ArticleJournal of the National Cancer Institute2012
Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer.
Article in Journal of the National Cancer Institute, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 2 of them syntheses that pooled it.
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Who cites it
82 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.Journal of the National Cancer Institute · 2020Pooled it
- Accuracy of administrative databases in detecting primary breast cancer diagnoses: a systematic review.BMJ open · 2018Pooled it
- Validation in Alberta of an administrative data algorithm to identify cancer recurrence.Current oncology (Toronto, Ont.) · 2020Trial
- Using machine learning to estimate time-to-progression in ovarian cancer based on cancer registry and administrative health data.ESMO real world data and digital oncology · 2026Article
- Bridging the evidence gap in trial inclusion with real world safety profile assessment in relapsed/refractory multiple myeloma patients.BMC cancer · 2026Article
- Diagnostic delays, treatment timeliness, and patient experiences in triple-negative breast cancer: a mixed-methods study.Frontiers in oncology · 2026Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- Considerations for emulations of randomized controlled trials using real-world data: learnings from an emulation of MONALEESA-2.Journal of comparative effectiveness research · 2025Article
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
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- Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Journal of biomedical informatics · 2025Article
- Multimodal identification of a rare head and neck cancer patient cohort in the clinical data warehouse of Greater Paris Teaching Hospital.ESMO real world data and digital oncology · 2025Article
- Optimal Surrogate-Assisted Sampling for Cost-Efficient Validation of Electronic Health Record Outcomes.Statistics in medicine · 2025Article
- Misclassification of second primary and recurrent breast cancer in the surveillance epidemiology and end results registry.Cancer causes & control : CCC · 2025Article
- Correlation Between Disease-Free Survival Endpoints and Overall Survival in Elderly Patients with Early-Stage HER2-Negative Breast Cancer: A SEER-Medicare Analysis.Advances in therapy · 2025Article
- Association between pre-diagnosis recreational physical activity and risk of breast cancer recurrence: the California Teachers Study.Cancer causes & control : CCC · 2024Article
- Accuracy of a Cancer Registry Versus Clinical Care Team Chart Abstraction in Identifying Cancer Recurrence.Mayo Clinic proceedings. Innovations, quality & outcomes · 2024Article
- Association between trajectories of adherence to endocrine therapy and risk of treated breast cancer recurrence among US nonmetastatic breast cancer survivors.British journal of cancer · 2024Article
- Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
22 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
Funding
Abstract
backgroundStudies of breast cancer outcomes rely on the identification of second breast cancer events (recurrences and second breast primary tumors). Cancer registries often do not capture recurrences, and chart abstraction can be infeasible or expensive. An alternative is using administrative health-care data to identify second breast cancer events; however, these algorithms must be validated against a gold standard.
methodsWe developed algorithms using data from 3152 women in an integrated health-care system who were diagnosed with stage I or II breast cancer in 1993-2006. Medical record review served as the gold standard for second breast cancer events. Administrative data used in algorithm development included procedures, diagnoses, prescription fills, and cancer registry records. We randomly divided the cohort into training and testing samples and used a classification and regression tree analysis to build algorithms for classifying women as having or not having a second breast cancer event. We created several algorithms for researchers to use based on the relative importance of sensitivity, specificity, and positive predictive value (PPV) in future studies.
resultsThe algorithm with high specificity and PPV had 89% sensitivity (95% confidence interval [CI] = 84% to 92%), 99% specificity (95% CI = 98% to 99%), and 90% PPV (95% CI = 86% to 94%); the high-sensitivity algorithm had 96% sensitivity (95% CI = 93% to 98%), 95% specificity (95% CI = 94% to 96%), and 74% PPV (95% CI = 68% to 78%).
conclusionsAlgorithms based on administrative data can identify second breast cancer events with high sensitivity, specificity, and PPV. The algorithms presented here promote efficient outcomes research, allowing researchers to prioritize sensitivity, specificity, or PPV in identifying second breast cancer events.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.