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Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.
Toumazis, Iakovos; Cao, Pianpian; de Nijs, Koen; Bastani, Mehrad; Munshi, Vidit; Hemmati, Mehdi; Ten Haaf, Kevin; Jeon, Jihyoun; Tammemägi, Martin; Gazelle, G Scott; Feuer, Eric J; Kong, Chung Yin; Meza, Rafael; de Koning, Harry J; Plevritis, Sylvia K; Han, Summer S.
Affiliation
  • Toumazis I; Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.).
  • Cao P; Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.).
  • de Nijs K; Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).
  • Bastani M; Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York (M.B.).
  • Munshi V; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.).
  • Hemmati M; Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (I.T., M.H.).
  • Ten Haaf K; Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).
  • Jeon J; Department of Epidemiology, University of Michigan, Ann Arbor, Michigan (P.C., J.J.).
  • Tammemägi M; Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada (M.T.).
  • Gazelle GS; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts (V.M., G.S.G.).
  • Feuer EJ; Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland (E.J.F.).
  • Kong CY; Division of General Internal Medicine, Department of Medicine, Mount Sinai Hospital, New York, New York (C.Y.K.).
  • Meza R; Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, and Department of Integrative Oncology, BC Cancer Research Institute, British Columbia, Canada (R.M.).
  • de Koning HJ; Erasmus MC-University Medical Center, Rotterdam, the Netherlands (K. de N., K. ten H., H.J. de K.).
  • Plevritis SK; Department of Biomedical Data Sciences, Stanford University, Stanford, California (S.K.P.).
  • Han SS; Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, California (S.S.H.).
Ann Intern Med ; 176(3): 320-332, 2023 03.
Article in En | MEDLINE | ID: mdl-36745885
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).
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lung Neoplasms Type of study: Diagnostic_studies / Etiology_studies / Health_economic_evaluation / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Aged80 / Humans / Middle aged Language: En Journal: Ann Intern Med Year: 2023 Type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Lung Neoplasms Type of study: Diagnostic_studies / Etiology_studies / Health_economic_evaluation / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Aged80 / Humans / Middle aged Language: En Journal: Ann Intern Med Year: 2023 Type: Article