Observational studyThe Journal of pediatrics2017
A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry.
Observational study in The Journal of pediatrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02249923 (Pediatric Pulmonary Hypertension Network), which is not on this map. Cited by 25 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.
Pediatric Pulmonary Hypertension Network (PPHNet) Informatics Registry
Who cites it
25 citing papers in PubMed.
- Natural language processing to build a computable phenotype library for adults with congenital heart disease.International journal of medical informatics · 2026Article
- A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.NPJ digital medicine · 2026Article
- Automated phenotyping of congenital heart disease for dynamic patient aggregation and outcome reporting.JAMIA open · 2025Article
- Integrating Health Care Data in an Informatics for Integrating Biology & the Bedside (i2b2) Model Persisted Through Elasticsearch: Design, Implementation, and Evaluation in a French University Hospital.JMIR medical informatics · 2025Article
- Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China.Frontiers in immunology · 2025Article
- Multisource representation learning for pediatric knowledge extraction from electronic health records.NPJ digital medicine · 2024Article
- Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital.JAMIA open · 2023Article
- Assessing Detection of Children With Suicide-Related Emergencies: Evaluation and Development of Computable Phenotyping Approaches.JMIR mental health · 2023Article
- The SMART Text2FHIR Pipeline.medRxiv : the preprint server for health sciences · 2023Article
- Article
- The SMART Text2FHIR Pipeline.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Development of a standards-based phenotype model for gross motor function to support learning health systems in pediatric rehabilitation.Learning health systems · 2022Article
- A high-throughput phenotyping algorithm is portable from adult to pediatric populations.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Accuracy of Asthma Computable Phenotypes to Identify Pediatric Asthma at an Academic Institution.Methods of information in medicine · 2020Article
- Claims-Based Algorithms for Identifying Patients With Pulmonary Hypertension: A Comparison of Decision Rules and Machine-Learning Approaches.Journal of the American Heart Association · 2020Article
- Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data.JAMIA open · 2020Article
- Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research.Journal of the American Society of Nephrology : JASN · 2019Article
- High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).Nature protocols · 2019Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectivesTo compare registry and electronic health record (EHR) data mining approaches for cohort ascertainment in patients with pediatric pulmonary hypertension (PH) in an effort to overcome some of the limitations of registry enrollment alone in identifying patients with particular disease phenotypes. STUDY
designThis study was a single-center retrospective analysis of EHR and registry data at Boston Children's Hospital. The local Informatics for Integrating Biology and the Bedside (i2b2) data warehouse was queried for billing codes, prescriptions, and narrative data related to pediatric PH. Computable phenotype algorithms were developed by fitting penalized logistic regression models to a physician-annotated training set. Algorithms were applied to a candidate patient cohort, and performance was evaluated using a separate set of 136 records and 179 registry patients. We compared clinical and demographic characteristics of patients identified by computable phenotype and the registry.
resultsThe computable phenotype had an area under the receiver operating characteristics curve of 90% (95% CI, 85%-95%), a positive predictive value of 85% (95% CI, 77%-93%), and identified 413 patients (an additional 231%) with pediatric PH who were not enrolled in the registry. Patients identified by the computable phenotype were clinically distinct from registry patients, with a greater prevalence of diagnoses related to perinatal distress and left heart disease.
conclusionsMining of EHRs using computable phenotypes identified a large cohort of patients not recruited using a classic registry. Fusion of EHR and registry data can improve cohort ascertainment for the study of rare diseases.
trial registrationClinicalTrials.gov: NCT02249923.
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