Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review
- PMID: 38347647
- PMCID: PMC10863273
- DOI: 10.1186/s41512-024-00166-4
Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review
Abstract
Background: Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.
Methods: Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.
Discussion: The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.
Systematic review registration: This protocol has been registered in PROSPERO under the registration number CRD42023483824.
Keywords: Lung cancer; Never smokers; Prediction model; Systematic review.
© 2024. The Author(s).
Conflict of interest statement
The authors declare that they have no competing interests.
Similar articles
-
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217. Cochrane Database Syst Rev. 2022. PMID: 36321557 Free PMC article.
-
Beyond the black stump: rapid reviews of health research issues affecting regional, rural and remote Australia.Med J Aust. 2020 Dec;213 Suppl 11:S3-S32.e1. doi: 10.5694/mja2.50881. Med J Aust. 2020. PMID: 33314144
-
Risk prediction models for emergence delirium in paediatric general anaesthesia: a systematic review.BMJ Open. 2021 Jan 6;11(1):e043968. doi: 10.1136/bmjopen-2020-043968. BMJ Open. 2021. PMID: 33408214 Free PMC article.
-
The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery.Cochrane Database Syst Rev. 2021 Dec 21;12(12):CD013139. doi: 10.1002/14651858.CD013139.pub2. Cochrane Database Syst Rev. 2021. PMID: 34931303 Free PMC article. Review.
-
Prognosis of adults and children following a first unprovoked seizure.Cochrane Database Syst Rev. 2023 Jan 23;1(1):CD013847. doi: 10.1002/14651858.CD013847.pub2. Cochrane Database Syst Rev. 2023. PMID: 36688481 Free PMC article. Review.
References
-
- SEER Cancer Stat Facts: Lung and Bronchus Cancer: National Cancer Institute. Bethesda, MD. Available from: https://seer.cancer.gov/statfacts/html/lungb.html. Accessed 5 Feb 2024.
-
- SEER*Explorer: an interactive website for SEER cancer statistics [Internet]: surveillance research program, National Cancer Institute; 2023 Apr 19. (updated: 2023 Jun 8). Accessed 26 Oct 2023. Available from: https://seer.cancer.gov/statistics-network/explorer/. Data source(s): SEER Incidence Data, November 2022 Submission (1975–2020), SEER 22 registries (excluding Illinois and Massachusetts). Expected Survival Life Tables by Socio-Economic Standards.
LinkOut - more resources
Full Text Sources