ArticleBritish journal of haematology2017
Using electronic medical record data to report laboratory adverse events.
Article in British journal of haematology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Bortezomib with standard chemotherapy for children with acute myeloid leukemia does not improve treatment outcomes: a report from the Children's Oncology Group.Haematologica · 2020Trial
- Racial, Ethnic, and Neighborhood Disparities in Acute Kidney Injury in Pediatric Acute Leukemia.Cancer medicine · 2026Article
- Article
- Automated Electronic Health Record Data Extraction and Curation Using ExtractEHR.JCO clinical cancer informatics · 2024Article
- Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module.JCO clinical cancer informatics · 2024Article
- Acute and chronic kidney injury during therapy for pediatric acute leukemia: A report from the Leukemia Electronic Abstraction of Records Network (LEARN).Pediatric blood & cancer · 2023Article
- Article
- Children's Oncology Group's 2023 blueprint for research: Cancer control and supportive care.Pediatric blood & cancer · 2023Article
- Informatics tools to implement late cardiovascular risk prediction modeling for population management of high-risk childhood cancer survivors.Pediatric blood & cancer · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends.Healthcare (Basel, Switzerland) · 2023Review
- Improving infectious adverse event reporting for children and adolescents enrolled in clinical trials for acute lymphoblastic leukemia: A report from the Children's Oncology Group.Pediatric blood & cancer · 2022Article
- Rates of laboratory adverse events by course in paediatric leukaemia ascertained with automated electronic health record extraction: a retrospective cohort study from the Children's Oncology Group.The Lancet. Haematology · 2022Article
- Automated Ascertainment of Typhlitis From the Electronic Health Record.JCO clinical cancer informatics · 2022Article
- Incidence and risk factors for hypoglycemia during maintenance chemotherapy in pediatric acute lymphoblastic leukemia.Pediatric blood & cancer · 2022Article
- A report from the Leukemia Electronic Abstraction of Records Network on risk of hepatotoxicity during pediatric acute lymphoblastic leukemia treatment.Haematologica · 2022Article
- Evolution of Hematology Clinical Trial Adverse Event Reporting to Improve Care Delivery.Current hematologic malignancy reports · 2021Review
- Improving symptom control and reducing toxicities for pediatric patients with hematological malignancies.Hematology. American Society of Hematology. Education Program · 2020Review
- Artificial intelligence (AI) and big data in cancer and precision oncology.Computational and structural biotechnology journal · 2020Review
- Future Directions of Pharmacovigilance Studies Using Electronic Medical Recording and Human Genetic Databases.Toxicological research · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Despite the importance of adverse event (AE) reporting, AEs are under-reported on clinical trials. We hypothesized that electronic medical record (EMR) data can ascertain laboratory-based AEs more accurately than those ascertained manually. EMR data on 12 AEs for patients enrolled on two Children's Oncology Group (COG) trials at one institution were extracted, processed and graded. When compared to gold standard chart data, COG AE report sensitivity and positive predictive values (PPV) were 0-21·1% and 20-100%, respectively. EMR sensitivity and PPV were >98·2% for all AEs. These results demonstrate that EMR-based AE ascertainment and grading substantially improves laboratory AE reporting accuracy.
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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.