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. 2018 Jul 3;169(1):1-9.
doi: 10.7326/M17-2561. Epub 2018 May 29.

Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study

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Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study

Tanner J Caverly et al. Ann Intern Med. .
Free PMC article

Abstract

Background: Many health systems are exploring how to implement low-dose computed tomography (LDCT) screening programs that are effective and patient-centered.

Objective: To examine factors that influence when LDCT screening is preference-sensitive.

Design: State-transition microsimulation model.

Data sources: Two large randomized trials, published decision analyses, and the SEER (Surveillance, Epidemiology, and End Results) cancer registry.

Target population: U.S.-representative sample of simulated patients meeting current U.S. Preventive Services Task Force criteria for screening eligibility.

Time horizon: Lifetime.

Perspective: Individual.

Intervention: LDCT screening annually for 3 years.

Outcome measures: Lifetime quality-adjusted life-year gains and reduction in lung cancer mortality. To examine the effect of preferences on net benefit, disutilities (the "degree of dislike") quantifying the burden of screening and follow-up were varied across a likely range. The effect of varying the rate of false-positive screening results and overdiagnosis associated with screening was also examined.

Results of base-case analysis: Moderate differences in preferences about the downsides of LDCT screening influenced whether screening was appropriate for eligible persons with annual lung cancer risk less than 0.3% or life expectancy less than 10.5 years. For higher-risk eligible persons with longer life expectancy (roughly 50% of the study population), the benefits of LDCT screening overcame even highly negative views about screening and its downsides.

Results of sensitivity analysis: Rates of false-positive findings and overdiagnosed lung cancer were not highly influential.

Limitation: The quantitative thresholds that were identified may vary depending on the structure of the microsimulation model.

Conclusion: Identifying circumstances in which LDCT screening is more versus less preference-sensitive may help clinicians personalize their screening discussions, tailoring to both preferences and clinical benefit.

Primary funding source: None.

Figures

Figure 1
Figure 1
Quality adjusted life year (QALY) gains with 3 annual low-dose computed tomography (LDCT) screens and lifetime follow-up (y-axis) by baseline lung cancer risk (x-axis). Baseline lung cancer risk is by percentile from 0–100. The table immediately below presents, for each corresponding decile of baseline risk, the number needed to screen to avoid 1 all-cause death. *NNS = Number needed to screen to avoid 1 death (95% bootstrap uncertainty range for mean value within each decile
Figure 2
Figure 2
Panel A. For each quintile of competing mortality risk, quality adjusted life year (QALY) gains (y-axis) by baseline lung cancer risk (x-axis) under base-case assumptions. Baseline lung cancer risk is by percentile from 0–100. Incremental QALY gains from LDCT screening decline as competing mortality risk increases. Panel B. Quality adjusted life year (QALY) gains for highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis). Panel C. Quality adjusted life year (QALY) gains when excluding persons in highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis).
Figure 2
Figure 2
Panel A. For each quintile of competing mortality risk, quality adjusted life year (QALY) gains (y-axis) by baseline lung cancer risk (x-axis) under base-case assumptions. Baseline lung cancer risk is by percentile from 0–100. Incremental QALY gains from LDCT screening decline as competing mortality risk increases. Panel B. Quality adjusted life year (QALY) gains for highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis). Panel C. Quality adjusted life year (QALY) gains when excluding persons in highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis).
Figure 2
Figure 2
Panel A. For each quintile of competing mortality risk, quality adjusted life year (QALY) gains (y-axis) by baseline lung cancer risk (x-axis) under base-case assumptions. Baseline lung cancer risk is by percentile from 0–100. Incremental QALY gains from LDCT screening decline as competing mortality risk increases. Panel B. Quality adjusted life year (QALY) gains for highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis). Panel C. Quality adjusted life year (QALY) gains when excluding persons in highest (5th) quintile of competing mortality risk, by baseline lung cancer risk (x-axis).

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