ArticleJournal of the American Medical Informatics Association : JAMIA2013
A comparison of phenotype definitions for diabetes mellitus.
Article in Journal of the American Medical Informatics Association : JAMIA, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 3 of them syntheses that pooled it.
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
80 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Characteristics of diabetes mellitus patients in Turkey: An analysis of national electronic health records.Turkish journal of medical sciences · 2023Pooled it
- Advancing Alzheimer's research: A review of big data promises.International journal of medical informatics · 2017Pooled it
- Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2017Pooled it
- Subtypes of newly diagnosed type 2 diabetes and risk of complications: analysis of electronic health records in the USA.Diabetologia · 2026Article
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Unsupervised Coverage Sampling to Enhance Clinical Chart Review Coverage for Computable Phenotype Development: Simulation and Empirical Study.JMIR medical informatics · 2025Article
- Equivalence of Type 2 Diabetes Prevalence Estimates: Comparative Study of Similar Phenotyping Algorithms Using Electronic Health Record Data.JMIR public health and surveillance · 2025Article
- Comparing computable type 2 diabetes phenotype definitions in identifying populations of interest for clinical research.BMJ health & care informatics · 2025Article
- Differential Effect of GLP-1 Receptor Agonists and SGLT2 Inhibitors on Lower-Extremity Amputation Outcomes in Type 2 Diabetes: A Nationwide Retrospective Cohort Study.Diabetes care · 2025Article
- Assessing the Impact of Computable Type 2 Diabetes Phenotypes on Predicting Healthcare Utilization Using Electronic Health Records and Administrative Claims.Healthcare (Basel, Switzerland) · 2025Article
- Implications of the choice of method to identify major depressive disorder in large research cohorts.Journal of mood and anxiety disorders · 2025Article
- An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions.BMC medical research methodology · 2025Article
- Migraine is associated with a higher risk of ischemic and hemorrhagic stroke: an analysis of theFrontiers in pain research (Lausanne, Switzerland) · 2025Article
- A Standardized Guideline for Assessing Extracted Electronic Health Records Cohorts: A Scoping Review.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Assessing the impact of social determinants of health on diabetes severity and management.JAMIA open · 2024Article
- Associations of the Philadelphia sweetened beverage tax with changes in adult body weight: an interrupted time series analysis.Lancet regional health. Americas · 2024Article
- Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study.JMIR medical informatics · 2024Article
- Article
- A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.Journal of biomedical informatics · 2023Article
- Computational phenotyping with the All of Us Research Program: identifying underrepresented people with HIV or at risk of HIV.JAMIA open · 2023Article
20 more citing papers are in PubMed but not listed here.
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study compares the yield and characteristics of diabetes cohorts identified using heterogeneous phenotype definitions. MATERIALS AND
methodsInclusion criteria from seven diabetes phenotype definitions were translated into query algorithms and applied to a population (n=173 503) of adult patients from Duke University Health System. The numbers of patients meeting criteria for each definition and component (diagnosis, diabetes-associated medications, and laboratory results) were compared.
resultsThree phenotype definitions based heavily on ICD-9-CM codes identified 9-11% of the patient population. A broad definition for the Durham Diabetes Coalition included additional criteria and identified 13%. The electronic medical records and genomics, NYC A1c Registry, and diabetes-associated medications definitions, which have restricted or no ICD-9-CM criteria, identified the smallest proportions of patients (7%). The demographic characteristics for all seven phenotype definitions were similar (56-57% women, mean age range 56-57 years).The NYC A1c Registry definition had higher average patient encounters (54) than the other definitions (range 44-48) and the reference population (20) over the 5-year observation period. The concordance between populations returned by different phenotype definitions ranged from 50 to 86%. Overall, more patients met ICD-9-CM and laboratory criteria than medication criteria, but the number of patients that met abnormal laboratory criteria exclusively was greater than the numbers meeting diagnostic or medication data exclusively. DISCUSSION: Differences across phenotype definitions can potentially affect their application in healthcare organizations and the subsequent interpretation of data.
conclusionsFurther research focused on defining the clinical characteristics of standard diabetes cohorts is important to identify appropriate phenotype definitions for health, policy, and research.
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.