评估风险预测模型以选择欧洲肺癌筛查参与者:前瞻性队列联合分析

IF 23.8 1区 医学 Q1 MEDICAL INFORMATICS Lancet Digital Health Pub Date : 2024-08-21 DOI:10.1016/S2589-7500(24)00123-7
Xiaoshuang Feng PhD , Patrick Goodley MBBCh , Karine Alcala MS , Florence Guida PhD , Prof Rudolf Kaaks PhD , Prof Roel Vermeulen PhD , George S Downward PhD , Catalina Bonet MSc , Sandra M Colorado-Yohar PhD , Demetrius Albanes MD , Stephanie J Weinstein PhD , Prof Marcel Goldberg PhD , Prof Marie Zins PhD , Prof Caroline Relton PhD , Prof Arnulf Langhammer PhD , Anne Heidi Skogholt PhD , Mattias Johansson PhD , Hilary A Robbins PhD
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引用次数: 0

摘要

背景肺癌风险预测模型可有效识别应接受肺癌筛查的人群。然而,欧洲尚未对这些模型的性能进行全面评估。我们的目的是在欧洲前瞻性队列中对几种预测肺癌发病率或死亡率的风险预测模型的性能进行外部验证和评估。方法我们分析了肺癌队列联合会(Lung Cancer Cohort Consortium)集合数据库中四个前瞻性队列中来自九个欧洲国家的 240 137 名 45-80 岁、目前或曾经有吸烟史的参与者,这四个前瞻性队列分别是:α-生育酚、β-胡萝卜素癌症预防研究(芬兰)、北特伦德拉格健康研究(挪威)、CONSTANCES(法国)和欧洲癌症与营养前瞻性调查(丹麦、德国、意大利、西班牙、瑞典、荷兰和挪威)。我们评估了十种肺癌风险模型,包括巴赫模型、前列腺癌、肺癌、结肠直肠癌和卵巢癌筛查试验 2012 模型 (PLCOm2012)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、北特伦德拉格健康研究 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌早期预警优化模型 (HUNT)、肺癌风险评估工具 (LCRAT)、肺癌死亡风险评估工具 (LCDRAT)、肺癌风险优化预警模型 (OWL)、伦敦大学学院死亡模型 (UCLD)、伦敦大学学院发病模型 (UCLI)、利物浦肺项目第二版模型 (LLP 第二版) 和利物浦肺项目第三版模型 (LLP 第三版)。我们用预期病例或死亡病例与观察病例或死亡病例之比来量化模型校准,并用接收者工作特征曲线下面积(AUC)来区分模型。对于每个模型,我们还确定了与美国预防服务工作组 2021 年(USPSTF-2021)、美国预防服务工作组 2013 年(USPSTF-2013)和 Nederlands-Leuvens Longkanker Screenings Onderzoek(NELSON)标准相同的筛查人数的风险阈值。在大多数国家,大多数模型都具有合理的校准,但在 8 个国家,LLP 第 2 版对风险的预测超过了 50%(预期与观察值之比≥1-50)。PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型在大多数国家显示出相似的区分度,AUC 从 0-68(95% CI 0-59-0-77)到 0-83(0-78-0-89)不等,而 LLP 版本 2 和 LLP 版本 3 显示出较低的区分度,AUC 从 0-64(95% CI 0-57-0-72)到 0-78(0-74-0-83)不等。在汇总所有国家的数据(但不包括 HUNT 队列)时,33-9% 的患者(216 387 例中的 73 313 例)符合 USPSTF-2021 标准,其中包括 74-8% 的肺癌患者(1185 例)和 76-3% 的 5 年以上肺癌死亡患者(730 例)。根据 USPSTF-2013 和 NELSON 标准,符合条件的人数较少。在应用阈值选择与 USPSTF-2021 相同规模的人群后,PLCOm2012、LCDRAT、LCRAT、Bach、HUNT、OWL、UCLD 和 UCLI 模型识别了 77%-6%-79-1% 的未来病例,尽管与 USPSTF-2021 标准相比,它们选择的个体年龄稍大。USPSTF-2013 和 NELSON 的结果相似。解释:在欧洲国家,几种肺癌风险预测模型表现良好,如果用来代替分类资格标准,可能会提高肺癌筛查的效率。
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Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis

Background

Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts.

Methods

We analysed 240 137 participants aged 45–80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCOm2012), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London—Death (UCLD), the University College London—Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands–Leuvens Longkanker Screenings Onderzoek (NELSON) criteria.

Findings

Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59–0·77) to 0·83 (0·78–0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57–0·72) to 0·78 (0·74–0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCOm2012, LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%–79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON.

Interpretation

Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria.

Funding

US National Cancer Institute, l’Institut National du Cancer, Cancer Research UK.

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来源期刊
CiteScore
41.20
自引率
1.60%
发文量
232
审稿时长
13 weeks
期刊介绍: The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health. The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health. We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.
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