BACKGROUND AND PURPOSE: Not only clinical factors, including the CHADS2 score, but also echocardiographic findings have been reported to be useful for predicting the risk of ischemic stroke in patients with nonvalvular atrial fibrillation (NVAF). However, it remains to be determined which of these factors might be more relevant for evaluation of the risk of stroke in each patient. METHODS: In 490 patients with NVAF who underwent transesophageal echocardiography (TEE), we examined the long-term incidence of ischemic stroke events (mean follow-up time, 5.7+/-3.3 years). For each patient, the predictive values of gender, the CHADS2 risk factors (congestive heart failure, hypertension, age > or =75 years, diabetes mellitus, history of cerebral ischemia), the CHADS2 score, and the findings on echocardiography, including TEE risk markers, were assessed. RESULTS: The ischemic stroke rate was significantly correlated with the CHADS2 score (p<0.05). According to the results of univariate analyses, age > or =75 years, history of cerebral ischemia, CHADS2 score > or =2, and presence of TEE risk were significantly correlated with the incidence of ischemic stroke. Cox proportional hazards regression analyses identified age > or =75 years and presence of TEE risk as significant predictors of subsequent ischemic stroke events in patients with NVAF. As compared with that in persons below 75 years of age without TEE risk, the ischemic stroke rate was significantly higher in persons who were > or =75 years of age with TEE risk (4.3 vs. 0.56%/year, adjusted hazard ratio=8.94, p<0.001). CONCLUSIONS: TEE findings might be more relevant predictors of ischemic stroke than the CHADS2 score in patients with NVAF. The stroke risk was more than 8-fold higher in patients aged > or =75 years with TEE risk.
Aged ; Atrial Fibrillation ; Brain Ischemia ; Diabetes Mellitus ; Echocardiography ; Echocardiography, Transesophageal ; Follow-Up Studies ; Heart Failure ; Humans ; Hypertension ; Incidence ; Risk Factors ; Stroke

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.

Drug dispensing is a statutory and designated duty of a pharmacist. We aimed to examine the changes in the nature of drug dispensing using a text mining method. Our corpus consisted of text documents from “Chozai Shishin”, the most standard manual for dispensing drugs in Japan, Editions 1 to 13 (Japan Pharmaceutical Association), and we used the KH Coder software for text mining. We constructed networks showing the association between frequent word co-occurrence and edition number, and co-occurrence relations for frequent words in each edition. We found that “patient” superseded “dispensing” as a frequent term over time. “Dispensing” was another frequent term with a highly centralized node in each edition. Accordingly, we targeted the term “dispensing” for network analysis to depict its co-occurrence relations. We found that the range of related words for “dispensing” broadened from “preparation” and “compounding” to include “patient adherence instructions”, “assessment”, “medical treatment”, and “information provision”. Accordingly, we concluded that the content of “dispensing”, which is a pharmacist’s duty, has expanded from the duties of “dispensing drugs” to include “responding to patients” within the definition of “dispensing”, and we were able to present this finding as objective data by using the mechanical method known as text mining.