ArticleBMC cancer2019
Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled 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
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
- Identification of recurrences in women diagnosed with early invasive breast cancer using routinely collected data in England.BJC reports · 2025Article
- Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Treatment Patterns and Health Outcomes among Patients with HER2 IHC0/-Low Metastatic or Recurrent Breast Cancer.Cancers · 2024Article
- Estimated incidence of disruptions to event-free survival from non-metastatic cancers in New South Wales, Australia - a population-wide epidemiological study of linked cancer registry and treatment data.Frontiers in oncology · 2024Article
- Racial Disparities in the Ascertainment of Cancer Recurrence in Electronic Health Records.JCO clinical cancer informatics · 2023Article
- Understanding Real-World Treatment Patterns and Clinical Outcomes among Metastatic Melanoma Patients in Alberta, Canada.Current oncology (Toronto, Ont.) · 2023Article
- Plasma hPGBMC cancer · 2023Article
- Understanding Characteristics, Treatment Patterns, and Clinical Outcomes for Individuals with Advanced or Recurrent Endometrial Cancer in Alberta, Canada: A Retrospective, Population-Based Cohort Study.Current oncology (Toronto, Ont.) · 2023Article
- Real-world treatment patterns, clinical outcomes, and health care resource utilization in advanced unresectable hepatocellular carcinoma.Canadian liver journal · 2022Article
- 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
- Examining the Landscape of Prognostic Factors and Clinical Outcomes for Cancer Control.Current oncology (Toronto, Ont.) · 2021Article
- Broad-Spectrum Antibiotic Use and Disease Progression in Early-Stage Melanoma Patients: A Retrospective Cohort Study.Cancers · 2021Article
- Real-World Treatment Patterns, Clinical Outcomes, and Health Care Resource Utilization in Extensive-Stage Small Cell Lung Cancer in Canada.Current oncology (Toronto, Ont.) · 2021Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundRecurrence is not explicitly documented in cancer registry data that are widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, and receipt of subsequent chemotherapy or radiation may indicate recurrence. This study aimed to develop and validate algorithms for identifying breast cancer recurrence using routinely collected administrative data.
methodsThe study cohort included all young (≤ 40 years) breast cancer patients (2007-2010), and all patients receiving neoadjuvant chemotherapy (2012-2014) in Alberta, Canada. Health events (including mastectomy, chemotherapy, radiation, biopsy and specialist visits) were obtained from provincial administrative data. The algorithms were developed using classification and regression tree (CART) models and validated against primary chart review.
resultsAmong 598 patients, 121 (20.2%) had recurrence after a median follow-up of 4 years. The high sensitivity algorithm achieved 94.2% (95% CI: 90.1-98.4%) sensitivity, 93.7% (91.5-95.9%) specificity, 79.2% (72.5-85.8%) positive predictive value (PPV), and 98.5% (97.3-99.6%) negative predictive value (NPV). The high PPV algorithm had 75.2% (67.5-82.9%) sensitivity, 98.3% (97.2-99.5%) specificity, 91.9% (86.6-97.3%) PPV, and 94% (91.9-96.1%) NPV. Combining high PPV and high sensitivity algorithms with additional (7.5%) chart review to resolve discordant cases resulted in 94.2% (90.1-98.4%) sensitivity, 98.3% (97.2-99.5%) specificity, 93.4% (89.1-97.8%) PPV, and 98.5% (97.4-99.6%) NPV.
conclusionThe proposed algorithms based on routinely collected administrative data achieved favorably high validity for identifying breast cancer recurrences in a universal healthcare system in Canada.
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Registered trials
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.