BACKGROUND:Patient engagement is associated with improved care quality, better health outcomes, increased trust and satisfaction, and reduced costs. Patient engagement is recommended in cancer care. Patient advisory groups (PAGs) are a commonly used approach for engaging patients. However, more evidence is needed to understand how effectively PAGs support meaningful engagement, what factors shape that engagement, and how their contributions align with program goals. This study used a mixed-methods approach to evaluate patient engagement within a PAG supporting an oncology quality improvement initiative. METHODS:The Oncology Equity Alliance (OEA), a quality improvement initiative to improve care coordination and reduce time to treatment, established a PAG to engage patients throughout the initiative. We conducted a mixed-method evaluation of PAG engagement. Focus groups with PAG members and qualitative interviews with OEA team members were rapidly analyzed to identify engagement successes and challenges, and to determine engagement principles to focus on for survey evaluation. Perceptions of PAG engagement were assessed via surveys of PAG members and OEA team members, using selected items from the Research Engagement Survey Tool (REST). Surveys were analyzed descriptively and according to the REST scoring scheme. RESULTS:Focus groups (n = 2) and interviews (n = 3) identified key facilitators that supported engagement including deliberate coordination, mutual respect, a sense of belonging, and co-learning. Engagement was also positively impacted by members' motivations for joining the PAG and practical and logistical considerations. Challenges included PAG members' desire for greater understanding of the project's impact, more agenda setting, and ongoing education about OEA core components. This qualitative data informed the selection of engagement principles of focus for quantitative evaluation using REST. A total of 80% of PAG members (n = 10) felt very engaged, with the degree of engagement corresponding to cooperation and collaboration domains; however, for individual survey items, an average of 20% of responses were marked by PAG members as "not applicable." CONCLUSION:This mixed-method evaluation found strong alignment between program goals and PAG member engagement, highlighted effective strategies, and identified addressable challenges. As patient engagement becomes more common in cancer care, applying these lessons is essential to advancing meaningful, person-centered programs. PATIENT CONTRIBUTION:This paper presents an evaluation of a PAG. In addition to being the study participants, the members of the patient advisory group participated in member checking to validate the study findings, ensuring that the interpretations reflected their experiences and perspectives.

Background: Lung cancer screening with an annual low-dose CT scan is recommended by the US Preventive Services Task Force for high-risk patients based on age (50-80 years of age) and smoking history (≥ 20 pack-years). Inaccurate smoking history data in the electronic health record (EHR) pose a challenge to identifying eligible patients. Digital strategies to collect patient-generated health data (PGHD) are a potential solution to elicit complete smoking histories directly from patients to then integrate into their EHR. Research Question: How do patients perceive and experience the use of digital outreach strategies to collect smoking history data to determine lung cancer screening eligibility? Study Design and Methods: As part of a quality improvement initiative, semistructured qualitative interviews were completed with a diverse group of patients who had received a request to complete a digital smoking history survey. Rapid analytical methods were used. Results: We completed 20 interviews with patients who did (n = 9) and did not (n = 11) complete the digital smoking history survey. Participants described varied preferences for digitally self-updating their smoking histories. Four themes emerged regarding barriers to uptake including (1) participants prefer to update their health record with a clinician, (2) technologic barriers influence participant engagement with digital PGHD collection strategies, (3) multiple options for collecting smoking frequency are needed to align with a variety of behaviors, and (4) participants have mixed perceptions of the value of accurate and updated EHR smoking data. Interpretation: Our findings highlight the need to address barriers to collection of digital PGHD to optimize reach and uptake. Using PGHD to obtain an updated and accurate smoking history has important implications, and incorporating patient education, strategies to overcome technologic barriers, and multiple metric options to collect smoking history may improve use.

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with varying degrees of airway wall thickening, chronic bronchitis, and emphysema. A better understanding of the underlying pathology is needed to improve the personalized treatment of the disease and identify new therapeutic targets. Available data from 56 COPD patients included in the GLUCOLD study were used (61.1±7.7 years, 89% male, and FEV1 of 62.5±8.9% predicted). Clinical characterization was performed including bronchoscopy and collection of bronchial biopsies at baseline. RNA from bronchial biopsies was sequenced and used for unsupervised clustering, using a 98 COPD gene signature previously identified in bronchial brushes comparing patients with COPD to non-COPD controls. Next, we assessed differences in the clinical expression of COPD, lung function decline, inflammatory cell counts, and gene expression between clusters. Validation was performed in an independent dataset. We identified two clusters: CAGE1 (n=39) and CAGE2 (n=17). CAGE2 patients had higher percentage of sputum lymphocytes, and more CD4+ and CD8+ T-cell counts in their bronchial biopsies. In addition, their FEV1 improved less in response to 30-months treatment with inhaled corticosteroids (ICS) (change in FEV1- CAGE1: +24.4mL; CAGE2: -29.1mL; p-value=0.048), and they experienced a faster decline in their lung function follow-up (CAGE1: -44.0mL/year; CAGE2: -69.9mL/year; p-value=0.002). Gene expression analysis showed more activation of T- and B-cell immune responses in CAGE2. We identified a new COPD endotype, CAGE2, characterized by ICS unresponsiveness and faster lung function decline. Additionally, we show a different pathobiology in CAGE2 with more activation of T- and B-cell immune responses. ### Competing Interest Statement A. Spira is an employee of Johnson and Johnson. ### Clinical Trial NCT00158847 ### Funding Statement The study was sponsored by the Dutch Ministry of Health via the Public Private Funding Program (PPP), GlaxoSmithKline (The Netherlands), the University Medical Centre Groningen and the Leiden University Medical Centre. Rui Marcalo is supported by Fundacao para a Ciencia e Tecnologia through the grant 10.54499/UI/BD/151337/2021. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of Groningen University Medical Center gave ethical approval for this work. Ethics committee of Leiden University Medical Center gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.

Despite evidence supporting lung cancer screening (LCS) with low-dose computed tomography imaging, LCS rates remain as low as 5% nationally. Many barriers to screening have been identified, including but not limited to the following: competing priorities in the medical visit, lack of documentation of robust smoking histories, absence of clinical decision support for primary care teams, patient and provider knowledge gaps, and challenges with access to screening. To address these barriers, the authors piloted a patient-centered population health nurse-driven LCS intervention in a general internal medicine practice embedded in an urban safety-net health system. The intervention, which began in August 2022, included prescheduled nurse telehealth visits focused on cancer screening opportunities, a patient registry, and with standardized documentation templates, customized decision support tools to clarify eligibility and direct scheduling workflows at the time of the nurse visit. Empowering the nurse to take a more prominent role, working at the top of their license, to provide formal LCS shared decision-making represents an innovation to the care delivery model. In addition, this model improves on licensed independent practitioner-driven screening as the population health nurse directly schedules the LCS in the radiology schedule, reducing friction in the process. As of December 18, 2023, early experience shows that the pilot program achieved a 58.7% screening completion rate, suggesting that a nurse-driven model can contribute positively to the uptake of LCS.

BackgroundTobacco smoking is an important risk factor for disease, but inaccurate smoking history data in the electronic medical record (EMR) limits the reach of lung cancer screening (LCS) and tobacco cessation interventions. Patient-generated health data is a novel approach to documenting smoking history; however, the comparative effectiveness of different approaches is unclear. ObjectiveWe designed a quality improvement intervention to evaluate the effectiveness of portal questionnaires compared to SMS text message–based surveys, to compare message frames, and to evaluate the completeness of patient-generated smoking histories. MethodsWe randomly assigned patients aged between 50 and 80 years with a history of tobacco use who identified English as a preferred language and have never undergone LCS to receive an EMR portal questionnaire or a text survey. The portal questionnaire used a “helpfulness” message, while the text survey tested frame types informed by behavior economics (“gain,” “loss,” and “helpfulness”) and nudge messaging. The primary outcome was the response rate for each modality and framing type. Completeness and consistency with documented structured smoking data were also evaluated. ResultsParticipants were more likely to respond to the text survey (191/1000, 19.1%) compared to the portal questionnaire (35/504, 6.9%). Across all text survey rounds, patients were less responsive to the “helpfulness” frame compared with the “gain” frame (odds ratio [OR] 0.29, 95% CI 0.09-0.91; P<.05) and “loss” frame (OR 0.32, 95% CI 11.8-99.4; P<.05). Compared to the structured data in the EMR, the patient-generated data were significantly more likely to be complete enough to determine LCS eligibility both compared to the portal questionnaire (OR 34.2, 95% CI 3.8-11.1; P<.05) and to the text survey (OR 6.8, 95% CI 3.8-11.1; P<.05). ConclusionsWe found that an approach using patient-generated data is a feasible way to engage patients and collect complete smoking histories. Patients are likely to respond to a text survey using “gain” or “loss” framing to report detailed smoking histories. Optimizing an SMS text message approach to collect medical information has implications for preventative and follow-up clinical care beyond smoking histories, LCS, and smoking cessation therapy.

PURPOSE OF REVIEW:Lung cancer remains the leading cause of cancer mortality worldwide. Health disparities have long been noted in lung cancer incidence and survival and persist across the continuum of care. Understanding the gaps in care that arise from disparities in lung cancer risk, screening, treatment, and survivorship are essential to guiding efforts to achieve equitable care.RECENT FINDINGS:Recent literature continues to show that Black people, women, and people who experience socioeconomic disadvantage or live in rural areas experience disparities throughout the spectrum of lung cancer care. Contributing factors include structural racism, lower education level and health literacy, insurance type, healthcare facility accessibility, inhaled carcinogen exposure, and unmet social needs. Promising strategies to improve lung cancer care equity include policy to reduce exposure to tobacco smoke and harmful pollutants, more inclusive lung cancer screening eligibility criteria, improved access and patient navigation in lung cancer screening, diagnosis and treatment, more deliberate offering of appropriate surgical and medical treatments, and improved availability of survivorship and palliative care.SUMMARY:Given ongoing disparities in lung cancer care, research to determine best practices for narrowing these gaps and to guide policy change are an essential focus of future lung cancer research.

Background:Chronic inflammation may increase susceptibility to pneumonia. Research Question:To explore associations between clinical comorbidities, serum protein immunoassays, and long-term pneumonia risk. Methods:Framingham Heart Study Offspring Cohort participants ≥65 years were linked to their Centers for Medicare Services claims data. Clinical data and 88 serum protein immunoassays were evaluated for associations with 10-year incident pneumonia risk using Fine-Gray models for competing risks of death and least absolute shrinkage and selection operators for covariate selection. Results:We identified 1,370 participants with immunoassays and linkage to Medicare data. During 10 years of follow up, 428 (31%) participants had a pneumonia diagnosis. Chronic pulmonary disease [subdistribution hazard ratio (SHR) 1.87; 95% confidence interval (CI), 1.33-2.61], current smoking (SHR 1.79, CI 1.31-2.45), heart failure (SHR 1.74, CI 1.10-2.74), atrial fibrillation/flutter (SHR 1.43, CI 1.06-1.93), diabetes (SHR 1.36, CI 1.05-1.75), hospitalization within one year (SHR 1.34, CI 1.09-1.65), and age (SHR 1.06 per year, CI 1.04-1.08) were associated with pneumonia. Three baseline serum protein measurements were associated with pneumonia risk independent of measured clinical factors: growth differentiation factor 15 (SHR 1.32; CI 1.02-1.69), C-reactive protein (SHR 1.16, CI 1.06-1.27) and matrix metallopeptidase 8 (SHR 1.14, CI 1.01-1.30). Addition of C-reactive protein to the clinical model improved prediction (Akaike information criterion 4950 from 4960; C-statistic of 0.64 from 0.62). Conclusions:Clinical comorbidities and serum immunoassays were predictive of pneumonia risk. C-reactive protein, a routinely-available measure of inflammation, modestly improved pneumonia risk prediction over clinical factors. Our findings support the hypothesis that prior inflammation may increase the risk of pneumonia.

Background: Lung nodules are common incidental findings, and timely evaluation is critical to ensure diagnosis of localized-stage and potentially curable lung cancers. Rates of guideline-concordant lung nodule evaluation are low, and the risk of delayed evaluation is higher for minoritized groups. Objectives: To summarize the existing evidence, identify knowledge gaps, and prioritize research questions related to interventions to reduce disparities in lung nodule evaluation. Methods: A multidisciplinary committee was convened to review the evidence and identify key knowledge gaps in four domains: 1) research methodology, 2) patient-level interventions, 3) clinician-level interventions, and 4) health system-level interventions. A modified Delphi approach was used to identify research priorities. Results: Key knowledge gaps included 1) a lack of standardized approaches to identify factors associated with lung nodule management disparities, 2) limited data evaluating the role of social determinants of health on disparities in lung nodule management, 3) a lack of certainty regarding the optimal strategy to improve patient-clinician communication and information transmission and/or retention, and 4) a paucity of information on the impact of patient navigators and culturally trained multidisciplinary teams. Conclusions: This statement outlines a research agenda intended to stimulate high-impact studies of interventions to mitigate disparities in lung nodule evaluation. Research questions were prioritized around the following domains: 1) need for methodologic guidelines for conducting research related to disparities in nodule management, 2) evaluating how social determinants of health influence lung nodule evaluation, 3) studying approaches to improve patient-clinician communication, and 4) evaluating the utility of patient navigators and culturally enriched multidisciplinary teams to reduce disparities.

Perspective from A Prediction Model for Lung Cancer Diagnosis that Integrates Genomic and Clinical Features

Tobacco smoking is an important risk factor for disease, but inaccurate smoking history data in the electronic health record (EHR) limits the reach of lung cancer screening and tobacco cessation interventions. Patient-generated health data is a novel approach to documenting smoking history; however, the comparative effectiveness of different approaches is unclear. We designed a quality improvement intervention to evaluate the effectiveness of portal questionnaires compared to text message-based surveys, to compare message frames, and to evaluate the completeness of patient-generated smoking histories. We evaluated an EHR portal questionnaire and a text survey. The portal questionnaire employed a “helpfulness” message, while the text survey tested frame types informed by behavior economics - “gain”, “loss” and “helpfulness”- and nudge messaging. The primary outcome was response rate for each modality and framing type. Completeness and consistency with documented structured smoking data was also evaluated. Participants were more likely to respond to the text survey (19.1%) compared to the portal questionnaire (6.9%). Across all survey rounds, patients were less responsive to the “helpfulness” frame compared to the “gain” frame (OR= 0.29, p < 0.05) and “loss” frame (OR =0.32, p <0.05). Compared to the structured data in the EMR, the patient-generated data was significantly more likely to be complete enough to determine lung cancer screening eligibility. We found that a learning health system approach using patient-generated data is a feasible way to engage patients and collect complete smoking histories. Patients are likely to respond to a text survey using “gain” or “loss” framing to report detailed smoking histories. Optimizing a text message approach to collect medical information has implications for preventative and follow-up clinical care beyond smoking histories, lung cancer screening, and smoking cessation therapy.

Purpose Medicare requires tobacco dependence counseling and shared decision-making (SDM) for lung cancer screening (LCS) reimbursement. We hypothesized that initiating SDM during inpatient tobacco treatment visits would increase LCS among patients with barriers to proactively seeking outpatient preventive care. Methods We collected baseline assessments and performed two pilot randomized trials at our safety-net hospital. Pilot 1 tested feasibility, acceptability, and preliminary efficacy of a nurse practitioner initiating SDM for LCS during hospitalization (Inpatient SDM). We collected qualitative data on barriers encountered during Pilot 1. Pilot 2 added a community health worker (CHW) to address barriers to LCS completion (Inpatient SDM + CHW-navigation). For both studies, preliminary efficacy was an intention-to-treat analysis of LCS completion at 3 months between intervention and comparator (furnishing of LCS decision aid only) groups. Results Baseline assessments showed that patients preferred in-person LCS discussions versus self-reviewing materials; overall 20% had difficulty understanding written information. In Pilot 1, 4% (2/52) in Inpatient SDM versus 2% (1/48, comparator) completed LCS (p = 0.6), despite 89% (89/100) desiring LCS. Primary care providers noted that competing priorities and patient factors (e.g., social barriers to keeping appointments) prevented the intervention from working as intended. In Pilot 2, 50% (5/10) in Inpatient SDM + CHW-navigation versus 9% (1/11, comparator) completed LCS (p < 0.05). Many patients were ineligible due to recent diagnostic chest CT (Pilot 1: 255/659; Pilot 2: 239/527). Conclusions Inpatient SDM + CHW-navigation shows promise to improve LCS rates among underserved patients who smoke, but feasibility is limited by recent diagnostic chest CT among inpatients. Implementing CHW-navigation in other clinical settings may facilitate LCS for underserved patients. Trail registration ClinicalTrials.gov Identifier: NCT03276806 (8 September 2017); NCT03793894 (4 January 2019).

INTRODUCTION/BACKGROUND:The USPSTF (United States Preventive Services Task Force) guidelines suggest criteria centering on smoking status and age to select patients for lung cancer screening. Despite the significant advances in screening with low-dose computed tomography (LDCT), cancer detection rate is low (1.1%), highlighting the need to investigate possible ways to refine the current lung cancer screening strategy. Our aim was to determine clinical risk factors predictive of lung cancer in an urban safety-net hospital.MATERIALS AND METHODS:We performed a retrospective chart review of 2847 patients who received LDCT screening for lung cancer between 3/1/2015 and 12/31/2019. Patient demographics and medical history were collected. A bivariate logistic regression was used to evaluate predictors of lung cancer.RESULTS:Compared to the National Lung Cancer Screening Trial (NLST) population, our screening cohort had significantly more African Americans (38.2% vs. 4.5%, P < .0001), more obesity (32.7% vs. 28.3%, P < .0001), and higher rates of chronic obstructive pulmonary disease (COPD) (45.9% vs. 5.0%, P < .0001). The strongest predictors of lung cancer were COPD (odds ratio [OR] = 2.14, P < .0001) and a family history of lung cancer (OR = 2.77, P < .0001). Age (OR = 1.04, P< .001) and pack years (OR = 1.01, P< .001) were less predictive.CONCLUSION:A diagnosis of COPD and family history of lung cancer were most predictive of lung cancer in a screening cohort at our urban safety-net hospital. Future studies should focus on whether inclusion of these additional risk-factors improves proportion of lung cancer detected via screening.

BACKGROUND Lung CT Screening Reporting and Data System (LungRADS) Category 4 represents lung nodules with the highest likelihood of cancer. For LungRADS-4 lesions, if positron emission tomography (PET) is negative, no uniform guideline currently exists on subsequent follow-up, particularly whether the surveillance interval can be extended. We sought to investigate the incidence of cancer, our surveillance practice, and any clinical factors associated with cancer in this patient subset. METHODS We retrospectively stratified LungRADS-4 patients screened at our institution from March 2015 to February 2019 into subgroups: PET positive, PET negative, and no PET performed. PET negativity was defined as the absence of a radiologist's suspicion or a maximum standardized uptake value at or below the mediastinal value. RESULTS Of the 191 LungRADS-4 patients identified, 67 (35.1%) met the criteria for PET negativity. Cancer was diagnosed in 28.8% of the entire cohort (55/191), 77.8% of the PET-positive subgroup (35/45), 22.4% of the PETnegative subgroup (15/67), and 6.3% of the no PET subgroup (5/79). The most common follow-up modality after a negative PET was a computed tomography (47/67, 70.1 %), with a median interval of 3.1 months. Clinical variables including nodule location/size, chronic obstructive pulmonary disease, family history of lung cancer, pack-years, and number of years quit in former smokers were not significantly associated with greater cancer risk among the PET-negative subgroup. CONCLUSIONS For LungRADS-4/PET-negative lesions the cancer risk remained high despite a lack of activity on PET. As such we believe the current surveillance practice of continuing to follow LungRADS-4/PET-negative patients as LungRADS-4 patients is appropriate. (C) 2022 by The Society of Thoracic Surgeons