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比较预测无症状个体结直肠癌发生的预后模型:一项系统文献回顾和 EPIC 及 UK Biobank 前瞻性队列研究的外部验证。

Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies.

机构信息

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.

Julius Center for Health Sciences and Primary Care, Umc Utrecht, Utrecht University, Utrecht, The Netherlands.

出版信息

Gut. 2019 Apr;68(4):672-683. doi: 10.1136/gutjnl-2017-315730. Epub 2018 Apr 3.

DOI:10.1136/gutjnl-2017-315730
PMID:29615487
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6580880/
Abstract

OBJECTIVE

To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.

DESIGN

Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).

RESULTS

The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.

CONCLUSION

Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.

摘要

目的

在两个大型基于人群的前瞻性队列中,系统地识别和验证不需要侵袭性检测的结直肠癌风险预测模型。

设计

通过更新已发表的系统评价来识别模型,并在欧洲癌症与营养前瞻性调查(EPIC)和英国生物库中进行验证。通过区分度(C 统计量)和校准(观察到的概率与预测概率的图)评估模型在研究入组后 5 年或 10 年内预测结直肠癌发生的性能。

结果

系统评价及其更新从 8 篇文献中确定了 16 个模型(8 个结直肠癌、5 个结肠癌和 3 个直肠癌)。每个模型验证的参与者人数从 41587 人到 396515 人不等,病例数从 115 人到 1781 人不等。模型之间的合格和不合格参与者基本相似。可评估模型的校准非常好,并通过重新校准进一步改善。模型在验证队列中的 C 统计量基本相似,英国生物库中的最高值为 0.70(95%置信区间 0.68 至 0.72),EPIC 中的最高值为 0.71(95%置信区间 0.67 至 0.74)。

结论

这些非侵袭性模型中的几个在两个外部验证人群中都表现出良好的校准和区分度,因此可能是基于人群的结直肠癌筛查计划中风险分层的潜在合适候选者。未来的工作应该通过建模和影响研究来评估这种潜力,并确定是否可以进一步提高其性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/1f84fb07f3ef/gutjnl-2017-315730f07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/fa2baa33a335/gutjnl-2017-315730f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/b67813869bdf/gutjnl-2017-315730f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/a38fc6a36858/gutjnl-2017-315730f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/6f0e6a8dc8d0/gutjnl-2017-315730f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/4a99d119b83d/gutjnl-2017-315730f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/df24ff42e5c4/gutjnl-2017-315730f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/1f84fb07f3ef/gutjnl-2017-315730f07.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/fa2baa33a335/gutjnl-2017-315730f01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/b67813869bdf/gutjnl-2017-315730f02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/a38fc6a36858/gutjnl-2017-315730f03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/6f0e6a8dc8d0/gutjnl-2017-315730f04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/4a99d119b83d/gutjnl-2017-315730f05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/df24ff42e5c4/gutjnl-2017-315730f06.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e8a2/6580880/1f84fb07f3ef/gutjnl-2017-315730f07.jpg

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