Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review.

Alpamys Issanov, Atul Aravindakshan, Lorri Puil, Martin C Tammemägi, Stephen Lam, Trevor J B Dummer
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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.

从未吸烟者罹患肺癌的风险预测模型:系统综述方案。
背景:肺癌是最常诊断出的癌症之一,也是全球癌症相关死亡的主要原因。虽然吸烟是导致肺癌的主要原因,但从未吸烟的人也常被诊断出肺癌。目前,从未吸烟者被确诊为肺癌的比例正在上升。尽管这一趋势令人担忧,但这部分人群却没有资格接受肺部筛查。随着从未吸烟者在肺癌病例中所占比例的增加,迫切需要开发预测模型来识别从未吸烟的高危人群,并将其纳入肺癌筛查计划。因此,我们的系统性综述旨在对现有的从未吸烟者肺癌风险预测模型的证据进行全面总结:将在MEDLINE(Ovid)、Embase(Ovid)、Web of Science Core Collection(Clarivate Analytics)、Scopus、Europe PMC和Open-Access Theses and Dissertations数据库中进行电子检索。两名审稿人将使用 Covidence 审稿平台独立进行标题和摘要筛选、全文审阅和数据提取。数据提取将根据预测建模研究系统性综述批判性评估和数据提取清单(CHARMS)进行。偏倚风险将由两名审稿人使用预测模型偏倚风险评估工具 (PROBAST) 独立评估。如果发现有足够数量的研究对同一预测模型进行了外部验证,我们将结合模型的性能指标,评估该模型在不同环境和人群中的平均预测准确性(如校准、区分度),并探索异质性的来源:综述结果将确定从未吸烟人群的肺癌风险预测模型。讨论:综述结果将确定从未吸烟人群肺癌风险预测模型,这些模型将对计划开发新型预测模型的研究人员、临床从业人员和政策制定者有所帮助,这些人员和政策制定者正在为从未吸烟人群的临床决策和未来肺癌筛查策略的制定寻求指导:本方案已在 PROSPERO 注册,注册号为 CRD42023483824。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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