ArticleJCO clinical cancer informatics2017
Algorithm to Identify Systemic Cancer Therapy Treatment Using Structured Electronic Data.
Article in JCO clinical cancer informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 19 citations in OpenAlex.
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- Reviewing the lines of therapy-concept in cancer treatment: a survey study among physicians.BMC cancer · 2025Article
- Patterns of Medical Care Cost by Service Type Associated With Lung Cancer Screening.Medical care · 2025Article
- Treatment patterns, health care resource utilization, and costs of chimeric antigen receptor T-cell vs standard therapy for relapsed/refractory mantle cell lymphoma in the United States.Journal of managed care & specialty pharmacy · 2025Article
- Association of systemic therapy with survival among adults with advanced non-small cell lung cancer.Translational lung cancer research · 2025Article
- Concepts of lines of therapy in cancer treatment: findings from an expert interview-based study.BMC research notes · 2024Article
- Uptake of novel systemic therapy: Real world patterns among adults with advanced non-small cell lung cancer.Cancer treatment and research communications · 2023Article
- Article
- Identifying options for oncology therapy regimen codification to improve standardization-combined results of an expert panel and a review.Journal of clinical pharmacy and therapeutics · 2021Review
- Characterizing the Anticancer Treatment Trajectory and Pattern in Patients Receiving Chemotherapy for Cancer Using Harmonized Observational Databases: Retrospective Study.JMIR medical informatics · 2021Article
- Extending the OMOP Common Data Model and Standardized Vocabularies to Support Observational Cancer Research.JCO clinical cancer informatics · 2021Article
- Consolidation of Cancer Registry and Administrative Claims Data on Cancer Diagnosis and Treatment in the US Military Health System.JCO clinical cancer informatics · 2020Article
- HemOnc: A new standard vocabulary for chemotherapy regimen representation in the OMOP common data model.Journal of biomedical informatics · 2019Article
- Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data.JCO clinical cancer informatics · 2019Article
- Medical Care Costs for Recurrent versus De Novo Stage IV Cancer by Age at Diagnosis.Health services research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeWith the shift in the majority of oncology clinical care in the United States from paper records to electronic health records, researchers need efficient and validated processes to obtain accurate data about the entire treatment history of patients diagnosed with cancer. The objective of this study was to develop and validate an algorithm that is agnostic to the source of data but that can identify specific regimens in the entire course of systemic therapy treatment for patients diagnosed with breast, colorectal, or lung cancer.
methodsA cohort of patients with incident breast, colorectal, and lung cancer were randomly distributed into six groups. The algorithm was iteratively modified, and the performance was assessed until no additional modifications could be identified in the first three groups. The performance of the algorithm was confirmed in the three groups that remained.
resultsThe final model produced ranges of sensitivity between 97.2% and 100% for first-course systemic therapy across all cancers, with a false-positive rate of 0%. The algorithm matched the exact number of courses and the exact regimens of systemic therapy agents as captured by infusion, pharmacy, and procedure electronic medical record data for all courses of therapy 88% to 100% of the time.
conclusionUse of our validated algorithm that characterizes entire courses of systemic therapy treatment in patients diagnosed with breast, colorectal, and lung cancer will allow researchers in a variety of settings to conduct comparative effectiveness studies related to the uptake, safety, outcomes, and costs associated with the use of both novel and standard regimens.
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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.