Dataset Information

A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.

ABSTRACT: Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose-response risk model. The dose represents the number of drug combinations. Using an empirical Bayes estimation method, we successfully identified high-dimensional (two to six) drug combinations that are associated with excessive myopathy risk at significantly low local false-discovery rates. From the curve of a dose-response model and high-dimensional drug interaction data, we observed that myopathy risk increases as the drug interaction dimension increases. This is the first time that such a dose-response relationship for high-dimensional drug interactions was observed and extracted from the medical record database.

SUBMITTER: Zhang P

PROVIDER: S-EPMC4562163 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.

Zhang P P Du L L Wang L L Liu M M Cheng L L Chiang C-W CW Wu H-Y HY Quinney S K SK Shen L L Li L L

CPT: pharmacometrics & systems pharmacology 20150706 8

Interactions between multiple drugs may yield excessive risk of adverse effects. This increased risk is not uniform for all combinations, although some combinations may have constant adverse effect risks. We developed a statistical model using medical record data to identify drug combinations that induce myopathy risk. Such combinations are revealed using a novel mixture model, comprised of a constant risk model and a dose-response risk model. The dose represents the number of drug combinations. ...[more]

PMID: 26380156

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Project description:BackgroundAsthma exacerbation leading to emergency department (ED) visit is prevalent, an indicator of poor control of asthma, and is a potentially preventable clinical outcome.ObjectiveWe propose to utilize multiple data elements available in electronic medical records (EMRs) and claims database to create separate algorithms with high validity for clinical and research purposes to identify asthma exacerbation-related ED visit among the general population.MethodsWe performed a retrospective study with inclusion criteria of patients aged 4 to 40 years, a visit to Geisinger ED from January 1, 2006, to October 28, 2013, with asthma on their problem list. Different electronic data elements including chief complaints, vitals, season, smoking, medication use, and discharge diagnoses were obtained to create the algorithm. A stratified random sample was generated to select the charts for review. Chart review was performed to classify patients with asthma-related ED visit, that is, the gold standard. Two reviewers performed the chart review and validation was done on a small subset.ResultsThere were 966 eligible ED visits in the EMR sample and 731 in the claims sample. Agreement between reviewers was 95.45% and kappa statistic was 0.91. Mean age of the EMR sample was 22 years, and mostly white (93%). Multiple models conventionally used in studies were evaluated and the final model chosen included principal diagnosis, bronchodilator, and steroid use for both algorithms, chief complaints for EMR, and secondary diagnosis for claims. Area under the curve was 0.93 (95% confidence interval: 0.91-0.94) and 0.94 (0.93-0.96), respectively, for EMR and claims data, with positive predictive value of > 94%. The algorithms are visually presented using nomograms.ConclusionWe were able to develop two separate algorithms for EMR and claims to identify asthma exacerbation-related ED visit with excellent diagnostic ability and varying discrimination threshold for clinical and research purposes.

Dataset Information

A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.

Publications

A Mixture Dose-Response Model for Identifying High-Dimensional Drug Interaction Effects on Myopathy Using Electronic Medical Record Databases.

Similar Datasets

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