ArticleHealth services research2011
Development and validation of a predictive algorithm to identify adult asthmatics from medical services and pharmacy claims databases.
Article in Health services research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Trial
- Identifying diseases in claims data using a machine learning approach - a case from Switzerland.Archives of public health = Archives belges de sante publique · 2025Article
- Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.PloS one · 2021Article
- Predictors of perceived asthma control among patients managed in primary care clinics.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2015Article
- Using decision support for population tracking of adherence to recommended asthma guidelines.BMJ open · 2014Article
- Development of an electronic medical record-based algorithm to identify patients with unknown HIV status.AIDS care · 2014Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveTo develop and validate the accuracy of a predictive model to identify adult asthmatics from administrative health care databases. STUDY
settingAn existing electronic medical record project in Montreal, Quebec. STUDY
designOne thousand four hundred and thirty-one patients with confirmed asthma status were identified from primary care physician's electronic medical record. DATA COLLECTION/EXTRACTION
methodsTherapeutic indication of asthma in an electronic prescription and/or confirmed asthma from an automated problem list were used as the gold standard. Five groups of asthma-specific markers were identified from administrative health care databases to estimate the probability of the presence of asthma. Cross-validation evaluated the diagnostic ability of each predictive model using 50 percent of sample. PRINCIPAL
findingsThe best performance in discriminating between the patients with asthma and those without it included indicators from medical service and prescription claims databases. The best-fitting algorithm had a sensitivity of 70 percent, a specificity of 94 percent, and positive predictive value of 65 percent. The prescriptions claims-specific algorithm demonstrated a nearly equal performance to the model with medical services and prescription claims combined.
conclusionsOur algorithm using asthma-specific markers from administrative claims databases provided moderate sensitivity and high specificity.
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Registered trials
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