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1.
Japanese Journal of Pharmacoepidemiology ; : 73-95, 2010.
Article in Japanese | WPRIM | ID: wpr-376020

ABSTRACT

<b>Objective:</b> The mechanism underlying the development of neuropsychiatric symptoms such as unconsciousness, abnormal behavior, delirium, hallucinations, and convulsions in influenza has not been thoroughly investigated. The relationship between drug administration and neuropsychiatric symptoms during influenza is also poorly understood. This study is the first pharmacoepidemiologic study focused on investigating the relationship between drug administration and neuropsychiatric symptoms.<br><b>Design:</b> Cohort study<br><b>Methods:</b> Study subjects were patients under 18 years old who had influenza during the 2006/07 season. We prepared two kinds of questionnaires for doctor and for patient's family, and carried out the survey between January and March, 2007. Using data from 9,389 patients, we analyzed the relationship between neuropsychiatric symptoms, such as delirium, unconsciousness and convulsion, and drug administration of acetaminophen and oseltamivir.<br><b>Results:</b> Analysis of the relationship between delirium and drug administration provided hazard ratios of 1.55(p=0.061)for acetaminophen and 1.51(p=0.084)for oseltamivir. These hazard ratios, which were adjusted for risk factors by multivariate analysis of the proportional hazard model, showed an increasing tendency of delirium after administration of each drug. In patients who received oseltamivir, a high incidence of delirium was observed between 6 and 12 hours after onset of fever. Furthermore, delirium was found to develop in a shorter time following oseltamivir use than it did after acetaminophen use. There was no relationship between unconsciousness and acetaminophen administration, as demonstrated by a hazard ratio of 1.06(p=0.839). The incidence of unconsciousness increased significantly with oseltamivir use with a hazard ratio of 1.79(p=0.0389), and unconsciousness was found to occur in a short time after oseltamivir use.<br><b>Conclusion:</b> The results obtained from this study suggest that there are increased risks of delirium and unconsciousness with drug administration. Further pharmacoepidemiologic studies for hypothesis testing are required to study the relationship between abnormal behavior and drug administration.

2.
Japanese Journal of Pharmacoepidemiology ; : 49-59, 2010.
Article in Japanese | WPRIM | ID: wpr-376018

ABSTRACT

<b>Objective:</b> To investigate quantitatively the risk factors of hyperkalaemia or increased blood potassium associated with ACE-inhibitor therapy<br><b>Design:</b> Nested case-control study<br><b>Methods:</b> We used the antihypertensive drug database(72,379 subjects)developed by the RAD-AR Council, Japan and the Institute of Statistical Mathematics based on the post-marketing surveillance(PMS) data of pharmaceutical companies. Of 37,372 subjects taking ACE-inhibitors, the case group was composed of 64 patients who experienced hyperkalaemia or blood potassium increase while taking ACE-inhibitors, and the control group was composed of 1,280 patients(20 patients per case)randomly selected from patients who did not experience hyperkalaemia or blood potassium increase while taking ACE-inhibitors. The relevant factors that can be extracted from the database were the followings: age, WHO classification of hypertension, complications, antihypertensive drugs used before the PMS survey, and concomitant drugs.<br><b>Results:</b> Among the subjects taking antihypertensive agents, 65 patients experienced hyperkalaemia orincreased blood potassium, 64(98.5%)of whom were taking ACE-inhibitors. The factors that were significantly different between two groups(p<0.05)by univariate analysis were WHO classification of hypertension(p=0.005), complications of nephritis/nephrosis(p<0.001), other disorder of urinary system(p<0.001), unclear symptom or diagnosis(p=0.005), taking diuretics as antihypertensive drugs before study(p=0.032), and concomitant treatment with diuretics(p=0.004), vasodilators(p<0.001), and antigout agents(p=0.001). Conditional multivariate logistic analysis of these factors yielded adjusted odds ratio of 21.31 for complications of nephritis/nephrosis(p<0.001), 6.83 for other disorder of urinary system(p<0.001), and 2.30 for concomitant therapy with diuretics(p=0.049).<br><b>Conclusion:</b> The risk factors of hyperkalaemia or blood potassium increase associated with taking ACE-inhibitors were nephritis/nephrosis, other disorder of urinary system and concomitant therapy with diuretics.

3.
Japanese Journal of Pharmacoepidemiology ; : 79-88, 2009.
Article in Japanese | WPRIM | ID: wpr-377940

ABSTRACT

<B>Objective</B>: Databases, such as the Medicaid recipient database in the USA and the General Practice Research Database (GPRD) in the UK, take on an important role as resources for balancing the benefits and risks of medicines in Europe and the United States.<Br>Their record sizes are several ten million and a few million each. They are actually used for epidemiological studies. However, in Japan, a database that can be used for such studies is insufficient. We attempted to create a database of pre-marketing clinical trial data for antihypertensive drugs. These data have been managed by a Controller Committee. <Br>(<B>Design</B> : not applicable )<Br><B>Methods</B>: The database is made from the data and the documents, including electronic and paper media. The creation process was as follows : computerizing documents, item-name identification, defining the integrated database, protocol review, batch processing, and logical/validation checking.<Br><B>Results</B>: The database has 13 datasets and consists of 56 trials and 12,389 subjects. Overall, 15 trials involved beta-blockers which is the largest drugs as the investigational drug, and 43 trials compared the same group of antihypertensive drugs.<Br><B>Conclusion</B>: A database that can be used for quantitative evaluation of various hypotheses has been built. It is possible to completely analyze all of the data in this large-scale database to conduct, for example, individual patient data (IPD) meta-analyses.

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