A Systematic Review of Estimating Breast Cancer Recurrence at the Population-Level with Administrative Data
A partir d'une revue systématique de la littérature (17 articles), cette méta-analyse compare la performance, du point de vue notamment de la sensibilité, de la spécificité et de la précision, d'algorithmes utilisant des données administratives pour estimer au niveau de la population le nombre de cancers du sein ayant récidivé
Background : Exact numbers of breast cancer (BC) recurrences are currently unknown at the population-level, since 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 of publications estimating BC 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 (PRISMA) 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 (GLMM) 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 to studies using detection rules without specified model (58.8%). The GLMM for all recurrence types reported an accuracy of 92.2% (95%CI: 88.4-94.8%).
Conclusion : Publications reporting algorithms for detecting BC 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 BC recurrence at the population-level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.
Journal of the National Cancer Institute , résumé, 2019