31 research outputs found
Mining directional drug interaction effects on myopathy using the FAERS database
Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization
Mining and visualizing high-order directional drug interaction effects using the FAERS database
Background: Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs.
Methods: We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset.
Results: Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs.
Conclusions: We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care
Translational high-dimesional drug interaction discovery and validation using health record databases and pharmacokinetics models
Indiana University-Purdue University Indianapolis (IUPUI)Polypharmacy leads to increased risk of drug-drug interactions (DDI’s). In this
dissertation, we create a database for quantifying fraction of metabolism (fm) of CYP450
isozymes for FDA approved drugs. A reproducible data collection protocol was
developed to extract key information from publicly available in vitro selective CYP
enzyme inhibition studies. The fm was then estimated from the curated data. Then,
proposed a random control selection approach for nested case-control design for
electronical health records (HER) and electronical medical records (EMR) databases. By
relaxing the matching by case’s index time restriction, random control dramatically
reduces the computational burden compared with traditional control selection
approaches. Using the Observational Medical Outcomes Partnership gold standard and
an EMR database, random control is demonstrated to have better performances as well.
Finally, combining epidemiological studies and pharmacokinetic modeling with fm
database, we detected and evaluated high-dimensional drug-drug interactions among
thirty high frequency drugs. Multi-drug combinations that increased risk of myopathy
were identified in the FAERS and EMR databases by a mixture drug-count response
model (MDCM) model. Twenty-eight 3-way and 43 4-way DDI’s increased ratio of area
under plasma concentration–time curve (AUCR) >2-fold and had significant myopathy
risk in both databases. The predicted AUCR of omeprazole in the presence of
fluconazole and clonidine was 9.35; and increased risk of myopathy was 6.41 (LFDR = 0.002) in FAERS and 18.46 (LFDR = 0.005) in EMR. We demonstrate that combining
health record informatics and pharmacokinetic modeling is a powerful translational
approach to detect high-dimensional DDI’s.2 year
Mixture drug-count response model for the high-dimensional drug combinatory effect on myopathy
Drug-drug interactions (DDIs) are a common cause of adverse drug events (ADEs). The electronic medical record (EMR) database and the FDA's adverse event reporting system (FAERS) database are the major data sources for mining and testing the ADE associated DDI signals. Most DDI data mining methods focus on pair-wise drug interactions, and methods to detect high-dimensional DDIs in medical databases are lacking. In this paper, we propose 2 novel mixture drug-count response models for detecting high-dimensional drug combinations that induce myopathy. The “count” indicates the number of drugs in a combination. One model is called fixed probability mixture drug-count response model with a maximum risk threshold (FMDRM-MRT). The other model is called count-dependent probability mixture drug-count response model with a maximum risk threshold (CMDRM-MRT), in which the mixture probability is count dependent. Compared with the previous mixture drug-count response model (MDRM) developed by our group, these 2 new models show a better likelihood in detecting high-dimensional drug combinatory effects on myopathy. CMDRM-MRT identified and validated (54; 374; 637; 442; 131) 2-way to 6-way drug interactions, respectively, which induce myopathy in both EMR and FAERS databases. We further demonstrate FAERS data capture much higher maximum myopathy risk than EMR data do. The consistency of 2 mixture models' parameters and local false discovery rate estimates are evaluated through statistical simulation studies
Study designs and statistical methods for pharmacogenomics and drug interaction studies
Indiana University-Purdue University Indianapolis (IUPUI)Adverse drug events (ADEs) are injuries resulting from drug-related medical
interventions. ADEs can be either induced by a single drug or a drug-drug interaction (DDI).
In order to prevent unnecessary ADEs, many regulatory agencies in public health maintain
pharmacovigilance databases for detecting novel drug-ADE associations. However,
pharmacovigilance databases usually contain a significant portion of false associations due
to their nature structure (i.e. false drug-ADE associations caused by co-medications).
Besides pharmacovigilance studies, the risks of ADEs can be minimized by understating
their mechanisms, which include abnormal pharmacokinetics/pharmacodynamics due to
genetic factors and synergistic effects between drugs. During the past decade,
pharmacogenomics studies have successfully identified several predictive markers to
reduce ADE risks. While, pharmacogenomics studies are usually limited by the sample
size and budget.
In this dissertation, we develop statistical methods for pharmacovigilance and
pharmacogenomics studies. Firstly, we propose an empirical Bayes mixture model to
identify significant drug-ADE associations. The proposed approach can be used for both
signal generation and ranking. Following this approach, the portion of false associations
from the detected signals can be well controlled. Secondly, we propose a mixture dose
response model to investigate the functional relationship between increased dimensionality
of drug combinations and the ADE risks. Moreover, this approach can be used to identify high-dimensional drug combinations that are associated with escalated ADE risks at a
significantly low local false discovery rates. Finally, we proposed a cost-efficient design
for pharmacogenomics studies. In order to pursue a further cost-efficiency, the proposed
design involves both DNA pooling and two-stage design approach. Compared to traditional
design, the cost under the proposed design will be reduced dramatically with an acceptable
compromise on statistical power. The proposed methods are examined by extensive
simulation studies. Furthermore, the proposed methods to analyze pharmacovigilance
databases are applied to the FDA’s Adverse Reporting System database and a local
electronic medical record (EMR) database. For different scenarios of pharmacogenomics
study, optimized designs to detect a functioning rare allele are given as well
Possibility of Multiple Drug-Drug Interactions in Patients Treated with Statins: Analysis of Data from the Japanese Adverse Drug Event Report (JADER) Database and Verification by Animal Experiments
Adverse drug events due to drug-drug interactions can be prevented by avoiding concomitant use of causative drugs; therefore, it is important to understand drug combinations that cause drug-drug interactions. Although many attempts to identify drug-drug interactions from real-world databases such as spontaneous reporting systems have been performed, little is known about drug-drug interactions caused by three or more drugs in polypharmacy, i.e., multiple drug-drug interactions. Therefore, we attempted to detect multiple drug-drug interactions using decision tree analysis using the Japanese Adverse Drug Event Report (JADER) database, a Japanese spontaneous reporting system. First, we used decision tree analysis to detect drug combinations that increase the risk of rhabdomyolysis in cases registered in the JADER database that used six statins. Next, the risk of three or more drug combinations that significantly increased the risk of rhabdomyolysis was validated with in vivo experiments in rats. The analysis identified a multiple drug-drug interaction signal only for pitavastatin. The reporting rate of rhabdomyolysis for pitavastatin in the JADER database was 0.09, and it increased to 0.16 in combination with allopurinol. Furthermore, the rate was even higher (0.40) in combination with valsartan. Additionally, necrosis of leg muscles was observed in some rats simultaneously treated with these three drugs, and their creatine kinase and myoglobin levels were elevated. The combination of pitavastatin, allopurinol, and valsartan should be treated with caution as a multiple drug-drug interaction. Since multiple drug-drug interactions were detected with decision tree analysis and the increased risk was verified in animal experiments, decision tree analysis is considered to be an effective method for detecting multiple drug-drug interactions.This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions
Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data
INTRODUCTION: Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE: This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS: First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS: We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS: ARM of claims data may be effective in the early detection of a wide range of ADR signals
Estimating causal log-odds ratio using the case-control sample and its application in the pharmaco-epidemiology study
One important goal in pharmaco-epidemiology studies is to understand the causal relationship between drug exposures and their clinical outcomes, including adverse drug events. In order to achieve this goal, however, we need to resolve several challenges. Most of pharmaco-epidemiology data are observational and confounding is largely present due to many co-medications. The pharmaco-epidemiology study data set is often sampled from large medical record databases using a matched case-control design, and it may not be representative of the original patient population in the medical record databases. Data analysis method needs to handle a large sample size that cannot be handled using existing statistical analysis packages. In this paper, we tackle these challenges both methodologically and computationally. We propose a conditional causal log-odds ratio (OR) definition to characterize causal effects of drug exposures on a binary adverse drug event adjusting for individual level confounders. Using a case-control design, we present a propensity score estimation using only case samples and we provide sufficient conditions for the consistency of the estimation of the causal log-odds ratio using case-based propensity scores. Computationally, we implement a principle component analysis to reduce high-dimensional confounders. Extensive simulation studies are performed to demonstrate superior performance of our method to existing methods. Finally, we apply the proposed method to analyze drug-induced myopathy data sampled from a de-identified subset of medical record database (close to 5 million patient records), The Indiana Network for Patient Care. Our method identified 70 drug-induced myopathy (p < 0.05) out 72 drugs, which have myoathy side effects on their FDA drug labels. These 70 drugs include three statins who are known for their myopathy side effects
A longitudinal analysis for the identification of the factors that affect the case mix index of hospitals in the U.S
Indiana University-Purdue University Indianapolis (IUPUI)The present thesis is an analysis of longitudinal data collected through the years 2011-2013, from a complex of four hospitals located in Indiana, USA. The aim of the analysis was the detection of changes (especially a decline) in the disease related group (DRG) weights (and thus, the case mix index (CMI)), and the determination of the predictors that significantly affect these changes.
The document is divided in four major parts. In the first part it is described the statistical theory required for the the analysis, in the second part the reimbursement strategies for the hospitals in the USA, are briefly described and the concept of the DRG and CMI are explained. In the third part the actual analysis is presented while the last part contains a summary of the findings and some conclusions.
The correlation between the observations was taken into account by modeling the data using linearmixed models (LMM). Three major factors were studied for their effect on the DRG weight of thehospitals: the changes in the type of cases (i.e. the product lines), the changes in the number of the Surgical cases, and also the changes of the length of stay (LOS). The analysis did not indicate any significant DRG change in any of the hospitals except from the H4. The H4 hospital has a significant decline over time regarding the Cardio-vascular (CV) DRG weights. For the hospitals H1, H2 and H3 the only decline observed in the product lines was that for the Medical-Surgical DRG. Finally, no significant change was observed for the LOS, or the number of Surgical cases.
In addition to the three predictors studied, changes in the coding system, the documentation etc. may also affect the DRG and CMI. However, these changes are not possible to be detected through this analysis, since no available information was given in the present data