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在 COVID-19 大流行之前,癌症诊断和治疗的滞后时间对临床结局的影响:系统评价和荟萃分析的范围综述。

The impact of lag time to cancer diagnosis and treatment on clinical outcomes prior to the COVID-19 pandemic: A scoping review of systematic reviews and meta-analyses.

机构信息

Division of Cancer Epidemiology, McGill University, Montreal, Canada.

Department of Oncology, McGill University, Montreal, Canada.

出版信息

Elife. 2023 Jan 31;12:e81354. doi: 10.7554/eLife.81354.

Abstract

BACKGROUND

The COVID-19 pandemic has disrupted cancer care, raising concerns regarding the impact of wait time, or 'lag time', on clinical outcomes. We aimed to contextualize pandemic-related lag times by mapping pre-pandemic evidence from systematic reviews and/or meta-analyses on the association between lag time to cancer diagnosis and treatment with mortality- and morbidity-related outcomes.

METHODS

We systematically searched MEDLINE, EMBASE, Web of Science, and Cochrane Library of Systematic Reviews for reviews published prior to the pandemic (1 January 2010-31 December 2019). We extracted data on methodological characteristics, lag time interval start and endpoints, qualitative findings from systematic reviews, and pooled risk estimates of mortality- (i.e., overall survival) and morbidity- (i.e., local regional control) related outcomes from meta-analyses. We categorized lag times according to milestones across the cancer care continuum and summarized outcomes by cancer site and lag time interval.

RESULTS

We identified 9032 records through database searches, of which 29 were eligible. We classified 33 unique types of lag time intervals across 10 cancer sites, of which breast, colorectal, head and neck, and ovarian cancers were investigated most. Two systematic reviews investigating lag time to diagnosis reported different findings regarding survival outcomes among paediatric patients with Ewing's sarcomas or central nervous system tumours. Comparable risk estimates of mortality were found for lag time intervals from surgery to adjuvant chemotherapy for breast, colorectal, and ovarian cancers. Risk estimates of pathologic complete response indicated an optimal time window of 7-8 weeks for neoadjuvant chemotherapy completion prior to surgery for rectal cancers. In comparing methods across meta-analyses on the same cancer sites, lag times, and outcomes, we identified critical variations in lag time research design.

CONCLUSIONS

Our review highlighted measured associations between lag time and cancer-related outcomes and identified the need for a standardized methodological approach in areas such as lag time definitions and accounting for the waiting-time paradox. Prioritization of lag time research is integral for revised cancer care guidelines under pandemic contingency and assessing the pandemic's long-term effect on patients with cancer.

FUNDING

The present work was supported by the Canadian Institutes of Health Research (CIHR-COVID-19 Rapid Research Funding opportunity, VR5-172666 grant to Eduardo L. Franco). Parker Tope, Eliya Farah, and Rami Ali each received an MSc. stipend from the Gerald Bronfman Department of Oncology, McGill University.

摘要

背景

COVID-19 大流行扰乱了癌症治疗,人们担心等待时间(也称为“滞后时间”)会对临床结果产生影响。我们旨在通过绘制与癌症诊断和治疗滞后时间与死亡率和发病率相关结局之间关联的系统评价和/或荟萃分析的前期证据,来了解与大流行相关的滞后时间。

方法

我们系统地检索了 MEDLINE、EMBASE、Web of Science 和 Cochrane 系统评价图书馆,以查找大流行前(2010 年 1 月 1 日至 2019 年 12 月 31 日)发表的综述。我们提取了关于方法特征、滞后时间间隔开始和终点、系统评价的定性发现以及荟萃分析中死亡率(即总生存)和发病率(即局部区域控制)相关结局的汇总风险估计的数据。我们根据癌症治疗连续体中的里程碑对滞后时间进行分类,并按癌症部位和滞后时间间隔总结结局。

结果

我们通过数据库检索共确定了 9032 条记录,其中 29 条符合条件。我们对 10 个癌症部位的 33 种不同类型的滞后时间间隔进行了分类,其中乳腺癌、结直肠癌、头颈部癌和卵巢癌的研究最多。两项关于诊断滞后时间的系统评价报告了在患有尤因肉瘤或中枢神经系统肿瘤的儿科患者中生存结局的不同发现。对于乳腺癌、结直肠癌和卵巢癌,从手术到辅助化疗的滞后时间间隔,我们发现了死亡率的可比风险估计。病理完全缓解的风险估计表明,直肠癌新辅助化疗完成与手术之间的最佳时间窗口为 7-8 周。在比较相同癌症部位、滞后时间和结局的荟萃分析中的方法时,我们发现滞后时间研究设计存在关键差异。

结论

我们的综述强调了滞后时间与癌症相关结局之间的测量关联,并确定了在滞后时间定义和考虑等待时间悖论等方面需要标准化的方法学方法。在修订大流行应急癌症治疗指南和评估大流行对癌症患者的长期影响时,优先考虑滞后时间研究至关重要。

资助

本工作得到加拿大卫生研究院(COVID-19 快速研究资金机会,VR5-172666 赠款给 Eduardo L. Franco)的支持。Parker Tope、Eliya Farah 和 Rami Ali 均从麦吉尔大学杰拉尔德·布朗夫曼肿瘤学系获得了 MSc 奖学金。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b79/9928418/790b424b2ad9/elife-81354-fig1.jpg

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