ArticleJournal of the National Cancer Institute2016
Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.
Article in Journal of the National Cancer Institute, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline 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
- External validation of a data-driven algorithm to identify breast cancer recurrences from administrative healthcare data.Breast (Edinburgh, Scotland) · 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
- Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.JCO clinical cancer informatics · 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
- Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation.Current oncology (Toronto, Ont.) · 2022Article
- Observational
- New method for determining breast cancer recurrence-free survival using routinely collected real-world health data.BMC cancer · 2022Article
- Validation of an Algorithm to Ascertain Late Breast Cancer Recurrence Using Danish Medical Registries.Clinical epidemiology · 2020Article
- Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data.BMC cancer · 2019Article
- Development, Validation, and Dissemination of a Breast Cancer Recurrence Detection and Timing Informatics Algorithm.Journal of the National Cancer Institute · 2018Article
- Use of Antihypertensive Medications and Risk of Adverse Breast Cancer Outcomes in a SEER-Medicare Population.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017Article
- Association of Serum Level of Vitamin D at Diagnosis With Breast Cancer Survival: A Case-Cohort Analysis in the Pathways Study.JAMA oncology · 2017Article
- Use of administrative data to increase the practicality of clinical trials: Insights from the Women's Health Initiative.Clinical trials (London, England) · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification.
methodsWe evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation.
resultsThe SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences.
conclusionsUse of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs.
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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.