基于行政数据评估人群乳腺癌复发的系统评价。
A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.
机构信息
Department of Oncology, KU Leuven - University of Leuven, Leuven, Belgium.
Research Department, Belgian Cancer Registry, Brussels, Belgium.
出版信息
J Natl Cancer Inst. 2020 Oct 1;112(10):979-988. doi: 10.1093/jnci/djaa050.
BACKGROUND
Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.
METHODS
The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.
RESULTS
Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).
CONCLUSIONS
Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.
背景
目前，由于难以主动收集，人群水平的乳腺癌复发的确切数字尚不清楚。此前，真实世界的数据（如行政索赔）已在专家或数据驱动（机器学习）算法中用于估计癌症复发。我们展示了第一个系统综述和荟萃分析，据我们所知，这是使用基于行政数据的算法估算人群水平乳腺癌复发的出版物。
方法
系统文献检索遵循系统评价和荟萃分析报告的首选项目。我们评估和比较了算法的敏感性、特异性、阳性预测值、阴性预测值和总体准确性。使用广义线性混合模型进行随机效应荟萃分析，以获得准确性的汇总估计。
结果
有 17 篇文章符合纳入标准。大多数文章将病历信息作为黄金标准，定义为任何复发。两项研究仅将骨转移包括在复发的定义中。与使用无特定模型的检测规则（58.8%）相比，使用基于模型的方法（决策树或逻辑回归）的研究较少（41.2%）。所有复发类型的广义线性混合模型报告的准确性为 92.2%（95%置信区间为 88.4%至 94.8%）。
结论
报道用于检测乳腺癌复发的算法的出版物数量有限且存在异质性。对现有算法的全面分析表明需要更加标准化和验证。荟萃分析总体上报告了较高的准确性，这表明算法是在人群水平识别乳腺癌复发的有前途的工具。基于规则的方法与新兴的机器学习算法相结合可能是未来值得探索的方向。
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