Trial reportPediatric blood & cancer2019
Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data.
Trial report in Pediatric blood & cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
18 citing papers in PubMed, 32 citations in OpenAlex.
- Identifying Hemophagocytic Lymphohistiocytosis and Describing Outcomes Using Computable Phenotypes: Retrospective Cohort Study.JMIR cancer · 2026Article
- Leveraging the PEDSnet clinical research network and electronic health record data to enhance efficiency of trial enrollment for a rare pediatric rheumatic disease.Pediatric rheumatology online journal · 2026Article
- Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.JCO clinical cancer informatics · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Development and validation of a computable phenotype for Turner syndrome utilizing electronic health records from a national pediatric network.American journal of medical genetics. Part A · 2024Article
- Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.Frontiers in immunology · 2024Article
- Electronic health records identify timely trends in childhood mental health conditions.Child and adolescent psychiatry and mental health · 2023Article
- Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.JMIR mental health · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Implementation science in pediatric oncology: A narrative review and future directions.Pediatric blood & cancer · 2022Review
- Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.Journal of biomedical informatics · 2022Article
- Positive predictive value and sensitivity of ICD-9-CM codes for identifying pediatric leukemia.Pediatric blood & cancer · 2022Article
- Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database.Informatics in medicine unlocked · 2022Article
- Clinical data for paediatric research: the Swiss approach : Proceedings of the National Symposium in Bern, Switzerland, Dec 5-6, 2019.BMC proceedings · 2021Article
- Clinical comparison between trial participants and potentially eligible patients using electronic health record data: A generalizability assessment method.Journal of biomedical informatics · 2021Article
- The effects of solution-focused nursing on leukemia chemotherapy patients' moods, cancer-related fatigue, coping styles, self-efficacy, and quality of life.American journal of translational research · 2021Article
- Using big data in pediatric oncology: Current applications and future directions.Seminars in oncology · 2020Review
- Big Data for Nutrition Research in Pediatric Oncology: Current State and Framework for Advancement.Journal of the National Cancer Institute. Monographs · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundWidespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies.
methodsThis study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters.
resultsThe computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99.
conclusionThe EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.
Indexed as
Identifiers
What OpenQuestion holds
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.