| 贺冰洁,陈暐烨,刘立立,朱海艳,程昊哲,张翌玺,王胜锋,詹思延.宫颈癌发病风险预测模型的系统综述[J].中华流行病学杂志,2021,42(10):1855-1862 |
| 宫颈癌发病风险预测模型的系统综述 |
| The risk prediction models for occurrence of cervical cancer: a systematic review |
| 收稿日期:2020-08-06 出版日期:2021-10-23 |
| DOI：10.3760/cma.j.cn112338-20200806-01031 |
| 中文关键词: 宫颈癌 预测模型 危险因素 偏倚风险 系统综述 |
| 英文关键词: Cervical cancer Predictive model Risk factor Risk of bias Systematic review |
| 基金项目:国家重点研发计划（2018YFC1311704）；大气重污染成因与治理攻关项目（DQGG0404）；中央高校基本科研业务费资助 |
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| 中文摘要: |
| 目的 系统评价宫颈癌发病风险预测模型的现况，为实践工作选择最合适的模型提供证据，指导宫颈癌筛查。方法 以宫颈癌和风险预测模型相关的两组中英文关键词，分别检索中国知网、万方数据知识服务平台及PubMed、Embase、Cochrane Library，筛选截至2019年11月21日发表构建或验证宫颈癌发病模型相关文献。根据CHARMS清单制定提取表，以PROBAST工具评估偏倚风险。结果 共纳入12篇文献，涉及15个模型，其中5个模型在中国构建。预测结局包含从宫颈癌前病变到癌症发生的多个阶段宫颈涂片异常（1）、CIN的发生或复发（9）、宫颈癌发生（5）。使用较多的预测因素为HPV感染（12）、年龄（7）、吸烟（5）和文化程度（5）。有2个模型采用机器学习建模。模型表现上，区分度范围为0.53~0.87，而校准度只有2个模型正确评价。仅2个模型在中国台湾地区利用不同时间段的人群进行了外部验证。偏倚风险评价发现所有模型均为高风险，尤其分析领域，问题集中在缺失数据处理不当（13）、模型表现评价不完整（13）、内部验证使用不当（12）和样本量不足（11）。另外，预测因素和结局测量不一致（8）、结局测量盲法使用情况未报告（8）的问题较突出。相对而言，Rothberg等（2018）的模型质量较高。结论 宫颈癌发病风险预测模型有一定数量但质量较差，亟须提高预测因素与结局的测量以及缺失数据处理和模型表现评价等统计分析细节，对现有模型进行外部验证，以更好地指导筛查。 |
| 英文摘要: |
| Objective To systematically summarize and assess risk prediction models for occurrence of cervical cancer and to provide evidence for selecting the most reliable model for practice, and guide cervical cancer screening. Methods Two groups of keywords related to cervical cancer and risk prediction model were searched on Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, and Cochrane Library). Original articles that developed or validated risk prediction models and published before November 21, 2019, were selected. Information form was created based on the CHARMS checklist. The PROBAST was used to assess the risk of bias. Results 12 eligible articles were identified, describing 15 prediction models, of which five were established in China. The predicted outcomes included multiple stages from cervical precancerous lesions to cancer occurrence, i.e., abnormal Pap smear (1), occurrence or recurrence of CIN (9), and occurrence of cervical cancer (5), etc. The most frequently used predictors were HPV infection (12), age (7), smoking (5), and education (5). There were two models using machine learning to develop models. In terms of model performance, the discrimination ranged from 0.53 to 0.87, while only two models assessed the calibration correctly. Only two models were externally validated in Taiwan of China, using people in different periods. All of the models were at high risk of bias, especially in the analysis domain. The problems were concentrated in the improper handling of missing data (13), preliminary evaluation of model performance (13), improper use of internal validation (12), and insufficient sample size (11). In addition, the problems of inconsistency measurements of predictors and outcomes (8) and the flawed report of the use of blindness for outcome measures (8) were also severe. Compared with the other models, the Rothberg (2018) model had relatively high quality.Conclusions There are a certain number of cervical cancer risk prediction models, but the quality is poor. It is urgent to improve the measurement of predictors and outcomes, the statistical analysis details such as handling missing data and evaluation of model performance and externally validate existing models to better guide screening. |
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