ArticleHealth services research2016
Development and Validation of HealthImpact: An Incident Diabetes Prediction Model Based on Administrative Data.
Article in Health services research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Diabetes Life Expectancy Prediction Model Inputs and Results From Patient Surveys Compared With Electronic Health Record Abstraction: Survey Study.JMIR aging · 2023Article
- Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.International journal of endocrinology and metabolism · 2021Article
- Deep neural network models for identifying incident dementia using claims and EHR datasets.PloS one · 2020Article
- Identifying incident dementia by applying machine learning to a very large administrative claims dataset.PloS one · 2019Article
- Evaluation of the HealthImpact Diabetes Risk Model in the Veterans Health Administration.Journal of managed care & specialty pharmacy · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and validate a model of incident type 2 diabetes based solely on administrative data. DATA SOURCES/STUDY
settingOptum Labs Data Warehouse (OLDW), a national commercial administrative dataset. STUDY
designHealthImpact model was developed and internally validated using nested case-control study design; n = 473,049 in training cohort and n = 303,025 in internal validation cohort. HealthImpact was externally validated in 2,000,000 adults followed prospectively for 3 years. Only adults ≥18 years were included. DATA COLLECTION/EXTRACTION
methodsPatients with incident diabetes were identified using HEDIS rules. Control subjects were sampled from patients without diabetes. Medical and pharmacy claims data collected over 3 years prior to index date were used to build the model variables. PRINCIPAL
findingsHealthImpact, scored 0-100, has 48 variables with c-statistic 0.80815. We identified HealthImpact threshold of 90 as identifying patients at high risk of incident diabetes. HealthImpact had excellent discrimination in external validation cohort (c-statistic 0.8171). The sensitivity, specificity, positive predictive value, and negative predictive value of HealthImpact >90 for new diagnosis of diabetes within 3 years were 32.35, 94.92, 22.25, and 96.90 percent, respectively.
conclusionsHealthImpact is an efficient and effective method of risk stratification for incident diabetes that is not predicated on patient-provided information or laboratory tests.
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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.