristic curve, 0.74-0.94). INTERPRETATION:The DELFI score distinguishes individuals with lung cancer from those without cancer better than suspicious symptoms do. These results represent proof-of-concept support that fragmentation-based biomarker approaches may facilitate diagnostic resolution for patients with concerning symptoms of lung cancer.

PURPOSE:Lung cancer screening (LCS) guidelines in the United States recommend LCS for those age 50-80 years with at least 20 pack-years smoking history who currently smoke or quit within the last 15 years. We tested the performance of simple smoking-related criteria derived from electronic health record (EHR) data and developed and tested the performance of a multivariable model in predicting LCS eligibility. METHODS:Analyses were completed within the Population-based Research to Optimize the Screening Process Lung Consortium (PROSPR-Lung). In our primary validity analyses, the reference standard LCS eligibility was based on self-reported smoking data collected via survey. Within one PROSPR-Lung health system, we used a training data set and penalized multivariable logistic regression using the Least Absolute Shrinkage and Selection Operator to select EHR-based variables into the prediction model including demographics, smoking history, diagnoses, and prescription medications. A separate test data set assessed model performance. We also conducted external validation analysis in a separate health system and reported AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy metrics associated with the Youden Index. RESULTS:There were 14,214 individuals with survey data to assess LCS eligibility in primary analyses. The overall performance for assigning LCS eligibility status as measured by the AUC values at the two health systems was 0.940 and 0.938. At the Youden Index cutoff value, performance metrics were as follows: accuracy, 0.855 and 0.895; sensitivity, 0.886 and 0.920; specificity, 0.896 and 0.850; PPV, 0.357 and 0.444; and NPV, 0.988 and 0.992. CONCLUSION:Our results suggest that health systems can use an EHR-derived multivariable prediction model to aid in the identification of those who may be eligible for LCS.

BACKGROUND:Lung cancer screening uptake for individuals at high risk is generally low across the United States, and reporting of lung cancer screening practices and outcomes is often limited to single hospitals or institutions. We describe a citywide, multicenter analysis of individuals receiving lung cancer screening integrated with geospatial analyses of neighborhood-level lung cancer risk factors. METHODS:The Philadelphia Lung Cancer Learning Community consists of lung cancer screening clinicians and researchers at the 3 largest health systems in the city. This multidisciplinary, multi-institutional team identified a Philadelphia Lung Cancer Learning Community study cohort that included 11 222 Philadelphia residents who underwent low-dose computed tomography for lung cancer screening from 2014 to 2021 at a Philadelphia Lung Cancer Learning Community health-care system. Individual-level demographic and clinical data were obtained, and lung cancer screening participants were geocoded to their Philadelphia census tract of residence. Neighborhood characteristics were integrated with lung cancer screening counts to generate bivariate choropleth maps. RESULTS:The combined sample included 37.8% Black adults, 52.4% women, and 56.3% adults who currently smoke. Of 376 residential census tracts in Philadelphia, 358 (95.2%) included 5 or more individuals undergoing lung cancer screening, and the highest counts were geographically clustered around each health system's screening sites. A relatively low percentage of screened adults resided in census tracts with high tobacco retailer density or high smoking prevalence. CONCLUSIONS:The sociodemographic characteristics of lung cancer screening participants in Philadelphia varied by health system and neighborhood. These results suggest that a multicenter approach to lung cancer screening can identify vulnerable areas for future tailored approaches to improving lung cancer screening uptake. Future directions should use these findings to develop and test collaborative strategies to increase lung cancer screening at the community and regional levels.

BACKGROUND:Declines in the prevalence of cigarette smoking, advances in targeted therapies, and implementation of lung cancer screening have changed the clinical landscape for lung cancer. The proportion of lung cancer deaths is increasing in those who have never smoked cigarettes. To better understand contemporary patterns in survival among patients with lung cancer, a comprehensive evaluation of factors associated with survival, including differential associations by smoking status, is needed. METHODS:Patients diagnosed with lung cancer between January 1, 2010, and September 30, 2019, were identified. We estimated all-cause and lung cancer-specific median, 5-year, and multivariable restricted mean survival time (RMST) to identify demographic, socioeconomic, and clinical factors associated with survival, overall and stratified by smoking status (never, former, and current). RESULTS:Analyses included 6813 patients with lung cancer: 13.9% never smoked, 54.2% formerly smoked, and 31.9% currently smoked. All-cause RMST through 5 years for those who never, formerly, and currently smoked was 32.1, 25.9, and 23.3 months, respectively. Lung cancer-specific RMST was 36.3 months, 30.3 months, and 26.0 months, respectively. Across most models, female sex, younger age, higher socioeconomic measures, first-course surgery, histology, and body mass index were positively associated, and higher stage was inversely associated with survival. Relative to White patients, Black patients had increased survival among those who formerly smoked. CONCLUSIONS:We identify actionable factors associated with survival between those who never, formerly, and currently smoked cigarettes. These findings illuminate opportunities to address underlying mechanisms driving lung cancer progression, including use of first-course treatment, and enhanced implementation of tailored smoking cessation interventions for individuals diagnosed with cancer.

IMPORTANCE:Guidelines recommend shared decision-making prior to initiating lung cancer screening (LCS). However, evidence is lacking on how to best implement shared decision-making in clinical practice. OBJECTIVE:To evaluate the impact of an LCS Decision Tool (LCSDecTool) on the quality of decision-making and LCS uptake. DESIGN, SETTING, AND PARTICIPANTS:This randomized clinical trial enrolled participants at Veteran Affairs Medical Centers in Philadelphia, Pennsylvania; Milwaukee, Wisconsin; and West Haven, Connecticut, from March 18, 2019, to September 29, 2021, with follow-up through July 18, 2022. Individuals aged 55 to 80 years with a smoking history of at least 30 pack-years who were current smokers or had quit within the past 15 years were eligible to participate. Individuals with LCS within 15 months were excluded. Of 1047 individuals who were sent a recruitment letter or had referred themselves, 140 were enrolled. INTERVENTION:A web-based patient- and clinician-facing LCS decision support tool vs an attention control intervention. MAIN OUTCOME AND MEASURES:The primary outcome was decisional conflict at 1 month. Secondary outcomes included decisional conflict immediately after intervention and 3 months after intervention, knowledge, decisional regret, and anxiety immediately after intervention and 1 and 3 months after intervention and LCS by 6 months. RESULTS:Of 140 enrolled participants (median age, 64.0 [IQR, 61.0-69.0] years), 129 (92.1%) were men and 11 (7.9%) were women. Of 137 participants with data available, 75 (53.6%) were African American or Black and 62 (44.3%) were White; 4 participants (2.9%) also reported Hispanic or Latino ethnicity. Mean decisional conflict score at 1 month did not differ between the LCSDecTool and control groups (25.7 [95% CI, 21.4-30.1] vs 29.9 [95% CI, 25.6-34.2], respectively; P = .18). Mean LCS knowledge score was greater in the LCSDecTool group immediately after intervention (7.0 [95% CI, 6.3-7.7] vs 4.9 [95% CI, 4.3-5.5]; P < .001) and remained higher at 1 month (6.3 [95% CI, 5.7-6.8] vs 5.2 [95% CI, 4.5-5.8]; P = .03) and 3 months (6.2 [95% CI, 5.6-6.8] vs 5.1 [95% CI, 4.4-5.8]; P = .01). Uptake of LCS was greater in the LCSDecTool group at 6 months (26 of 69 [37.7%] vs 15 of 71 [21.1%]; P = .04). CONCLUSIONS AND RELEVANCE:In this randomized clinical trial of an LCSDecTool compared with attention control, no effect on decisional conflict occurred at 1 month. The LCSDecTool used in the primary care setting did not yield a significant difference in decisional conflict. The intervention led to greater knowledge and LCS uptake. These findings can inform future implementation strategies and research in LCS shared decision-making. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02899754.

Diagnostic testing is fundamental to medicine. However, studies of diagnostic testing in respiratory medicine vary significantly in terms of their methodology, definitions, and reporting of results. This has led to often conflicting or ambiguous results. To address this issue, a group of 20 respiratory journal editors worked to develop reporting standards for studies of diagnostic testing based on a rigorous methodology to guide authors, peer reviewers, and researchers when conducting studies of diagnostic testing in respiratory medicine. Four key areas are covered, including defining the reference standard of truth, measures of dichotomous test performance when used for dichotomous outcomes, measures of multichotomous test performance for dichotomous outcomes, and what constitutes a useful definition of diagnostic yield. The importance of using contingency tables for reporting results is addressed with examples from the literature. A practical checklist is provided as well for reporting studies of diagnostic testing.