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Kearney L, Bolton RE, Núñez ER, Boudreau JH, Sliwinski S, Herbst AN, Caverly TJ, Wiener RS. Tackling Guideline Non-concordance: Primary Care Barriers to Incorporating Life Expectancy into Lung Cancer Screening Decision-Making-A Qualitative Study. J Gen Intern Med 2024; 39:2284-2291. [PMID: 38459413 PMCID: PMC11347517 DOI: 10.1007/s11606-024-08705-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024]
Abstract
BACKGROUND Primary care providers (PCPs) are often the first point of contact for discussing lung cancer screening (LCS) with patients. While guidelines recommend against screening people with limited life expectancy (LLE) who are less likely to benefit, these patients are regularly referred for LCS. OBJECTIVE We sought to understand barriers PCPs face to incorporating life expectancy into LCS decision-making for patients who otherwise meet eligibility criteria, and how a hypothetical point-of-care tool could support patient selection. DESIGN Qualitative study based on semi-structured telephone interviews. PARTICIPANTS Thirty-one PCPs who refer patients for LCS, from six Veterans Health Administration facilities. APPROACH We thematically analyzed interviews to understand how PCPs incorporated life expectancy into LCS decision-making and PCPs' receptivity to a point-of-care tool to support patient selection. Final themes were organized according to the Cabana et al. framework Why Don't Physicians Follow Clinical Practice Guidelines, capturing the influence of clinician knowledge, attitudes, and behavior on LCS appropriateness determinations. KEY RESULTS PCP referrals to LCS for patients with LLE were influenced by limited knowledge of the life expectancy threshold at which patients are less likely to benefit from LCS, discomfort estimating life expectancy, fear of missing cancer at the point of early detection, and prioritization of factors such as quality of life, patient values, clinician-patient relationship, and family support. PCPs were receptive to a decision support tool to inform and communicate LCS appropriateness decisions if easy to use and integrated into clinical workflows. CONCLUSIONS Our study suggests knowledge gaps and attitudes may drive decisions to offer screening despite LLE, a behavior counter to guideline recommendations. Integrating a LCS decision support tool that incorporates life expectancy within the electronic medical record and existing clinical workflows may be one acceptable solution to improve guideline concordance and increase confidence in selecting high benefit patients for LCS.
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Affiliation(s)
- Lauren Kearney
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA.
- The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA.
| | - Rendelle E Bolton
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
- The Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA
| | - Eduardo R Núñez
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
- The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
- Department of Healthcare Delivery and Population Sciences, University of Massachusetts Chan Medical School-Baystate, Springfield, MA, USA
| | - Jacqueline H Boudreau
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
| | - Samantha Sliwinski
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
| | - Abigail N Herbst
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
| | - Tanner J Caverly
- VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC, USA
- University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA and VA Bedford Healthcare System, Bedford, MA, USA
- The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC, USA
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Kearney LE, Belancourt P, Katki HA, Tanner NT, Wiener RS, Robbins HA, Landy R, Caverly TJ. The Development and Performance of Alternative Criteria for Lung Cancer Screening. Ann Intern Med 2024. [PMID: 39159457 DOI: 10.7326/m23-3250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/21/2024] Open
Abstract
BACKGROUND The recommendation for lung cancer screening (LCS) developed by the U.S. Preventive Services Task Force (USPSTF) may exclude some high-benefit people. OBJECTIVE To determine whether alternative criteria can identify these high-benefit people. DESIGN Model-based projections. SETTING United States. PARTICIPANTS People from the 1997-2014 National Health Interview Survey (NHIS) to develop alternative criteria using fast-and-frugal tree algorithms and from the 2014-2018 NHIS and the 2022 Behavioral Risk Factor Surveillance System for comparisons of USPSTF criteria versus alternative criteria. MEASUREMENTS Life-years gained from LCS were estimated using the life-years gained from screening computed tomography (LYFS-CT) model. "High-benefit" was defined as gaining an average of at least 16.2 days of life from 3 annual screenings, which reflects high lung cancer risk and substantial life gains if lung cancer is detected by screening. RESULTS The final alternative criteria were 1) people who smoked any amount each year for at least 40 years, or 2) people aged 60 to 80 years with at least 40 pack-years of smoking. The USPSTF and alternative criteria selected similar numbers of people for LCS. Compared with the USPSTF criteria, the alternative criteria had higher sensitivity (91% vs. 78%; P < 0.001) and specificity (86% vs. 84%; P < 0.001) for identifying high-benefit people. For racial and ethnic minorities, the alternative criteria provided greater gains in sensitivity than the USPSTF criteria (Black: 83% vs. 56% [P < 0.001]; Hispanic: 95% vs. 73% [P = 0.086]; Asian: 94% vs. 68% [P = 0.171]) at similar specificity. The alternative criteria identify high-risk, high-benefit groups excluded by the USPSTF criteria (those with a smoking duration of ≥40 years but <20 pack-years and a quit history of >15 years), many of whom are members of racial and ethnic minorities. LIMITATION The results were based on model projections. CONCLUSION These results suggest that simple alternative LCS criteria can identify substantially more high-benefit people, especially in some racial and ethnic groups. PRIMARY FUNDING SOURCE U.S. Department of Veterans Affairs Lung Precision Oncology Program.
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Affiliation(s)
- Lauren E Kearney
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, and The Pulmonary Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts (L.E.K.)
| | - Patrick Belancourt
- VA Ann Arbor Healthcare System and Center for Clinical Management Research, Ann Arbor, Michigan (P.B.)
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland (H.A.K., R.L.)
| | - Nichole T Tanner
- Health Equity and Rural Outreach Innovation Center, Ralph H. Johnson Veterans Affairs Healthcare System, and Division of Pulmonary, Critical Care and Sleep Medicine, Medical University of South Carolina, Charleston, South Carolina (N.T.T.)
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts; The Pulmonary Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts; and National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC (R.S.W.)
| | - Hilary A Robbins
- International Agency for Research on Cancer, Lyon, France (H.A.R.)
| | - Rebecca Landy
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland (H.A.K., R.L.)
| | - Tanner J Caverly
- VA Ann Arbor Healthcare System, Ann Arbor, Michigan; National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC; and University of Michigan Medical School, Ann Arbor, Michigan (T.J.C.)
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Tiase VL, Richards G, Taft T, Stevens L, Balbin C, Kaphingst KA, Fagerlin A, Caverly T, Kukhareva P, Flynn M, Butler JM, Kawamoto K. Patient Perspectives on a Patient-Facing Tool for Lung Cancer Screening. Health Expect 2024; 27:e14143. [PMID: 38992907 PMCID: PMC11239535 DOI: 10.1111/hex.14143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 06/02/2024] [Accepted: 06/27/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Individuals with high risk for lung cancer may benefit from lung cancer screening, but there are associated risks as well as benefits. Shared decision-making (SDM) tools with personalized information may provide key support for patients. Understanding patient perspectives on educational tools to facilitate SDM for lung cancer screening may support tool development. AIM This study aimed to explore patient perspectives related to a SDM tool for lung cancer screening using a qualitative approach. METHODS We elicited patient perspectives by showing a provider-facing SDM tool. Focus group interviews that ranged in duration from 1.5 to 2 h were conducted with 23 individuals with high risk for lung cancer. Data were interpreted inductively using thematic analysis to identify patients' thoughts on and desires for a patient-facing SDM tool. RESULTS The findings highlight that patients would like to have educational information related to lung cancer screening. We identified several key themes to be considered in the future development of patient-facing tools: barriers to acceptance, preference against screening and seeking empowerment. One further theme illustrated effects of patient-provider relationship as a limitation to meeting lung cancer screening information needs. Participants also noted several suggestions for the design of technology decision aids. CONCLUSION These findings suggest that patients desire additional information on lung cancer screening in advance of clinical visits. However, there are several issues that must be considered in the design and development of technology to meet the information needs of patients for lung cancer screening decisions. PATIENT OR PUBLIC CONTRIBUTION Patients, service users, caregivers or members of the public were not involved in the study design, conduct, analysis or interpretation of the data. However, clinical experts in health communication provided detailed feedback on the study protocol, including the focus group approach. The study findings contribute to a better understanding of patient expectations for lung cancer screening decisions and may inform future development of tools for SDM.
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Affiliation(s)
- Victoria L Tiase
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Grace Richards
- Department of Biomedical Engineering, University of Utah, Salt Lake City, Utah, USA
| | - Teresa Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Leticia Stevens
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Christian Balbin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Kimberly A Kaphingst
- Department of Communication and Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Tanner Caverly
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Michael Flynn
- Departments of Internal Medicine and Pediatrics, Community Physicians Group, University of Utah Health, Salt Lake City, Utah, USA
| | - Jorie M Butler
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
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Caverly TJ, Wiener RS, Kumbier K, Lowery J, Fagerlin A. Prediction-Augmented Shared Decision-Making and Lung Cancer Screening Uptake. JAMA Netw Open 2024; 7:e2419624. [PMID: 38949809 DOI: 10.1001/jamanetworkopen.2024.19624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
Importance Addressing poor uptake of low-dose computed tomography lung cancer screening (LCS) is critical, especially for those having the most to gain-high-benefit persons with high lung cancer risk and life expectancy more than 10 years. Objective To assess the association between LCS uptake and implementing a prediction-augmented shared decision-making (SDM) tool, which enables clinicians to identify persons predicted to be at high benefit and encourage LCS more strongly for these persons. Design, Setting, and Participants Quality improvement interrupted time series study at 6 Veterans Affairs sites that used a standard set of clinical reminders to prompt primary care clinicians and screening coordinators to engage in SDM for LCS-eligible persons. Participants were persons without a history of LCS who met LCS eligibility criteria at the time (aged 55-80 years, smoked ≥30 pack-years, and current smoking or quit <15 years ago) and were not documented to be an inappropriate candidate for LCS by a clinician during October 2017 through September 2019. Data were analyzed from September to November 2023. Exposure Decision support tool augmented by a prediction model that helps clinicians personalize SDM for LCS, tailoring the strength of screening encouragement according to predicted benefit. Main outcome and measure LCS uptake. Results In a cohort of 9904 individuals, the median (IQR) age was 64 (57-69) years; 9277 (94%) were male, 1537 (16%) were Black, 8159 (82%) were White, 5153 (52%) were predicted to be at intermediate (preference-sensitive) benefit and 4751 (48%) at high benefit, and 1084 (11%) received screening during the study period. Following implementation of the tool, higher rates of LCS uptake were observed overall along with an increase in benefit-based LCS uptake (higher screening uptake among persons anticipated to be at high benefit compared with those at intermediate benefit; primary analysis). Mean (SD) predicted probability of getting screened for a high-benefit person was 24.8% (15.5%) vs 15.8% (11.8%) for a person at intermediate benefit (mean absolute difference 9.0 percentage points; 95% CI, 1.6%-16.5%). Conclusions and Relevance Implementing a robust approach to personalized LCS, which integrates SDM, and a decision support tool augmented by a prediction model, are associated with improved uptake of LCS and may be particularly important for those most likely to benefit. These findings are timely given the ongoing poor rates of LCS uptake.
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Affiliation(s)
- Tanner J Caverly
- Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor
| | - Renda S Wiener
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts
- Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts
| | - Kyle Kumbier
- Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Julie Lowery
- Center for Clinical Management Research, Department of Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Angela Fagerlin
- University of Utah School of Medicine, Salt Lake City
- Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, Department of Veterans Affairs Salt Lake City Healthcare System, Salt Lake City, Utah
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Haaf KT, de Nijs K, Simoni G, Alban A, Cao P, Sun Z, Yong J, Jeon J, Toumazis I, Han SS, Gazelle GS, Kong CY, Plevritis SK, Meza R, de Koning HJ. The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations. Med Decis Making 2024; 44:497-511. [PMID: 38738534 PMCID: PMC11281869 DOI: 10.1177/0272989x241249182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Abstract
BACKGROUND Recommendations regarding personalized lung cancer screening are being informed by natural-history modeling. Therefore, understanding how differences in model assumptions affect model-based personalized screening recommendations is essential. DESIGN Five Cancer Intervention and Surveillance Modeling Network (CISNET) models were evaluated. Lung cancer incidence, mortality, and stage distributions were compared across 4 theoretical scenarios to assess model assumptions regarding 1) sojourn times, 2) stage-specific sensitivities, and 3) screening-induced lung cancer mortality reductions. Analyses were stratified by sex and smoking behavior. RESULTS Most cancers had sojourn times <5 y (model range [MR]; lowest to highest value across models: 83.5%-98.7% of cancers). However, cancer aggressiveness still varied across models, as demonstrated by differences in proportions of cancers with sojourn times <2 y (MR: 42.5%-64.6%) and 2 to 4 y (MR: 28.8%-43.6%). Stage-specific sensitivity varied, particularly for stage I (MR: 31.3%-91.5%). Screening reduced stage IV incidence in most models for 1 y postscreening; increased sensitivity prolonged this period to 2 to 5 y. Screening-induced lung cancer mortality reductions among lung cancers detected at screening ranged widely (MR: 14.6%-48.9%), demonstrating variations in modeled treatment effectiveness of screen-detected cases. All models assumed longer sojourn times and greater screening-induced lung cancer mortality reductions for women. Models assuming differences in cancer epidemiology by smoking behaviors assumed shorter sojourn times and lower screening-induced lung cancer mortality reductions for heavy smokers. CONCLUSIONS Model-based personalized screening recommendations are primarily driven by assumptions regarding sojourn times (favoring longer intervals for groups more likely to develop less aggressive cancers), sensitivity (higher sensitivities favoring longer intervals), and screening-induced mortality reductions (greater reductions favoring shorter intervals). IMPLICATIONS Models suggest longer screening intervals may be feasible and benefits may be greater for women and light smokers. HIGHLIGHTS Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.
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Affiliation(s)
- Kevin ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Koen de Nijs
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Giulia Simoni
- Department of Biomedical Data Sciences, Stanford University, Stanford, California
| | - Andres Alban
- MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts
| | - Pianpian Cao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Zhuolu Sun
- Canadian Partnership Against Cancer, Toronto, ON, Canada
| | - Jean Yong
- Canadian Partnership Against Cancer, Toronto, ON, Canada
| | - Jihyoun Jeon
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Iakovos Toumazis
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Summer S. Han
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California
| | - G. Scott Gazelle
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Chung Ying Kong
- Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York
| | - Sylvia K. Plevritis
- Department of Biomedical Data Sciences, Stanford University, Stanford, California
| | - Rafael Meza
- Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada
- School of Population and Public Health, University of British Columbia, British Columbia Canada
| | - Harry J. de Koning
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
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Hsin-Hung C, En-Kuei T, Yun-Ju W, Fu-Zong W. Impact of annual trend volume of low-dose computed tomography for lung cancer screening on overdiagnosis, overmanagement, and gender disparities. Cancer Imaging 2024; 24:73. [PMID: 38867342 PMCID: PMC11170916 DOI: 10.1186/s40644-024-00716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 06/05/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND With the increasing prevalence of nonsmoking-related lung cancer in Asia, Asian countries have increasingly adopted low-dose computed tomography (LDCT) for lung cancer screening, particularly in private screening programs. This study examined how annual LDCT volume affects lung cancer stage distribution, overdiagnosis, and gender disparities using a hospital-based lung cancer database. METHODS This study analyzed the annual utilized LDCT volume, clinical characteristics of lung cancer, stage shift distribution, and potential overdiagnosis. At the individual level, this study also investigated the relationship between stage 0 lung cancer (potential strict definition regarding overdiagnosis) and the clinical characteristics of lung cancer. RESULTS This study reviewed the annual trend of 4971 confirmed lung cancer cases from 2008 to 2021 and conducted a link analysis with an LDCT imaging examination database over these years. As the volume of lung cancer screenings has increased over the years, the number and proportion of stage 0 lung cancers have increased proportionally. Our study revealed that the incidence of stage 0 lung cancer increased with increasing LDCT scan volume, particularly during the peak growth period from 2017 to 2020. Conversely, stage 4 lung cancer cases remained consistent across different time intervals. Furthermore, the increase in the lung cancer screening volume had a more pronounced effect on the increase in stage 0 lung cancer cases among females than it had among males. The estimated potential for overdiagnosis brought about by the screening process, compared to non-participating individuals, ranged from an odds ratio of 7.617 to one of 17.114. Both strict and lenient definitions of overdiagnosis (evaluating cases of stage 0 lung cancer and stages 0 to 1 lung cancer) were employed. CONCLUSIONS These results provide population-level evidence of potential lung cancer overdiagnosis in the Taiwanese population due to the growing use of LDCT screening, particularly concerning the strict definition of stage 0 lung cancer. The impact was greater in the female population than in the male population, especially among females younger than 40 years. To improve lung cancer screening in Asian populations, creating risk-based prediction models for smokers and nonsmokers, along with gender-specific strategies, is vital for ensuring survival benefits and minimizing overdiagnosis.
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Affiliation(s)
- Chen Hsin-Hung
- Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, 813414, Taiwan
| | - Tang En-Kuei
- Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, 813414, Taiwan
| | - Wu Yun-Ju
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
| | - Wu Fu-Zong
- Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Faculty of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Núñez ER, Ito Fukunaga M, Stevens GA, Yang JK, Reid SE, Spiegel JL, Ingemi MR, Wiener RS. Review of Interventions That Improve Uptake of Lung Cancer Screening: A Cataloging of Strategies That Have Been Shown to Work (or Not). Chest 2024:S0012-3692(24)00654-8. [PMID: 38797278 DOI: 10.1016/j.chest.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/08/2024] [Accepted: 04/16/2024] [Indexed: 05/29/2024] Open
Abstract
TOPIC IMPORTANCE Lung cancer screening (LCS) has the potential to decrease mortality from lung cancer by 20%. Yet, more than a decade since LCS was established as an evidence-based practice, < 20% of the eligible population in the United States has been screened. This review focuses on critically appraising interventions that have been designed to increase the initial uptake of LCS, including how they address known barriers to LCS and their effectiveness in overcoming these barriers. REVIEW FINDINGS Studies were categorized based on the primary barriers that they addressed: (1) identifying eligible patients (including enhancing awareness through smoking history collection, outreach, and education), (2) shared decision-making-related interventions, and (3) patient navigation interventions. Four of the studies included multicomponent interventions, which often included patient navigation as one of the components. Overall, the effectiveness of the studies reviewed at improving LCS uptake generally was modest and was limited by the multilevel barriers that need to be overcome. Multicomponent interventions generally were more effective at improving LCS uptake, but most studies still had relatively low completion of screening. SUMMARY Improving uptake of LCS requires learning from prior interventions to design multilevel interventions that address barriers to LCS at key steps and identifying which components of these interventions are effective and generalizable.
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Affiliation(s)
- Eduardo R Núñez
- University of Massachusetts Chan Medical School-Baystate, Springfield, MA.
| | | | - Gregg A Stevens
- University of Massachusetts Chan Medical School Worcester, MA
| | - James K Yang
- University of Massachusetts Chan Medical School-Baystate, Springfield, MA
| | - Sarah E Reid
- University of Massachusetts Chan Medical School Worcester, MA
| | - Jennifer L Spiegel
- University of Massachusetts Chan Medical School Worcester, MA; School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Molly R Ingemi
- University of Massachusetts Chan Medical School-Baystate, Springfield, MA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA; The Pulmonary Center, Boston University School of Medicine, Boston, MA; National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
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Núñez ER, Bolton RE, Boudreau JH, Sliwinski SK, Herbst AN, Kearney LE, Caverly TJ, Wiener RS. "It Can't Hurt!": Why Many Patients With Limited Life Expectancy Decide to Accept Lung Cancer Screening. Ann Fam Med 2024; 22:95-102. [PMID: 38527813 PMCID: PMC11237214 DOI: 10.1370/afm.3081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/01/2023] [Accepted: 11/13/2023] [Indexed: 03/27/2024] Open
Abstract
PURPOSE Lung cancer screening (LCS) has less benefit and greater potential for iatrogenic harm among people with multiple comorbidities and limited life expectancy. Yet, such individuals are more likely to undergo screening than healthier LCS-eligible people. We sought to understand how patients with marginal LCS benefit conceptualize their health and make decisions regarding LCS. METHODS We interviewed 40 people with multimorbidity and limited life expectancy, as determined by high Care Assessment Need scores, which predict 1-year risk of hospitalization or death. Patients were recruited from 6 Veterans Health Administration facilities after discussing LCS with their clinician. We conducted a thematic analysis using constant comparison to explore factors that influence LCS decision making. RESULTS Patients commonly held positive beliefs about screening and perceived LCS to be noninvasive. When posed with hypothetical scenarios of limited benefit, patients emphasized the nonlongevity benefits of LCS (eg, peace of mind, planning for the future) and generally did not consider their health status or life expectancy when making decisions regarding LCS. Most patients were unaware of possible additional evaluations or treatment of screen-detected findings, but when probed further, many expressed concerns about the potential need for multiple evaluations, referrals, or invasive procedures. CONCLUSIONS Patients in this study with multimorbidity and limited life expectancy were unaware of their greater risk of potential harm when accepting LCS. Given patient trust in clinician recommendations, it is important that clinicians engage patients with marginal LCS benefit in shared decision making, ensuring that their values of desiring more information about their health are weighed against potential harms from further evaluations.
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Affiliation(s)
- Eduardo R Núñez
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts
- Department of Healthcare Delivery and Population Sciences, University of Massachusetts Chan Medical School-Baystate, Springfield, Massachusetts
| | - Rendelle E Bolton
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
- The Heller School for Social Policy and Management, Brandeis University, Waltham, Massachusetts
| | - Jacqueline H Boudreau
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
| | - Samantha K Sliwinski
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
| | - Abigail N Herbst
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
| | - Lauren E Kearney
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts
| | - Tanner J Caverly
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
- VA Ann Arbor Healthcare System, Ann Arbor, Michigan
- University of Michigan School of Medicine, Ann Arbor, Michigan
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, Massachusetts and VA Bedford Healthcare System, Bedford, Massachusetts
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
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9
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Núñez ER, Zhang S, Glickman ME, Qian SX, Boudreau JH, Lindenauer PK, Slatore CG, Miller DR, Caverly TJ, Wiener RS. What Goes into Patient Selection for Lung Cancer Screening? Factors Associated with Clinician Judgments of Suitability for Screening. Am J Respir Crit Care Med 2024; 209:197-205. [PMID: 37819144 PMCID: PMC10806423 DOI: 10.1164/rccm.202301-0155oc] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/11/2023] [Indexed: 10/13/2023] Open
Abstract
Rationale: Achieving the net benefit of lung cancer screening (LCS) depends on optimizing patient selection. Objective: To identify factors associated with clinician assessments that a patient was unlikely to benefit from LCS ("LCS-inappropriate") because of comorbidities or limited life expectancy. Methods: Retrospective analysis of patients assessed for LCS at 30 Veterans Health Administration facilities from January 1, 2015 to February 1, 2021. We conducted hierarchical mixed-effects logistic regression analyses to determine factors associated with clinicians' designations of LCS inappropriateness (primary outcome), accounting for 3-year predicted probability (i.e., competing risk) of non-lung cancer death. Measurements and Main Results: Among 38,487 LCS-eligible patients, 1,671 (4.3%) were deemed LCS-inappropriate by clinicians, whereas 4,383 (11.4%) had an estimated 3-year competing risk of non-lung cancer death greater than 20%. Patients with higher competing risks of non-lung cancer death were more likely to be deemed LCS-inappropriate (odds ratio [OR], 2.66; 95% confidence interval [CI], 2.32-3.05). Older patients (ages 75-80; OR, 1.45; 95% CI, 1.18-1.78) and those with interstitial lung disease (OR, 1.98; 95% CI, 1.51-2.59) were more likely to be deemed LCS-inappropriate than would be explained by competing risk of non-lung cancer death, whereas patients currently smoking (OR, 0.65; 95% CI, 0.58-0.73) were less likely to be deemed LCS-inappropriate, suggesting that clinicians over- or underweighted these factors. The probability of being deemed LCS-inappropriate varied from 0.4% to 74%, depending on the clinician making the assessment (median OR, 3.07; 95% CI, 2.89-3.25). Conclusion: Concerningly, the likelihood that a patient is deemed LCS-inappropriate is more strongly associated with the clinician making the assessment than with patient characteristics. Patient selection may be optimized by providing decision support to help clinicians assess net LCS benefit.
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Affiliation(s)
- Eduardo R. Núñez
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
- The Pulmonary Center, School of Medicine, Boston University, Boston, Massachusetts
- Department of Healthcare Delivery and Population Sciences, Chan Medical School-Baystate, University of Massachusetts, Springfield, Massachusetts
| | - Sanqian Zhang
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
- Department of Statistics, Harvard University, Cambridge, Massachusetts
| | - Mark E. Glickman
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
- Department of Statistics, Harvard University, Cambridge, Massachusetts
| | - Shirley X. Qian
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
| | - Jacqueline H. Boudreau
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
| | - Peter K. Lindenauer
- Department of Healthcare Delivery and Population Sciences, Chan Medical School-Baystate, University of Massachusetts, Springfield, Massachusetts
| | - Christopher G. Slatore
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland Oregon
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
- Division of Pulmonary and Critical Care Medicine, Oregon Health and Science University, Portland, Oregon
| | - Donald R. Miller
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
- Zuckerberg College of Health Sciences, University of Massachusetts, Lowell, Massachusetts
| | - Tanner J. Caverly
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
- VA Ann Arbor Healthcare System, Ann Arbor, Michigan; and
- School of Medicine, University of Michigan, Ann Arbor, Michigan
| | - Renda Soylemez Wiener
- Center for Healthcare Organization and Implementation Research, VA Boston and Bedford Healthcare Systems, Boston, Massachusetts
- VA Bedford Healthcare System, Bedford, Massachusetts
- The Pulmonary Center, School of Medicine, Boston University, Boston, Massachusetts
- National Center for Lung Cancer Screening, Veterans Health Administration, Washington, DC
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10
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Meza R, Cao P, de Nijs K, Jeon J, Smith RA, ten Haaf K, de Koning H. Assessing the impact of increasing lung screening eligibility by relaxing the maximum years-since-quit threshold: A simulation modeling study. Cancer 2024; 130:244-255. [PMID: 37909874 PMCID: PMC11188688 DOI: 10.1002/cncr.34925] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/10/2023] [Accepted: 05/02/2023] [Indexed: 11/03/2023]
Abstract
BACKGROUND In 2021, the US Preventive Services Task Force expanded its lung screening recommendation to include persons aged 50-80 years who had ever smoked and had at least 20 pack-years of exposure and less than 15 years since quitting (YSQ). However, studies have suggested that screening persons who formerly smoked with longer YSQ could be beneficial. METHODS The authors used two validated lung cancer models to assess the benefits and harms of screening using various YSQ thresholds (10, 15, 20, 25, 30, and no YSQ) and the age at which screening was stopped. The impact of enforcing the YSQ criterion only at entry, but not at exit, also was evaluated. Outcomes included the number of screens, the percentage ever screened, screening benefits (lung cancer deaths averted, life-years gained), and harms (false-positive tests, overdiagnosed cases, radiation-induced lung cancer deaths). Sensitivity analyses were conducted to evaluate the effect of restricting screening to those who had at least 5 years of life expectancy. RESULTS As the YSQ criterion was relaxed, the number of screens and the benefits and harms of screening increased. Raising the age at which to stop screening age resulted in additional benefits but with more overdiagnosis, as expected, because screening among those older than 80 years increased. Limiting screening to those who had at least 5 years of life expectancy would maintain most of the benefits while considerably reducing the harms. CONCLUSIONS Expanding screening to persons who formerly smoked and have greater than 15 YSQ would result in considerable increases in deaths averted and life-years gained. Although additional harms would occur, these could be moderated by ensuring that screening is restricted to only those with reasonable life expectancy.
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Affiliation(s)
- Rafael Meza
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Koen de Nijs
- Erasmus MC–University Medical Center, Rotterdam, The Netherlands
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Robert A. Smith
- Early Cancer Detection Science Department, American Cancer Society, Atlanta, GA
| | - Kevin ten Haaf
- Erasmus MC–University Medical Center, Rotterdam, The Netherlands
| | - Harry de Koning
- Erasmus MC–University Medical Center, Rotterdam, The Netherlands
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11
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Skurla SE, Leishman NJ, Fagerlin A, Wiener RS, Lowery J, Caverly TJ. Clinician Perceptions on Using Decision Tools to Support Prediction-Based Shared Decision Making for Lung Cancer Screening. MDM Policy Pract 2024; 9:23814683241252786. [PMID: 38779527 PMCID: PMC11110512 DOI: 10.1177/23814683241252786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 04/08/2024] [Indexed: 05/25/2024] Open
Abstract
Background Considering a patient's full risk factor profile can promote personalized shared decision making (SDM). One way to accomplish this is through encounter tools that incorporate prediction models, but little is known about clinicians' perceptions of the feasibility of using these tools in practice. We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS). Design We conducted a qualitative study based on field notes from academic detailing visits during a multisite quality improvement program. The detailer engaged one-on-one with 96 primary care clinicians across multiple Veterans Affairs sites (7 medical centers and 6 outlying clinics) to get feedback on 1) the rationale for prediction-based LCS and 2) how to use the DecisionPrecision (DP) encounter tool with eligible patients to personalize LCS discussions. Results Thematic content analysis from detailing visit data identified 6 categories of clinician willingness to use the DP tool to personalize SDM for LCS (adoption potential), varying from "Enthusiastic Potential Adopter" (n = 18) to "Definite Non-Adopter" (n = 16). Many clinicians (n = 52) articulated how they found the concept of prediction-based SDM highly appealing. However, to varying degrees, nearly all clinicians identified challenges to incorporating such an approach in routine practice. Limitations The results are based on the clinician's initial reactions rather than longitudinal experience. Conclusions While many primary care clinicians saw real value in using prediction to personalize LCS decisions, more support is needed to overcome barriers to using encounter tools in practice. Based on these findings, we propose several strategies that may facilitate the adoption of prediction-based SDM in contexts such as LCS. Highlights Encounter tools that incorporate prediction models promote personalized shared decision making (SDM), but little is known about clinicians' perceptions of the feasibility of using these tools in practice.We examined how clinicians react to using one such encounter tool for personalizing SDM about lung cancer screening (LCS).While many clinicians found the concept of prediction-based SDM highly appealing, nearly all clinicians identified challenges to incorporating such an approach in routine practice.We propose several strategies to overcome adoption barriers and facilitate the use of prediction-based SDM in contexts such as LCS.
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Affiliation(s)
- Sarah E. Skurla
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | | | - Angela Fagerlin
- University of Utah School of Medicine, Salt Lake City, UT, USA
- Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, UT, USA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA, USA
- The Pulmonary Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Julie Lowery
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | - Tanner J. Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
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12
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Saini SD, Lewis CL, Kerr EA, Zikmund-Fisher BJ, Hawley ST, Forman JH, Zauber AG, Lansdorp-Vogelaar I, van Hees F, Saffar D, Myers A, Gauntlett LE, Lipson R, Kim HM, Vijan S. Personalized Multilevel Intervention for Improving Appropriate Use of Colorectal Cancer Screening in Older Adults: A Cluster Randomized Clinical Trial. JAMA Intern Med 2023; 183:1334-1342. [PMID: 37902744 PMCID: PMC10616770 DOI: 10.1001/jamainternmed.2023.5656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/01/2023] [Indexed: 10/31/2023]
Abstract
Importance Despite guideline recommendations, clinicians do not systematically use prior screening or health history to guide colorectal cancer (CRC) screening decisions in older adults. Objective To evaluate the effect of a personalized multilevel intervention on screening orders in older adults due for average-risk CRC screening. Design, Setting, and Participants Interventional 2-group parallel unmasked cluster randomized clinical trial conducted from November 2015 to February 2019 at 2 US Department of Veterans Affairs (VA) facilities: 1 academic VA medical center and 1 of its connected outpatient clinics. Randomization at the primary care physician/clinician (PCP) level, stratified by study site and clinical full-time equivalency. Participants were 431 average-risk, screen-due US veterans aged 70 to 75 years attending a primary care visit. Data analysis was performed from August 2018 to August 2023. Intervention The intervention group received a multilevel intervention including a decision-aid booklet with detailed information on screening benefits and harms, personalized for each participant based on age, sex, prior screening, and comorbidity. The control group received a multilevel intervention including a screening informational booklet. All participants received PCP education and system-level modifications to support personalized screening. Main Outcomes and Measures The primary outcome was whether screening was ordered within 2 weeks of clinic visit. Secondary outcomes were concordance between screening orders and screening benefit and screening utilization within 6 months. Results A total of 436 patients were consented, and 431 were analyzed across 67 PCPs. Patients had a mean (SD) age of 71.5 (1.7) years; 424 were male (98.4%); 374 were White (86.8%); 89 were college graduates (21.5%); and 351 (81.4%) had undergone prior screening. A total of 258 (59.9%) were randomized to intervention, and 173 (40.1%) to control. Screening orders were placed for 162 of 258 intervention patients (62.8%) vs 114 of 173 control patients (65.9%) (adjusted difference, -4.0 percentage points [pp]; 95% CI, -15.4 to 7.4 pp). In a prespecified interaction analysis, the proportion receiving orders was lower in the intervention group than in the control group for those in the lowest benefit quartile (59.4% vs 71.1%). In contrast, the proportion receiving orders was higher in the intervention group than in the control group for those in the highest benefit quartile (67.6% vs 52.2%) (interaction P = .049). Fewer intervention patients (106 of 256 [41.4%]) utilized screening overall at 6 months than controls (96 of 173 [55.9%]) (adjusted difference, -13.4 pp; 95% CI, -25.3 to -1.6 pp). Conclusions and Relevance In this cluster randomized clinical trial, patients who were presented with personalized information about screening benefits and harms in the context of a multilevel intervention were more likely to receive screening orders concordant with benefit and were less likely to utilize screening. Trial Registration ClinicalTrials.gov Identifier: NCT02027545.
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Affiliation(s)
- Sameer D. Saini
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
- Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
| | | | - Eve A. Kerr
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
- Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
| | - Brian J. Zikmund-Fisher
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor
| | - Sarah T. Hawley
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
- Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
| | - Jane H. Forman
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
| | - Ann G. Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Iris Lansdorp-Vogelaar
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Darcy Saffar
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
| | - Aimee Myers
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
| | - Lauren E. Gauntlett
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
| | - Rachel Lipson
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
| | - H. Myra Kim
- Center for Clinical Management Research, LTC Charles S. Kettles VA Healthcare System, Ann Arbor, Michigan
- Consulting for Statistics, Computing and Analytics Research (CSCAR), University of Michigan, Ann Arbor
| | - Sandeep Vijan
- Department of Internal Medicine, University of Michigan, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
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13
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Skolnick S, Cao P, Jeon J, Meza R. Contribution of smoking, disease history, and survival to lung cancer disparities in Black individuals. J Natl Cancer Inst Monogr 2023; 2023:204-211. [PMID: 37947334 PMCID: PMC10637023 DOI: 10.1093/jncimonographs/lgad016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/01/2023] [Accepted: 06/11/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American ("Black") people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities. METHODS We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences. RESULTS Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort. CONCLUSIONS After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.
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Affiliation(s)
- Sarah Skolnick
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
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14
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Susai CJ, Velotta JB, Sakoda LC. Clinical Adjuncts to Lung Cancer Screening: A Narrative Review. Thorac Surg Clin 2023; 33:421-432. [PMID: 37806744 PMCID: PMC10926946 DOI: 10.1016/j.thorsurg.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The updated US Preventive Services Task Force guidelines on lung cancer screening have significantly expanded the population of screening eligible adults, among whom the balance of benefits and harms associated with lung cancer screening vary considerably. Clinical adjuncts are additional information and tools that can guide decision-making to optimally screen individuals who are most likely to benefit. Proposed adjuncts include integration of clinical history, risk prediction models, shared-decision-making tools, and biomarker tests at key steps in the screening process. Although evidence regarding their clinical utility and implementation is still evolving, they carry significant promise in optimizing screening effectiveness and efficiency for lung cancer.
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Affiliation(s)
- Cynthia J Susai
- UCSF East Bay General Surgery, 1411 East 31st Street QIC 22134, Oakland, CA 94612, USA
| | - Jeffrey B Velotta
- Department of Thoracic Surgery, Kaiser Permanente Northern California, 3600 Broadway, Oakland, CA 94611, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, 2000 Broadway, Oakland, CA 94612, USA.
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15
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Kukhareva PV, Li H, Caverly TJ, Del Fiol G, Fagerlin A, Butler JM, Hess R, Zhang Y, Taft T, Flynn MC, Reddy C, Martin DK, Warner IA, Rodriguez-Loya S, Warner PB, Kawamoto K. Implementation of Lung Cancer Screening in Primary Care and Pulmonary Clinics: Pragmatic Clinical Trial of Electronic Health Record-Integrated Everyday Shared Decision-Making Tool and Clinician-Facing Prompts. Chest 2023; 164:1325-1338. [PMID: 37142092 PMCID: PMC10792294 DOI: 10.1016/j.chest.2023.04.040] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Although low-dose CT (LDCT) scan imaging lung cancer screening (LCS) can reduce lung cancer mortality, it remains underused. Shared decision-making (SDM) is recommended to assess the balance of benefits and harms for each patient. RESEARCH QUESTION Do clinician-facing electronic health record (EHR) prompts and an EHR-integrated everyday SDM tool designed to support routine incorporation of SDM into primary care improve LDCT scan imaging ordering and completion? STUDY DESIGN AND METHODS A preintervention and postintervention analysis was conducted in 30 primary care and four pulmonary clinics for visits with patients who met United States Preventive Services Task Force criteria for LCS. Propensity scores were used to adjust for covariates. Subgroup analyses were conducted based on the expected benefit from screening (high benefit vs intermediate benefit), pulmonologist involvement (ie, whether the patient was seen in a pulmonary clinic in addition to a primary care clinic), sex, and race and ethnicity. RESULTS In the 12-month preintervention phase among 1,090 eligible patients, 77 patients (7.1%) had LDCT scan imaging orders and 48 patients (4.4%) completed screenings. In the 9-month intervention phase among 1,026 eligible patients, 280 patients (27.3%) had LDCT scan imaging orders and 182 patients (17.7%) completed screenings. Adjusted ORs were 4.9 (95% CI, 3.4-6.9; P < .001) and 4.7 (95% CI, 3.1-7.1; P < .001) for LDCT imaging ordering and completion, respectively. Subgroup analyses showed increases in ordering and completion for all patient subgroups. In the intervention phase, the SDM tool was used by 23 of 102 ordering providers (22.5%) and for 69 of 274 patients (25.2%) for whom LDCT scan imaging was ordered and who needed SDM at the time of ordering. INTERPRETATION Clinician-facing EHR prompts and an EHR-integrated everyday SDM tool are promising approaches to improving LCS in the primary care setting. However, room for improvement remains. As such, further research is warranted. TRIAL REGISTRY ClinicalTrials.gov; No.: NCT04498052; URL: www. CLINICALTRIALS gov.
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Affiliation(s)
- Polina V Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Haojia Li
- Study Design and Biostatistics Center, University of Utah, Salt Lake City, UT
| | - Tanner J Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI; Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI; Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Salt Lake City VA Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, Salt Lake City, UT
| | - Jorie M Butler
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT; Department of Internal Medicine, University of Utah, Salt Lake City, UT; Geriatrics Research and Education Center, George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT; Department of Internal Medicine, University of Utah, Salt Lake City, UT
| | - Yue Zhang
- Study Design and Biostatistics Center, University of Utah, Salt Lake City, UT
| | - Teresa Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Michael C Flynn
- Department of Internal Medicine, University of Utah, Salt Lake City, UT; Department of Pediatrics, University of Utah, Salt Lake City, UT; Community Physicians Group, University of Utah Health, Salt Lake City, UT
| | | | - Douglas K Martin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Isaac A Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | | | - Phillip B Warner
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT.
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16
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Eberth JM, Zgodic A, Pelland SC, Wang SY, Miller DP. Outcomes of Shared Decision-Making for Low-Dose Screening for Lung Cancer in an Academic Medical Center. JOURNAL OF CANCER EDUCATION : THE OFFICIAL JOURNAL OF THE AMERICAN ASSOCIATION FOR CANCER EDUCATION 2023; 38:522-537. [PMID: 35488967 DOI: 10.1007/s13187-022-02148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/15/2022] [Indexed: 05/20/2023]
Abstract
Shared decision-making (SDM) helps patients weigh risks and benefits of screening approaches. Little is known about SDM visits between patients and healthcare providers in the context of lung cancer screening. This study explored the extent that patients were informed by their provider of the benefits and harms of lung cancer screening and expressed certainty about their screening choice. We conducted a survey with 75 patients from an academic medical center in the Southeastern U.S. Survey items included knowledge of benefits and harms of screening, patients' value elicitation during SDM visits, and decisional certainty. Patient and provider characteristics were collected through electronic medical records or self-report. Descriptive statistics, Kruskal-Wallis tests, and Pearson correlations between screening knowledge, value elicitation, and decisional conflict were calculated. The sample was predominately non-Hispanic White (73.3%) with no more than high school education (53.4%) and referred by their primary care provider for screening (78.7%). Patients reported that providers almost always discussed benefits of screening (81.3%), but infrequently discussed potential harms (44.0%). On average, patients had low knowledge about screening (score = 3.71 out of 8) and benefits/harms. Decisional conflict was low (score = - 3.12) and weakly related to knowledge (R= - 0.25) or value elicitation (R= - 0.27). Black patients experienced higher decisional conflict than White patients (score = - 2.21 vs - 3.44). Despite knowledge scores being generally low, study patients experienced low decisional conflict regarding their decision to undergo lung cancer screening. Additional work is needed to optimize the quality and consistency of information presented to patients considering screening.
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Affiliation(s)
- Jan M Eberth
- Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene St., Columbia, SC, 29208, USA.
- Rural and Minority Health Research Center, University of South Carolina, Columbia, SC, USA.
| | - Anja Zgodic
- Department of Epidemiology and Biostatistics, University of South Carolina, 915 Greene St., Columbia, SC, 29208, USA
- Rural and Minority Health Research Center, University of South Carolina, Columbia, SC, USA
| | | | | | - David P Miller
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
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17
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Toumazis I, Cao P, de Nijs K, Bastani M, Munshi V, Hemmati M, Ten Haaf K, Jeon J, Tammemägi M, Gazelle GS, Feuer EJ, Kong CY, Meza R, de Koning HJ, Plevritis SK, Han SS. Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis. Ann Intern Med 2023; 176:320-332. [PMID: 36745885 PMCID: PMC11025620 DOI: 10.7326/m22-2216] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND In their 2021 lung cancer screening recommendation update, the U.S. Preventive Services Task Force (USPSTF) evaluated strategies that select people based on their personal lung cancer risk (risk model-based strategies), highlighting the need for further research on the benefits and harms of risk model-based screening. OBJECTIVE To evaluate and compare the cost-effectiveness of risk model-based lung cancer screening strategies versus the USPSTF recommendation and to explore optimal risk thresholds. DESIGN Comparative modeling analysis. DATA SOURCES National Lung Screening Trial; Surveillance, Epidemiology, and End Results program; U.S. Smoking History Generator. TARGET POPULATION 1960 U.S. birth cohort. TIME HORIZON 45 years. PERSPECTIVE U.S. health care sector. INTERVENTION Annual low-dose computed tomography in risk model-based strategies that start screening at age 50 or 55 years, stop screening at age 80 years, with 6-year risk thresholds between 0.5% and 2.2% using the PLCOm2012 model. OUTCOME MEASURES Incremental cost-effectiveness ratio (ICER) and cost-effectiveness efficiency frontier connecting strategies with the highest health benefit at a given cost. RESULTS OF BASE-CASE ANALYSIS Risk model-based screening strategies were more cost-effective than the USPSTF recommendation and exclusively comprised the cost-effectiveness efficiency frontier. Among the strategies on the efficiency frontier, those with a 6-year risk threshold of 1.2% or greater were cost-effective with an ICER less than $100 000 per quality-adjusted life-year (QALY). Specifically, the strategy with a 1.2% risk threshold had an ICER of $94 659 (model range, $72 639 to $156 774), yielding more QALYs for less cost than the USPSTF recommendation, while having a similar level of screening coverage (person ever-screened 21.7% vs. USPSTF's 22.6%). RESULTS OF SENSITIVITY ANALYSES Risk model-based strategies were robustly more cost-effective than the 2021 USPSTF recommendation under varying modeling assumptions. LIMITATION Risk models were restricted to age, sex, and smoking-related risk predictors. CONCLUSION Risk model-based screening is more cost-effective than the USPSTF recommendation, thus warranting further consideration. PRIMARY FUNDING SOURCE National Cancer Institute (NCI).
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Affiliation(s)
- Iakovos Toumazis
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.)
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.)
| | - Koen de Nijs
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Mehrad Bastani
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York (M.B.)
| | - Vidit Munshi
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.)
| | - Mehdi Hemmati
- Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.)
| | - Kevin Ten Haaf
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.)
| | - Martin Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada (M.T.)
| | - G Scott Gazelle
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.)
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland (E.J.F.)
| | - Chung Yin Kong
- Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York (C.Y.K.)
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, and Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada (R.M.)
| | - Harry J de Koning
- Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.)
| | - Sylvia K Plevritis
- Department of Biomedical Data Sciences, Stanford University, Stanford, California (S.K.P.)
| | - Summer S Han
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California (S.S.H.)
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Toward More Effective Lung Cancer Risk Stratification to Empower Screening Programs for the Asian Nonsmoking Population. J Am Coll Radiol 2023; 20:156-161. [PMID: 36646597 DOI: 10.1016/j.jacr.2022.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/14/2022] [Accepted: 10/14/2022] [Indexed: 01/15/2023]
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Richman IB, Long JB, Poghosyan H, Sather P, Gross CP. The role of lung cancer risk and comorbidity in lung cancer screening use. Prev Med Rep 2022; 30:102006. [PMID: 36203942 PMCID: PMC9530957 DOI: 10.1016/j.pmedr.2022.102006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 08/26/2022] [Accepted: 09/24/2022] [Indexed: 11/24/2022] Open
Abstract
Although lung cancer screening (LCS) with low dose computed tomography has been shown to reduce lung cancer mortality, benefits and harms of screening vary among eligible adults. The goal of this study was to evaluate whether LCS is more commonly used among populations most likely to benefit, namely adults with high lung cancer risk and low comorbidity. In this cohort study of patients eligible for LCS, we used data from the electronic health record to evaluate the relationship between lung cancer risk, comorbidity, and receipt of LCS. We also evaluated use of diagnostic chest CT. Analyses used a nonparametric test for trend across quartiles of lung cancer risk and comorbidity. The study sample included 551 LCS-eligible adults who were followed for a mean 2.9 years (SD 1.6 years). A cumulative 190 (34 %) received at least 1 LCS, and 141 (26 %) had a diagnostic chest CT. Receipt of LCS increased across quartiles of lung cancer risk (5 per 100 person years in the lowest quartile vs 13 per 100 person-years in the highest, p < 0.001 for test of trend). LCS receipt decreased across increasing quartiles of comorbidity (14 vs 8 per 100 person-years, p = 0.008). Diagnostic CT was more common in among patient with higher levels of comorbidity (15 vs 5 per 100 person-years, p < 0.001). In conclusion, lung cancer screening was more commonly used in patients with greater lung cancer risk and lower comorbidity. Results suggest that both patient characteristics and use of diagnostic imaging may shape current patterns of LCS use.
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Affiliation(s)
- Ilana B. Richman
- Department of Medicine, Yale School of Medicine, New Haven, CT, United States
- Cancer Outcomes, Public Policy, and Effectiveness Research Center (COPPER), Yale School of Medicine, New Haven, CT, United States
- Corresponding author at: Ilana Richman, 367 Cedar St, Harkness Hall A, Room 301a, New Haven, CT 06510, United States.
| | - Jessica B. Long
- Department of Medicine, Yale School of Medicine, New Haven, CT, United States
- Cancer Outcomes, Public Policy, and Effectiveness Research Center (COPPER), Yale School of Medicine, New Haven, CT, United States
| | | | - Polly Sather
- Yale School of Nursing, New Haven, CT, United States
| | - Cary P. Gross
- Department of Medicine, Yale School of Medicine, New Haven, CT, United States
- Cancer Outcomes, Public Policy, and Effectiveness Research Center (COPPER), Yale School of Medicine, New Haven, CT, United States
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20
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Li M, Zhang L, Charvat H, Callister ME, Sasieni P, Christodoulou E, Kaaks R, Johansson M, Carvalho AL, Vaccarella S, Robbins HA. The influence of postscreening follow-up time and participant characteristics on estimates of overdiagnosis from lung cancer screening trials. Int J Cancer 2022; 151:1491-1501. [PMID: 35809038 PMCID: PMC10157369 DOI: 10.1002/ijc.34167] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/04/2022] [Accepted: 06/03/2022] [Indexed: 11/06/2022]
Abstract
We aimed to explore the underlying reasons that estimates of overdiagnosis vary across and within low-dose computed tomography (LDCT) lung cancer screening trials. We conducted a systematic review to identify estimates of overdiagnosis from randomised controlled trials of LDCT screening. We then analysed the association of Ps (the excess incidence of lung cancer as a proportion of screen-detected cases) with postscreening follow-up time using a linear random effects meta-regression model. Separately, we analysed annual Ps estimates from the US National Lung Screening Trial (NLST) and German Lung Cancer Screening Intervention Trial (LUSI) using exponential decay models with asymptotes. We conducted stratified analyses to investigate participant characteristics associated with Ps using the extended follow-up data from NLST. Among 12 overdiagnosis estimates from 8 trials, the postscreening follow-up ranged from 3.8 to 9.3 years, and Ps ranged from -27.0% (ITALUNG, 8.3 years follow-up) to 67.2% (DLCST, 5.0 years follow-up). Across trials, 39.1% of the variation in Ps was explained by postscreening follow-up time. The annual changes in Ps were -3.5% and -3.9% in the NLST and LUSI trials, respectively. Ps was predicted to plateau at 2.2% for NLST and 9.2% for LUSI with hypothetical infinite follow-up. In NLST, Ps increased with age from -14.9% (55-59 years) to 21.7% (70-74 years), and time trends in Ps varied by histological type. The findings suggest that differences in postscreening follow-up time partially explain variation in overdiagnosis estimates across lung cancer screening trials. Estimates of overdiagnosis should be interpreted in the context of postscreening follow-up and population characteristics.
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Affiliation(s)
- Mengmeng Li
- Department of Cancer Prevention, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Li Zhang
- International Agency for Research on Cancer, Lyon, France
| | - Hadrien Charvat
- International Agency for Research on Cancer, Lyon, France
- Faculty of International Liberal Arts, Juntendo University, Tokyo, Japan
- Division of International Health Policy Research, Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | | | | | - Evangelia Christodoulou
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany
- Translational Lung Research Center (TLRC) Heidelberg, Member of the German Center for Lung Research (DZL), Heidelberg, Germany
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21
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Cao P, Jeon J, Meza R. Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study. J Med Screen 2022; 29:260-267. [PMID: 35989646 PMCID: PMC9574899 DOI: 10.1177/09691413221118194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Although lung cancer screening (LCS) has been proven effective in reducing lung cancer mortality, it is associated with some potential harms, such as false positives and invasive follow-up procedures. Determining the time to next screen based on individual risk could reduce harms while maintaining health gains. Here, we evaluate the benefits and harms of LCS strategies with adaptive schedules, and compare these with those from non-adaptive strategies. METHODS We extended the Lee and Zelen risk threshold method to select screening schedules based on individual's lung cancer risk and life expectancy (adaptive schedules). We compared the health benefits and harms of these adaptive schedules with regular (non-adaptive) schedules (annual, biennial and triennial) using a validated lung cancer microsimulation model. Outcomes include lung cancer deaths (LCD) averted, life years gained (LYG), discounted quality adjusted life years (QALYs) gained, and false positives per LCD averted. We also explored the impact of varying screening-related disutilities. RESULTS In comparison to standard regular screening recommendations, risk-dependent adaptive screening reduced screening harms while maintaining a similar level of health benefits. The net gains and the balance of benefits and harms from LCS with efficient adaptive schedules were improved compared to those from regular screening, especially when the screening-related disutilities are high. CONCLUSIONS Adaptive screening schedules can reduce the associated harms of screening while maintaining its associated lung cancer mortality reductions and years of life gained. Our study identifies individually tailored schedules that optimize the screening benefit/harm trade-offs.
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Affiliation(s)
- Pianpian Cao
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
| | - Jihyoun Jeon
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Department of Epidemiology, 1259University of Michigan, Ann Arbor, MI, USA
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22
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Núñez ER, Caverly TJ, Zhang S, Glickman ME, Qian SX, Boudreau JH, Miller DR, Wiener RS. Invasive Procedures and Associated Complications After Initial Lung Cancer Screening in a National Cohort of Veterans. Chest 2022; 162:475-484. [PMID: 35231480 PMCID: PMC9424329 DOI: 10.1016/j.chest.2022.02.031] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 01/21/2022] [Accepted: 02/13/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Little is known about rates of invasive procedures and associated complications after lung cancer screening (LCS) in nontrial settings. RESEARCH QUESTION What are the frequency of invasive procedures, complication rates, and factors associated with complications in a national sample of veterans screened for lung cancer? STUDY DESIGN AND METHODS We conducted a retrospective cohort analysis of veterans who underwent LCS in any Veterans Health Administration (VA) facility between 2013 and 2019 and identified veterans who underwent invasive procedures within 10 months of initial LCS. The primary outcome was presence of a complication within 10 days after an invasive procedure. We conducted hierarchical mixed-effects logistic regression analyses to determine patient- and facility-level factors associated with complications resulting from an invasive procedure. RESULTS Our cohort of 82,641 veterans who underwent LCS was older, more racially diverse, and had more comorbidities than National Lung Screening Trial (NLST) participants. Overall, 1,741 veterans (2.1%) underwent an invasive procedure after initial screening, including 856 (42.3%) bronchoscopies, 490 (24.2%) transthoracic needle biopsies, and 423 (20.9%) thoracic surgeries. Among veterans who underwent procedures, 151 (8.7%) experienced a major complication (eg, respiratory failure, prolonged hospitalization) and an additional 203 (11.7%) experienced an intermediate complication (eg, pneumothorax, pleural effusion). Veterans who underwent thoracic surgery (OR, 7.70; 95% CI, 5.48-10.81), underwent multiple nonsurgical procedures (OR, 1.49; 95% CI, 1.15-1.92), or carried a dementia diagnosis (OR, 3.91; 95% CI, 1.79-8.52) were more likely to experience complications. Invasive procedures were performed less often than in the NLST (2.1% vs 4.2%), but veterans were more likely to experience complications after each type of procedure. INTERPRETATION These findings may reflect a higher threshold to perform procedures in veteran populations with multiple comorbidities and higher risks of complications. Future work should focus on optimizing the identification of patients whose chance of benefit likely outweighs the complication risks.
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23
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Bade B, Gwin M, Triplette M, Wiener RS, Crothers K. Comorbidity and life expectancy in shared decision making for lung cancer screening. Semin Oncol 2022; 49:S0093-7754(22)00057-4. [PMID: 35940959 DOI: 10.1053/j.seminoncol.2022.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/02/2022] [Accepted: 07/03/2022] [Indexed: 11/11/2022]
Abstract
Shared decision making (SDM) is an important part of lung cancer screening (LCS) that includes discussing the risks and benefits of screening, potential outcomes, patient eligibility and willingness to participate, tobacco cessation, and tailoring a strategy to an individual patient. More than other cancer screening tests, eligibility for LCS is nuanced, incorporating the patient's age as well as tobacco use history and overall health status. Since comorbidities and multimorbidity (ie, 2 or more comorbidities) impact the risks and benefits of LCS, these topics are a fundamental part of decision-making. However, there is currently little evidence available to guide clinicians in addressing comorbidities and an individual's "appropriateness" for LCS during SDM visits. Therefore, this literature review investigates the impact of comorbidities and multimorbidity among patients undergoing LCS. Based on available evidence and guideline recommendations, we identify comorbidities that should be considered during SDM conversations and review best practices for navigating SDM conversations in the context of LCS. Three conditions are highlighted since they concomitantly portend higher risk of developing lung cancer, potentially increase risk of screening-related evaluation and treatment complications and can be associated with limited life expectancy: chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and human immunodeficiency virus infection.
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Affiliation(s)
- Brett Bade
- Veterans Affairs (VA) Connecticut Healthcare System, Section of Pulmonary, Critical Care, and Sleep Medicine, West Haven, CT, United States of America (USA); Yale University School of Medicine, Section of Pulmonary, Critical Care, and Sleep Medicine, New Haven, CT, USA.
| | - Mary Gwin
- University of Washington School of Medicine, Seattle, WA, USA
| | - Matthew Triplette
- University of Washington School of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA, USA; Fred Hutchinson Cancer Center, Clinical Research Division, Seattle, WA, USA
| | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research and Medical Service, VA Boston Healthcare System, Boston, MA, USA; The Pulmonary Center, Boston University School of Medicine, Boston, MA, USA
| | - Kristina Crothers
- University of Washington School of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA, USA; VA Puget Sound Health Care System, Section of Pulmonary, Critical Care and Sleep Medicine, Seattle, WA, USA
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24
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Cao P, Smith L, Mandelblatt JS, Jeon J, Taylor KL, Zhao A, Levy DT, Williams RM, Meza R, Jayasekera J. Cost-Effectiveness of a Telephone-Based Smoking Cessation Randomized Trial in the Lung Cancer Screening Setting. JNCI Cancer Spectr 2022; 6:pkac048. [PMID: 35818125 PMCID: PMC9382714 DOI: 10.1093/jncics/pkac048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 06/17/2022] [Accepted: 06/22/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND There are limited data on the cost-effectiveness of smoking cessation interventions in lung cancer screening settings. We conducted an economic analysis embedded in a national randomized trial of 2 telephone counseling cessation interventions. METHODS We used a societal perspective to compare the short-term cost per 6-month bio-verified quit and long-term cost-effectiveness of the interventions. Trial data were used to micro-cost intervention delivery, and the data were extended to a lifetime horizon using an established Cancer Intervention Surveillance and Modeling Network lung cancer model. We modeled the impact of screening accompanied by 8 weeks vs 3 weeks of telephone counseling (plus nicotine replacement) vs screening alone based on 2021 screening eligibility. Lifetime downstream costs (2021 dollars) and effects (life-years gained, quality-adjusted life-years [QALYs]) saved were discounted at 3%. Sensitivity analyses tested the effects of varying quit rates and costs; all analyses assumed nonrelapse after quitting. RESULTS The costs for delivery of the 8-week vs 3-week protocol were $380.23 vs $144.93 per person, and quit rates were 7.14% vs 5.96%, respectively. The least costly strategy was a 3-week counseling approach. An 8-week (vs 3-week) counseling approach increased costs but gained QALYs for an incremental cost-effectiveness ratio of $4029 per QALY. Screening alone cost more and saved fewer QALYs than either counseling strategy. Conclusions were robust in sensitivity analyses. CONCLUSIONS Telephone-based cessation interventions with nicotine replacement are considered cost-effective in the lung screening setting. Integrating smoking cessation interventions with lung screening programs has the potential to maximize long-term health benefits at reasonable costs.
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Affiliation(s)
- Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Laney Smith
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Kathryn L Taylor
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Amy Zhao
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - David T Levy
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Randi M Williams
- Department of Oncology, Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Jinani Jayasekera
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
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Meng Q, Li B, Gao P, Liu W, Zhou P, Ding J, Zhang J, Ge H. Development and Validation of a Risk Stratification Model of Pulmonary Ground-Glass Nodules Based on Complementary Lung-RADS 1.1 and Deep Learning Scores. Front Public Health 2022; 10:891306. [PMID: 35677762 PMCID: PMC9168898 DOI: 10.3389/fpubh.2022.891306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/29/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To assess the value of novel deep learning (DL) scores combined with complementary lung imaging reporting and data system 1.1 (cLung-RADS 1.1) in managing the risk stratification of ground-glass nodules (GGNs) and therefore improving the efficiency of lung cancer (LC) screening in China. Materials and Methods Overall, 506 patients with 561 GGNs on routine computed tomography images, obtained between January 2017 and March 2021, were enrolled in this single-center, retrospective Chinese study. Moreover, the cLung-RADS 1.1 was previously validated, and the DL algorithms were based on a multi-stage, three-dimensional DL-based convolutional neural network. Therefore, the DL-based cLung-RADS 1.1 model was created using a combination of the risk scores of DL and category of cLung-RADS 1.1. The recall rate, precision, accuracy, per-class F1 score, weighted average F1 score (F1weighted), Matthews correlation coefficient (MCC), and area under the curve (AUC) were used to evaluate the performance of DL-based cLung-RADS 1.1. Results The percentage of neoplastic lesions appeared as GGNs in our study was 95.72% (537/561) after long-period follow-up.Compared to cLung-RADS 1.1 model or DL model, The DL-based cLung-RADS 1.1 model achieved the excellent performance with F1 scores of 95.96% and 95.58%, F1weighted values of 97.49 and 96.62%, accuracies of 92.38 and 91.77%, and MCCs of 32.43 and 37.15% in the training and validation tests, respectively. The combined model achieved the best AUCs of 0.753 (0.526–0.980) and 0.734 (0.585–0.884) for the training and validation tests, respectively. Conclusion The DL-based cLung-RADS 1.1 model shows the best performance in risk stratification management of GGNs, which demonstrates substantial promise for developing a more effective personalized lung neoplasm management paradigm for LC screening in China.
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Affiliation(s)
- Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Bing Li
- Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Pengrui Gao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Wentao Liu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Peijin Zhou
- Department of Radiology, The People's Hospital of Nanzhao Country, Nanyang, China
| | - Jia Ding
- Yizhun Medical AI Co. Ltd, Beijing, China
| | | | - Hong Ge
- Department of Radiotherapy, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
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Risk perception and disease knowledge in attendees of a community-based lung cancer screening programme. Lung Cancer 2022; 168:1-9. [DOI: 10.1016/j.lungcan.2022.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 02/06/2022] [Accepted: 04/04/2022] [Indexed: 12/24/2022]
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Lu B, Wang L, Lu M, Zhang Y, Cai J, Luo C, Chen H, Dai M. Microsimulation Model for Prevention and Intervention of Coloretal Cancer in China (MIMIC-CRC): Development, Calibration, Validation, and Application. Front Oncol 2022; 12:883401. [PMID: 35530306 PMCID: PMC9072786 DOI: 10.3389/fonc.2022.883401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/24/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction A microsimulation model provides important references for decision-making regarding colorectal cancer (CRC) prevention strategies, yet such a well-validated model is scarce in China. Methods We comprehensively introduce the development of MIcrosimulation Model for the prevention and Intervention of Colorectal Cancer in China (MIMIC-CRC). The MIMIC-CRC was first constructed to simulate the natural history of CRC based on the adenoma-carcinoma pathway. The parameters were calibrated and validated using data from population-based cancer registry data and CRC screening programs. Furthermore, to assess the model’s external validity, we compared the model-derived results to outcome patterns of a sigmoidoscopy screening trial in the UK [UK Flexible Sigmoidoscopy Screening (UKFSS) trial]. Finally, we evaluated the application potential of the MIMIC-CRC model in CRC screening by comparing the 8 different strategies. Results We found that most of the model-predicted colorectal lesion prevalence was within the 95% CIs of observed prevalence in a large population-based CRC screening program in China. In addition, model-predicted sex- and age-specific CRC incidence and mortality were equivalent to the registry-based data. The hazard ratios of model-estimated CRC-related incidence and mortality for sigmoidoscopy screening compared to no screening were 0.60 and 0.51, respectively, which were comparable to the reported results of the UKFSS trial. Moreover, we found that all 8 strategies could reduce CRC incidence and mortality compared to no screening. Conclusions The well-calibrated and validated MIMIC-CRC model may represent a valid tool to assess the comparative effectiveness of CRC screening strategies and will be useful for further decision-making to CRC prevention.
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Affiliation(s)
- Bin Lu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Wang
- Department of Cancer Prevention, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Cancer and Basic Medicine, Chinese Academy of Sciences, Hangzhou, China
| | - Ming Lu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuhan Zhang
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Cai
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenyu Luo
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongda Chen
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Min Dai, ; Hongda Chen,
| | - Min Dai
- Medical Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Min Dai, ; Hongda Chen,
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Lowery J, Fagerlin A, Larkin AR, Wiener RS, Skurla SE, Caverly TJ. Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation. JMIR Hum Factors 2022; 9:e32399. [PMID: 35363144 PMCID: PMC9015752 DOI: 10.2196/32399] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/28/2021] [Accepted: 11/28/2021] [Indexed: 12/18/2022] Open
Abstract
Background Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient’s individualized lung cancer risk and estimated net benefit. Objective The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD). Methods Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits. Results Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI −5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits. Conclusions The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients. Trial Registration ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412
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Affiliation(s)
- Julie Lowery
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, UT, United States
- Informatics Decision-Enhancement and Analytics Sciences Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, MI, United States
| | - Angela R Larkin
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Renda S Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA, United States
- The Pulmonary Center, Boston University School of Medicine, Boston, MA, United States
| | - Sarah E Skurla
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
| | - Tanner J Caverly
- Center for Clinical Management Research, Ann Arbor VA Healthcare System, Ann Arbor, MI, United States
- Department of Learning Health Sciences, University of Michigan School of Medicine, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
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Kukhareva PV, Caverly TJ, Li H, Katki HA, Cheung LC, Reese TJ, Del Fiol G, Hess R, Wetter DW, Zhang Y, Taft TY, Flynn MC, Kawamoto K. OUP accepted manuscript. J Am Med Inform Assoc 2022; 29:779-788. [PMID: 35167675 PMCID: PMC9006678 DOI: 10.1093/jamia/ocac020] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 11/14/2022] Open
Abstract
Objective Materials and Methods Results Discussion Conclusion
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Affiliation(s)
- Polina V Kukhareva
- Corresponding Author: Polina V. Kukhareva, PhD, MPH, Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Suite 108, Salt Lake City, UT 84108, USA;
| | - Tanner J Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, Michigan, USA
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Haojia Li
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Li C Cheung
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
- Department of Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| | - David W Wetter
- Department of Population Health Sciences and Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA
| | - Yue Zhang
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, USA
| | - Teresa Y Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Michael C Flynn
- Department of Pediatrics, University of Utah, Salt Lake City, Utah, USA
- Community Physicians Group, University of Utah Health, Salt Lake City, Utah, USA
- Community Physicians Group, University of Utah, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
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Impact of Joint Lung Cancer Screening and Cessation Interventions Under the New Recommendations of the U.S. Preventive Services Task Force. J Thorac Oncol 2022; 17:160-166. [PMID: 34648947 PMCID: PMC8692396 DOI: 10.1016/j.jtho.2021.09.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/15/2021] [Accepted: 09/20/2021] [Indexed: 01/03/2023]
Abstract
INTRODUCTION In 2021, the U.S. Preventive Services Task Force (USPSTF) revised its lung cancer screening recommendations expanding its eligibility. As more smokers become eligible, cessation interventions at the point of screening could enhance the benefits. Here, we evaluate the effects of joint screening and cessation interventions under the new recommendations. METHODS A validated lung cancer natural history model was used to estimate lifetime number of low-dose computed tomography screens, percentage ever screened, lung cancer deaths, lung cancer deaths averted, and life-years gained for the 1960 U.S. birth cohort aged 45 to 90 years (4.5 million individuals). Screening occurred according to the USPSTF 2013 and 2021 recommendations with varying uptake (0%, 30%, 100%), with or without a cessation intervention at the point of screening with varying effectiveness (15%, 100%). RESULTS Screening 30% of the eligible population according to the 2021 criteria with no cessation intervention (USPSTF 2021, 30% uptake, without cessation intervention) was estimated to result in 6845 lung cancer deaths averted and 103,725 life-years gained. These represent 28% and 34% increases, respectively, relative to screening according to the 2013 guidelines (USPSTF 2013, 30% uptake, without cessation intervention). Adding a cessation intervention at the time of the first screen with 15% effectiveness (USPSTF 2021, 30% uptake, with cessation intervention with 15% effectiveness) was estimated to result in 2422 additional lung cancer deaths averted (9267 total, ∼73% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 322,785 life-years gained (∼318% increase). Screening 100% of the eligible according to the 2021 guidelines with no cessation intervention (USPSTF 2021, 100% uptake, without cessation intervention) was estimated to result in 23,444 lung cancer deaths averted (∼337% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 354,330 life-years gained (∼359% increase). Adding a cessation intervention with 15% effectiveness (USPSTF 2021, 100% uptake, with cessation intervention with 15% effectiveness) would result in 31,998 lung cancer deaths averted (∼497% increase versus USPSTF 2013, 30% uptake, without cessation intervention) and 1,086,840 life-years gained (∼1309% increase). CONCLUSIONS Joint screening and cessation interventions would result in considerable lung cancer deaths averted and life-years gained. Adding a one-time cessation intervention of modest effectiveness (15%) results in comparable life-years gained as increasing screening uptake from 30% to 100% because while cessation decreases mortality from many causes, screening only reduces lung cancer mortality. This simulation indicates that incorporating cessation programs into screening practice should be a priority as it can maximize overall benefits.
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Landy R, Young CD, Skarzynski M, Cheung LC, Berg CD, Rivera MP, Robbins HA, Chaturvedi AK, Katki HA. Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines. J Natl Cancer Inst 2021; 113:1590-1594. [PMID: 33399825 PMCID: PMC8562965 DOI: 10.1093/jnci/djaa211] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 10/20/2020] [Accepted: 10/29/2020] [Indexed: 01/04/2023] Open
Abstract
We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.
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Affiliation(s)
- Rebecca Landy
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Corey D Young
- Department of Microbiology, Biochemistry and Immunology, Morehouse School of Medicine, Atlanta, GA, USA
| | - Martin Skarzynski
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Christine D Berg
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - M Patricia Rivera
- Division of Pulmonary and Critical Care Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Anil K Chaturvedi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
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Mazzone PJ, Silvestri GA, Souter LH, Caverly TJ, Kanne JP, Katki HA, Wiener RS, Detterbeck FC. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest 2021; 160:e427-e494. [PMID: 34270968 PMCID: PMC8727886 DOI: 10.1016/j.chest.2021.06.063] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/11/2021] [Accepted: 06/16/2021] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part because of the results of the National Lung Screening Trial (NLST). Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, and increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not. METHODS Approved panelists reviewed previously developed key questions using the Population, Intervention, Comparator, Outcome format to address the benefit and harms of low-dose CT screening, and key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Meta-analyses were performed when enough evidence was available. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and ungraded statements were drafted, voted on, and revised until consensus was reached. RESULTS The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in seven graded recommendations and nine ungraded consensus statements. CONCLUSIONS Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen-detected findings, and the effectiveness of smoking cessation interventions can impact this balance.
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Affiliation(s)
| | | | | | - Tanner J Caverly
- Ann Arbor VA Center for Clinical Management Research, Ann Arbor, MI; University of Michigan Medical School, Ann Arbor, MI
| | - Jeffrey P Kanne
- University of Wisconsin School of Medicine and Public Health, Madison, WI
| | | | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System, Boston, MA; Boston University School of Medicine, Boston, MA
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Schreuder A, Jacobs C, Lessmann N, Broeders MJ, Silva M, Išgum I, de Jong PA, van den Heuvel MM, Sverzellati N, Prokop M, Pastorino U, Schaefer-Prokop CM, van Ginneken B. Scan-based competing death risk model for reevaluating lung cancer computed tomography screening eligibility. Eur Respir J 2021; 59:13993003.01613-2021. [PMID: 34649976 DOI: 10.1183/13993003.01613-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 09/17/2021] [Indexed: 11/05/2022]
Abstract
PURPOSE A baseline CT scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC and a high CD risk. METHODS Participant demographics and quantitative CT measures of LC, cardiovascular disease, and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting five-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data was used to perform external validation (n=2287). RESULTS Our final CD model outperformed an external pre-scan model (CDRAT) in both the derivation (Area under the curve=0.744 [95% confidence interval=0.727 to 0.761] and 0.677 [0.658 to 0.695], respectively) and validation cohorts (0.744 [0.652 to 0.835] and 0.725 [0.633 to 0.816], respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096, 27%) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287, 34%) were 129 (versus 29) and 1.67 (versus 0.43). CONCLUSIONS Evaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.
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Affiliation(s)
- Anton Schreuder
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Colin Jacobs
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nikolas Lessmann
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mireille Jm Broeders
- Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, the Netherlands.,Dutch Expert Centre for Screening, Nijmegen, the Netherlands
| | - Mario Silva
- Unit of Thoracic Surgery, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.,Section of Radiology, Unit of Surgical Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, Amsterdam.,Department of Radiology and Nuclear Medicine, Amsterdam UMC - location AMC, Amsterdam
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, the Netherlands.,Utrecht University, the Netherlands
| | - Michel M van den Heuvel
- Department of Respiratory Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nicola Sverzellati
- Department of Radiology and Nuclear Medicine, Amsterdam UMC - location AMC, Amsterdam
| | - Mathias Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ugo Pastorino
- Department of Biomedical Engineering and Physics, Amsterdam UMC - location AMC, Amsterdam
| | - Cornelia M Schaefer-Prokop
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.,Department of Radiology, Meander Medisch Centrum, Amersfoort, the Netherlands
| | - Bram van Ginneken
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.,Fraunhofer MEVIS, Bremen, Germany
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Toumazis I, Alagoz O, Leung A, Plevritis SK. A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings. Cancer 2021; 127:4432-4446. [PMID: 34383299 DOI: 10.1002/cncr.33835] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 02/12/2021] [Accepted: 03/18/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time. METHODS ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms. RESULTS Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%. CONCLUSIONS ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening. LAY SUMMARY A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.
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Affiliation(s)
- Iakovos Toumazis
- Department of Biomedical Data Science, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Ann Leung
- Department of Radiology, Stanford University, Stanford, California
| | - Sylvia K Plevritis
- Department of Biomedical Data Science, Stanford University, Stanford, California.,Department of Radiology, Stanford University, Stanford, California
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Cadham CJ, Cao P, Jayasekera J, Taylor KL, Levy DT, Jeon J, Elkin EB, Foley KL, Joseph A, Kong CY, Minnix JA, Rigotti NA, Toll BA, Zeliadt SB, Meza R, Mandelblatt J. Cost-Effectiveness of Smoking Cessation Interventions in the Lung Cancer Screening Setting: A Simulation Study. J Natl Cancer Inst 2021; 113:1065-1073. [PMID: 33484569 PMCID: PMC8502465 DOI: 10.1093/jnci/djab002] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/02/2020] [Accepted: 01/04/2021] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Guidelines recommend offering cessation interventions to smokers eligible for lung cancer screening, but there is little data comparing specific cessation approaches in this setting. We compared the benefits and costs of different smoking cessation interventions to help screening programs select specific cessation approaches. METHODS We conducted a societal-perspective cost-effectiveness analysis using a Cancer Intervention and Surveillance Modeling Network model simulating individuals born in 1960 over their lifetimes. Model inputs were derived from Medicare, national cancer registries, published studies, and micro-costing of cessation interventions. We modeled annual lung cancer screening following 2014 US Preventive Services Task Force guidelines plus cessation interventions offered to current smokers at first screen, including pharmacotherapy only or pharmacotherapy with electronic and/or web-based, telephone, individual, or group counseling. Outcomes included lung cancer cases and deaths, life-years saved, quality-adjusted life-years (QALYs) saved, costs, and incremental cost-effectiveness ratios. RESULTS Compared with screening alone, all cessation interventions decreased cases of and deaths from lung cancer. Compared incrementally, efficient cessation strategies included pharmacotherapy with either web-based cessation ($555 per QALY), telephone counseling ($7562 per QALY), or individual counseling ($35 531 per QALY). Cessation interventions continued to have costs per QALY well below accepted willingness to pay thresholds even with the lowest intervention effects and was more cost-effective in cohorts with higher smoking prevalence. CONCLUSION All smoking cessation interventions delivered with lung cancer screening are likely to provide benefits at reasonable costs. Because the differences between approaches were small, the choice of intervention should be guided by practical concerns such as staff training and availability.
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Affiliation(s)
- Christopher J Cadham
- Department of Oncology, Georgetown University School of Medicine, Washington, DC, USA
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jinani Jayasekera
- Department of Oncology, Georgetown University School of Medicine, Washington, DC, USA
| | - Kathryn L Taylor
- Department of Oncology, Georgetown University School of Medicine, Washington, DC, USA
| | - David T Levy
- Department of Oncology, Georgetown University School of Medicine, Washington, DC, USA
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Elena B Elkin
- Department of Health Policy and Management at Columbia University Mailman School of Public Health, New York, NY, USA
| | - Kristie L Foley
- Department of Implementation Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Anne Joseph
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Chung Yin Kong
- Division of General Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer A Minnix
- Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Nancy A Rigotti
- Department of Medicine and Mongan Institute, Tobacco Research and Treatment Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Benjamin A Toll
- Department of Public Health Sciences and Psychiatry, Medical University of South Carolina, Charleston, SC, USA
| | - Steven B Zeliadt
- Department of Health Services, School of Public Health, University of Washington, Seattle, WA, USA
- Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System, Seattle, WA, USA
| | - Rafael Meza
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jeanne Mandelblatt
- Department of Oncology, Georgetown University School of Medicine, Washington, DC, USA
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36
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Mazzone PJ, Silvestri GA, Souter LH, Caverly TJ, Kanne JP, Katki HA, Wiener RS, Detterbeck FC. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report - Executive Summary. Chest 2021; 160:1959-1980. [PMID: 34270965 DOI: 10.1016/j.chest.2021.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Low-dose chest CT screening for lung cancer has become a standard of care in the United States, in large part due to the results of the National Lung Screening Trial. Additional evidence supporting the net benefit of low-dose chest CT screening for lung cancer, as well as increased experience in minimizing the potential harms, has accumulated since the prior iteration of these guidelines. Here, we update the evidence base for the benefit, harms, and implementation of low-dose chest CT screening. We use the updated evidence base to provide recommendations where the evidence allows, and statements based on experience and expert consensus where it does not. METHODS Approved panelists reviewed previously developed key questions using the PICO (population, intervention, comparator, and outcome) format to address the benefit and harms of low-dose CT screening, as well as key areas of program implementation. A systematic literature review was conducted using MEDLINE via PubMed, Embase, and the Cochrane Library on a quarterly basis since the time of the previous guideline publication. Reference lists from relevant retrievals were searched, and additional papers were added. Retrieved references were reviewed for relevance by two panel members. The quality of the evidence was assessed for each critical or important outcome of interest using the GRADE approach. Meta-analyses were performed where appropriate. Important clinical questions were addressed based on the evidence developed from the systematic literature review. Graded recommendations and un-graded statements were drafted, voted on, and revised until consensus was reached. RESULTS The systematic literature review identified 75 additional studies that informed the response to the 12 key questions that were developed. Additional clinical questions were addressed resulting in 7 graded recommendations and 9 ungraded consensus statements. CONCLUSIONS Evidence suggests that low-dose CT screening for lung cancer can result in a favorable balance of benefit and harms. The selection of screen-eligible individuals, the quality of imaging and image interpretation, the management of screen detected findings, and the effectiveness of smoking cessation interventions, can impact this balance.
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Affiliation(s)
| | | | | | - Tanner J Caverly
- Ann Arbor VA Center for Clinical Management Research and University of Michigan Medical School , Madison, WI
| | - Jeffrey P Kanne
- University of Wisconsin School of Medicine and Public Health, Madison, WI
| | | | - Renda Soylemez Wiener
- Center for Healthcare Organization & Implementation Research, VA Boston Healthcare System and Boston University School of Medicine, Boston, MA
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Caverly TJ, Skurla SE, Robinson CH, Zikmund-Fisher BJ, Hayward RA. The Need for Brevity During Shared Decision Making (SDM) for Cancer Screening: Veterans' Perspectives on an "Everyday SDM" Compromise. MDM Policy Pract 2021; 6:23814683211055120. [PMID: 34722882 PMCID: PMC8554567 DOI: 10.1177/23814683211055120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 09/09/2021] [Indexed: 11/16/2022] Open
Abstract
Introduction. Detailed or "full" shared decision making (SDM) about cancer screening is difficult in the primary care setting. Time spent discussing cancer screening is time not spent on other important issues. Given time constraints, brief SDM that is incomplete but addresses key elements may be feasible and acceptable. However, little is known about how patients feel about abbreviated SDM. This study assessed patient perspectives on a compromise solution ("everyday SDM"): 1) primary care provided makes a tailored recommendation, 2) briefly presents qualitative information on key tradeoffs, and 3) conveys full support for decisional autonomy and desires for more information. Methods. We recruited a stratified random sample of Veterans from an academic Veterans Affairs medical center who were eligible for lung cancer screening, oversampling women and minority patients, to attend a 6-hour deliberative focus group. Experts informed participants about cancer screening, factors that influence screening benefits, and the role of patient preferences. Then, facilitator-led small groups elicited patient questions and informed opinions about the everyday SDM proposal, its acceptability, and their recommendations for improvement. Results. Thirty-six Veterans with a heavy smoking history participated (50% male, 83% white). There was a strong consensus that everyday SDM was acceptable if patients were the final deciders and could get more information on request. Participants broadly recommended that clinicians only mention downsides directly related to screening and avoid discussion of potential downstream harms (such as biopsies). Discussion. Although further testing in more diverse populations and different conditions is needed, these patients found the everyday SDM approach to be acceptable for routine lung cancer screening discussions, despite its use of an explicit recommendation and presentation of only qualitative information.
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Affiliation(s)
- Tanner J. Caverly
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
- Institute for Health Policy Innovation, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Department of Learning Health Sciences, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Sarah E. Skurla
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | - Claire H. Robinson
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
| | - Brian J. Zikmund-Fisher
- Department of Health Behavior and Health Education, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Rodney A. Hayward
- Center for Clinical Management Research, Department of Veterans Affairs, Ann Arbor, MI, USA
- Institute for Health Policy Innovation, University of Michigan School of Medicine, Ann Arbor, MI, USA
- Department of Internal Medicine TJC, University of Michigan School of Medicine, Ann Arbor, MI, USA
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Leishman NJ, Wiener RS, Fagerlin A, Hayward RA, Lowery J, Caverly TJ. Variation in Eligible Patients' Agreeing to and Receiving Lung Cancer Screening: A Cohort Study. Am J Prev Med 2021; 60:520-528. [PMID: 33342671 PMCID: PMC10440012 DOI: 10.1016/j.amepre.2020.10.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 10/07/2020] [Accepted: 10/12/2020] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Little is known about how clinicians make low-dose computed tomography lung cancer screening decisions in practice. Investigators assessed the factors associated with real-world decision making, hypothesizing that lung cancer risk and comorbidity would not be associated with agreeing to or receiving screening. Though these factors are key determinants of the benefit of lung cancer screening, they are often difficult to incorporate into decisions without the aid of decision tools. METHODS This was a retrospective cohort study of patients meeting current national eligibility criteria and deemed appropriate candidates for lung cancer screening on the basis of clinical reminders completed over a 2-year period (2013-2015) at 8 Department of Veterans Affairs medical facilities. Multilevel mixed-effects logistic regression models (conducted in 2019-2020) assessed predictors (age, sex, lung cancer risk, Charlson Comorbidity Index, travel distance to facility, and central versus outlying decision-making location) of primary outcomes of agreeing to and receiving lung cancer screening. RESULTS Of 5,551 patients (mean age=67 years, 97% male, mean lung cancer risk=0.7%, mean Charlson Comorbidity Index=1.14, median travel distance=24.2 miles), 3,720 (67%) agreed to lung cancer screening and 2,398 (43%) received screening. Lung cancer risk and comorbidity score were not strong predictors of agreeing to or receiving screening. Empirical Bayes adjusted rates of agreeing to and receiving screening ranged from 22% to 84% across facilities and from 19% to 85% across clinicians. A total of 33.7% of the variance in agreeing to and 34.2% of the variance in receiving screening was associated with the facility or the clinician offering screening. CONCLUSIONS Substantial variation was found in Veterans agreeing to and receiving lung cancer screening during the Veterans Affairs Lung Cancer Screening Demonstration Project. This variation was not explained by differences in key determinants of patient benefit, whereas the facility and clinician advising the patient had a large impact on lung cancer screening decisions.
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Affiliation(s)
- N Joseph Leishman
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; University of Michigan School of Public Health, Ann Arbor, Michigan
| | - Renda S Wiener
- The Pulmonary Center, Boston University School of Medicine, Boston, Massachusetts; Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts
| | - Angela Fagerlin
- Department of Population Health Sciences, University of Utah School of Medicine, Salt Lake City, Utah; Informatics Decision-Enhancement and Analytic Sciences (IDEAS) Center for Innovation, VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Rodney A Hayward
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; Departments of Learning Health Sciences and Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan
| | - Julie Lowery
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Tanner J Caverly
- Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan; Departments of Learning Health Sciences and Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan.
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Gillmann C, Pajor G, Ramadori P, Albers P, Mons U, Steindorf K, Kentner S, Baumann M. Solving problems is smart, preventing them is wise: Lessons learned from the 2nd International DKFZ Conference on Cancer Prevention. Int J Cancer 2021; 148:3086-3096. [PMID: 33544875 DOI: 10.1002/ijc.33502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 01/22/2021] [Indexed: 11/10/2022]
Abstract
The 2nd International DKFZ Conference on Cancer Prevention (CCP2020) organized by the German Cancer Research Center (DKFZ) was held as a virtual event on 17-18 September 2020. The event gathered experts on cancer prevention from around the world with the aim of generating a stimulating interchange of opinions between clinicians and basic researchers working in the field. The talks and posters of the conference fueled exciting discussions and debates about the state of the art of cancer prevention and provided a comprehensive outlook on the many aspects of the field. The program was divided into three main sessions, illustrating the most recent methodological approaches and interventions in primary, secondary and tertiary prevention, enriched by introductory lectures depicting the most relevant aspects of each session. The key concepts covered in this meeting were risk factors, early detection, improving life after cancer, cancer prevention in Europe and personalized prevention. The importance of the latter was expressly highlighted, many presentations emphasizing that in the era of personalized medicine, prevention also needs to be based on the unique genetic, epigenetic, social and behavioral characteristics of the individual to achieve maximal efficacy. In this article, we summarize the key messages emerging from each section, with particular attention on the most important challenges yet to be met in the field of cancer prevention.
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Affiliation(s)
- Clarissa Gillmann
- Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology (NCRO), Heidelberg, Germany
| | - Gabor Pajor
- Department of Molecular Hematology/Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pierluigi Ramadori
- Division of Chronic inflammation and Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Albers
- Department of Urology, Düsseldorf University Hospital, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany.,Division of Personalized Prevention of Prostate Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ute Mons
- Cancer Prevention Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany.,Heart Center, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Karen Steindorf
- Division of Physical Activity, Prevention and Cancer, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Susan Kentner
- German Cancer Research Center (DKFZ), Heidelberg, Germany
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Ten Haaf K, Bastani M, Cao P, Jeon J, Toumazis I, Han SS, Plevritis SK, Blom EF, Kong CY, Tammemägi MC, Feuer EJ, Meza R, de Koning HJ. A Comparative Modeling Analysis of Risk-Based Lung Cancer Screening Strategies. J Natl Cancer Inst 2021; 112:466-479. [PMID: 31566216 PMCID: PMC7225672 DOI: 10.1093/jnci/djz164] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 06/27/2019] [Accepted: 08/14/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared with current United States Preventive Services Task Force (USPSTF) recommendations. METHODS Four independent natural history models were used to perform a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. In total, 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, or Lung Cancer Death Risk Assessment Tool [LCDRAT]), and risk threshold were evaluated for a 1950 US birth cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained, and overdiagnosis. RESULTS Risk-based screening strategies requiring similar screens among individuals ages 55-80 years as the USPSTF criteria (corresponding risk thresholds: Bach = 2.8%; PLCOm2012 = 1.7%; LCDRAT = 1.7%) averted considerably more lung cancer deaths (Bach = 693; PLCOm2012 = 698; LCDRAT = 696; USPSTF = 613). However, life-years gained were only modestly higher (Bach = 8660; PLCOm2012 = 8862; LCDRAT = 8631; USPSTF = 8590), and risk-based strategies had more overdiagnosed cases (Bach = 149; PLCOm2012 = 147; LCDRAT = 150; USPSTF = 115). Sensitivity analyses suggest excluding individuals with limited life expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by more than 65.3%. CONCLUSIONS Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations do. However, they yield modest additional life-years and increased overdiagnosis because of predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life expectancy for determining optimal individual stopping ages.
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Affiliation(s)
- Kevin Ten Haaf
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Zuid-Holland, the Netherlands
| | - Mehrad Bastani
- Department of Radiology, Stanford University, Palo Alto, CA
| | - Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | | | - Summer S Han
- Department of Radiology, Stanford University, Palo Alto, CA.,Department of Medicine, Stanford University, Palo Alto, CA (SSH)
| | | | - Erik F Blom
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Zuid-Holland, the Netherlands
| | - Chung Yin Kong
- Harvard Medical School, Boston, MA.,Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Martin C Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, ON, Canada
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Harry J de Koning
- Department of Public Health, Erasmus MC-University Medical Center Rotterdam, Rotterdam, Zuid-Holland, the Netherlands
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Katki HA, Cheung LC, Landy R. Basing Eligibility for Lung Cancer Screening on Individualized Risk Calculators Should Save More Lives, but Life Expectancy Matters. J Natl Cancer Inst 2020; 112:429-430. [PMID: 31566223 DOI: 10.1093/jnci/djz165] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 08/14/2019] [Indexed: 12/17/2022] Open
Affiliation(s)
- Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
| | - Rebecca Landy
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD
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Rivera MP, Katki HA, Tanner NT, Triplette M, Sakoda LC, Wiener RS, Cardarelli R, Carter-Harris L, Crothers K, Fathi JT, Ford ME, Smith R, Winn RA, Wisnivesky JP, Henderson LM, Aldrich MC. Addressing Disparities in Lung Cancer Screening Eligibility and Healthcare Access. An Official American Thoracic Society Statement. Am J Respir Crit Care Med 2020; 202:e95-e112. [PMID: 33000953 PMCID: PMC7528802 DOI: 10.1164/rccm.202008-3053st] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background: There are well-documented disparities in lung cancer outcomes across populations. Lung cancer screening (LCS) has the potential to reduce lung cancer mortality, but for this benefit to be realized by all high-risk groups, there must be careful attention to ensuring equitable access to this lifesaving preventive health measure.Objectives: To outline current knowledge on disparities in eligibility criteria for, access to, and implementation of LCS, and to develop an official American Thoracic Society statement to propose strategies to optimize current screening guidelines and resource allocation for equitable LCS implementation and dissemination.Methods: A multidisciplinary panel with expertise in LCS, implementation science, primary care, pulmonology, health behavior, smoking cessation, epidemiology, and disparities research was convened. Participants reviewed available literature on historical disparities in cancer screening and emerging evidence of disparities in LCS.Results: Existing LCS guidelines do not consider racial, ethnic, socioeconomic, and sex-based differences in smoking behaviors or lung cancer risk. Multiple barriers, including access to screening and cost, further contribute to the inequities in implementation and dissemination of LCS.Conclusions: This statement identifies the impact of LCS eligibility criteria on vulnerable populations who are at increased risk of lung cancer but do not meet eligibility criteria for screening, as well as multiple barriers that contribute to disparities in LCS implementation. Strategies to improve the selection and dissemination of LCS in vulnerable groups are described.
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Abstract
Robust evidence exists in support of lung cancer (LC) screening with low-dose computed tomography in patients at high risk of developing LC; however, judicious patient selection is necessary to obtain optimal benefit while minimizing harm. Several professional societies have published recommendations regarding patient selection criteria for screening. Multiple risk prediction models that include additional patient-specific risk factors have since been developed to more accurately predict risk of developing LC. Implementation of a new screening program requires thorough multidisciplinary planning and maintenance. Multisociety guidelines highlight 9 principal components to implement and maintain a successful program.
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Affiliation(s)
- Nina A Thomas
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, 96 Jonathan Lucas Street, CSB Suite 816, MSC 630, Charleston, SC 29425, USA
| | - Nichole T Tanner
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Medical University of South Carolina, 96 Jonathan Lucas Street, CSB Suite 816, MSC 630, Charleston, SC 29425, USA; Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Veterans Affairs Hospital, 109 Bee Street, Charleston, SC 29401, USA.
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Lung cancer screening by nodule volume in Lung-RADS v1.1: negative baseline CT yields potential for increased screening interval. Eur Radiol 2020; 31:1956-1968. [PMID: 32997182 PMCID: PMC7979670 DOI: 10.1007/s00330-020-07275-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 07/21/2020] [Accepted: 09/09/2020] [Indexed: 12/17/2022]
Abstract
Objectives The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). Methods Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. Results In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6–21.9% and a potential reduction of LDCT burden of 25.5–41%. Conclusions Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. Key Points • Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. • Negative low-dose CT by Lung-RADS v1.1 was found in 80.6% of NLST eligible and yielded 40 times lower relative risk of lung cancer at 2 years, compared to positive low-dose CT; annual screening could be preference sensitive in this group. • Semi-automatic segmentation of nodule volume and increased screening interval by volumetric Lung-RADS v1.1 could retrospectively suggest a 25.5–41% reduction of LDCT burden, at the cost of 13.6–21.9% rate of delayed diagnosis. Electronic supplementary material The online version of this article (10.1007/s00330-020-07275-w) contains supplementary material, which is available to authorized users.
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Caverly TJ, Zhang X, Hayward RA, Zhu J, Waljee AK. Effects of Random Measurement Error on Lung Cancer Screening Decisions: A Retrospective Cohort-Based Microsimulation Study. Chest 2020; 159:853-861. [PMID: 32941860 DOI: 10.1016/j.chest.2020.08.2112] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Self-reported tobacco pack-year history plays a large role in decisions about low-dose CT screening for lung cancer, yet is challenging to measure accurately. RESEARCH QUESTION To what extent does random measurement error in pack-year information impact screening decisions and screening effectiveness? STUDY DESIGN AND METHODS Retrospective cohort study of 10,449 patients with pack-year history documented at least twice between October 2013 and July 2017 across 8 academic Veterans Affairs sites. Outcome measures included (1) observed reliability of pack-year information based on all repeat measures for the study population and (2) each person's statistically "true" pack-year information based on best linear unbiased predictor from a multilevel linear random effects model. To examine how unreliability leads to misclassification of screening eligibility and inaccuracy in estimating lung cancer risk, we simulated pack-year observations for each person, first comparing simulated pack-year and lung cancer risk values with true values, then comparing outcomes when basing screening decisions on unreliable pack-year information vs true information. RESULTS Reliability of assessing pack-year information in routine practice varied across sites. Thus, we examined the clinical impact of two different levels of reliability, based on the range of intraclass correlation coefficients observed. Using a ≥ 30-pack-year threshold led to a high rate of eligibility misclassifications (48.1% misclassified with higher reliability pack-year information and 60.7% misclassified with lower reliability information). However, using a lung cancer risk threshold leads to fewer misclassifications (47.3%-49.7% misclassified when using lower reliability pack-year information) and maintains screening effectiveness better when using unreliable pack-year information. INTERPRETATION Random error in real-world pack-year assessments leads to a substantial rate of misclassifying who should be offered CT screening if a ≥ 30-pack-year criterion is used. However, using a lung cancer risk threshold mitigates the impact of unreliable pack-year information. Decision-makers concerned about the impact of unreliable pack-year information should consider using risk-based approaches to CT screening.
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Affiliation(s)
- Tanner J Caverly
- VA Ann Arbor Center for Clinical Management Research and University of Michigan Medical School, University of Michigan, Ann Arbor, MI; Institute for Health Policy Innovation, University of Michigan, Ann Arbor, MI.
| | - Xuefei Zhang
- Department of Statistics, University of Michigan, Ann Arbor, MI
| | - Rodney A Hayward
- VA Ann Arbor Center for Clinical Management Research and University of Michigan Medical School, University of Michigan, Ann Arbor, MI; Institute for Health Policy Innovation, University of Michigan, Ann Arbor, MI
| | - Ji Zhu
- Department of Statistics, University of Michigan, Ann Arbor, MI
| | - Akbar K Waljee
- VA Ann Arbor Center for Clinical Management Research and University of Michigan Medical School, University of Michigan, Ann Arbor, MI; Institute for Health Policy Innovation, University of Michigan, Ann Arbor, MI; Michigan Integrated Center for Health Analytics and Medical Prediction, University of Michigan, Ann Arbor, MI
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Cao P, Jeon J, Levy DT, Jayasekera JC, Cadham CJ, Mandelblatt JS, Taylor KL, Meza R. Potential Impact of Cessation Interventions at the Point of Lung Cancer Screening on Lung Cancer and Overall Mortality in the United States. J Thorac Oncol 2020; 15:1160-1169. [PMID: 32160967 PMCID: PMC7329583 DOI: 10.1016/j.jtho.2020.02.008] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/12/2020] [Accepted: 02/14/2020] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Annual lung cancer screening with low-dose computed tomography is recommended for adults aged 55 to 80 years with a greater than or equal to 30 pack-year smoking history who currently smoke or quit within the past 15 years. The 50% who are current smokers should be offered cessation interventions, but information about the impact of adding cessation to screening is limited. METHODS We used an established lung cancer simulation model to compare the effects on mortality of a hypothetical one-time cessation intervention and annual screening versus annual screening only among screen-eligible individuals born in 1950 or 1960. Model inputs were derived from national data and included smoking history, probability of quitting with and without intervention, lung cancer risk and treatment effectiveness, and competing tobacco-related mortality. We tested the sensitivity of results under different assumptions about screening use and cessation efficacy. RESULTS Smoking cessation reduces lung cancer mortality and delays overall deaths versus screening only across all assumptions. For example, if screening was used by 30% of screen-eligible individuals born in 1950, adding an intervention with a 10% quit probability reduces lung cancer deaths by 14% and increases life years gained by 81% compared with screening alone. The magnitude of cessation benefits varied under screening uptake rates, cessation effectiveness, and birth cohort. CONCLUSIONS Smoking cessation interventions have the potential to greatly enhance the impact of lung cancer screening programs. Evaluation of specific interventions, including costs and feasibility of implementation and dissemination, is needed to determine the best possible strategies and realize the full promise of lung cancer screening.
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Affiliation(s)
- Pianpian Cao
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan
| | - David T Levy
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Jinani C Jayasekera
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Christopher J Cadham
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Jeanne S Mandelblatt
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Kathryn L Taylor
- Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, District of Columbia
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.
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Robbins HA, Berg CD, Cheung LC, Chaturvedi AK, Katki HA. Identification of Candidates for Longer Lung Cancer Screening Intervals Following a Negative Low-Dose Computed Tomography Result. J Natl Cancer Inst 2020; 111:996-999. [PMID: 30976808 DOI: 10.1093/jnci/djz041] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Revised: 01/23/2019] [Accepted: 02/22/2019] [Indexed: 12/17/2022] Open
Abstract
Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.
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Slatore CG. COUNTERPOINT: Can Shared Decision-Making of Physicians and Patients Improve Outcomes in Lung Cancer Screening? No. Chest 2020; 156:15-17. [PMID: 31279362 DOI: 10.1016/j.chest.2019.03.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 03/08/2019] [Indexed: 01/28/2023] Open
Affiliation(s)
- Christopher G Slatore
- Center to Improve Veteran Involvement in Care, VA Portland Health Care System, Portland, OR; Section of Pulmonary & Critical Care Medicine, VA Portland Health Care System, Portland, OR; Division of Pulmonary & Critical Care Medicine, Department of Medicine, Oregon Health & Science University, Portland, OR.
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Meng Q, Ren P, Gao P, Dou X, Chen X, Guo L, Song Y. Effectiveness and Feasibility of Complementary Lung-RADS Version 1.1 in Risk Stratification for pGGN in LDCT Lung Cancer Screening in a Chinese Population. Cancer Manag Res 2020; 12:189-198. [PMID: 32021435 PMCID: PMC6957006 DOI: 10.2147/cmar.s232269] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 12/30/2019] [Indexed: 12/18/2022] Open
Abstract
Purpose To evaluate the effectiveness of using a modified lung imaging reporting and data system (Lung-RADS) for risk stratification of pure ground-glass nodules (pGGNs) in low-dose computed tomography (LDCT) for lung cancer (LC) screenings in China. Patients and Methods Eight subjects with nine pGGNs originating from a Cancer Screening Program were enrolled as training set and 32 asymptomatic subjects with 35 pGGNs were selected as validation set from November 2013 to October 2018. The complementary Lung-RADS categories were set based on the GGN-vessel relationship (GVR). The correlations between GGN-vessel relationships and pathology were evaluated, and the diagnostic value of complementary Lung-RADS version 1.1 in discriminating malignant pGGNs were analyzed. Results The inter-reader agreements for Lung-RADS 1.1 (intraclass correlation coefficient (ICC= 0.999) and complementary Lung-RADS 1.1 (ICC= 0.971) displayed good reliability. The combined incidence of invasive adenocarcinoma in type III and IV was more than that of benign and preinvasive diseases (30% vs 75%, P=0.013). Type II GVR between two benign (66.7%), seven preinvasive (53.8%), and six invasive (21.4%) GGN cases was statistically significant (χ2=5.415, P=0.019). GGN pathological groups and GVR had a significant correlation (r=0.584, P=0.00). Compared to Lung-RADS 1.1, complementary Lung-RADS 1.1 had better performance in the training set, with its sensitivity increased from 33.3% to 88.9%, accuracy increased from 44.4% to 88.9%, false-negative proportion (FNP) decreased from 66.7% to 11.1%, and the sensitivity to predict malignant nodules increased from 13.8% to 93.1%, accuracy increased from 28.6% to 80.0%, and FNP decreased from 86.2% to 6.9% in validation set. The detection rate of preinvasive disease and adenocarcinoma was increased from 12.5% to 90.6% and that of missed diagnosis decreased from 87.5% to 9.4% in the validation set, P=0.004. Conclusion Complementary Lung-RADS 1.1 is superior to Lung-RADS 1.1 and would be beneficial for LC screening of LDCT in China.
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Affiliation(s)
- Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengfei Ren
- Department of Molecular Pathology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Pengrui Gao
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xinmin Dou
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xuejun Chen
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Lanwei Guo
- Department of Cancer Prevention Office, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yongping Song
- Department of Hematology, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
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Abstract
Biomarkers that focus on lung cancer risk assessment, detection, prognosis, diagnosis, and personalized treatment are in various stages of development. This article provides an overview of lung cancer biomarker development, focusing on clinical utility and highlighting 2 unmet clinical needs: selection of high-risk patients for lung cancer screening and differentiation of early lung cancer from benign pulmonary nodules. The authors highlight biomarkers under development and those lung cancer screening and nodule management biomarkers post-clinical validation. Finally, trends in lung cancer biomarker development that may improve accuracy and accelerate implementation in practice are discussed.
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