ABSTRACT
<p><b>OBJECTIVE</b>To evaluate the incidence of Ketoconazole associated hepatotoxicity and related factor.</p><p><b>METHODS</b>Literature retrieval was conducted by using multi-databases for meta-analysis on Ketoconazole associated hepatotoxicity. The data were collected with a standardized form. Overall estimation of incidence of hepatotoxicity for specific study type was calculated by using a DerSimonian-Laird random-effects model owing to the substantial differences among the studies.</p><p><b>RESULTS</b>Totally 204 eligible studies were included in the analysis. The incidence of Ketoconazole associated hepatotoxicity was 3.6%-4.2%. The dosage and duration specific subgroup analyses did not show any significant difference among groups, while the age specific subgroup analysis showed the incidence in children and people aged >60 years was 1.4% (95% CI: 0.5%-4.2%) and 3.2% (95% CI: 1.1%-8.7%) respectively. Additionally, the incidence of the hepatotoxicity was higher in people who had oral administration of ketoconazole beyond the provisions of the usage instructions, and the incidence was 5.7% (95% CI: 4.5%-7.2%).</p><p><b>CONCLUSION</b>Ketoconazole associated hepatotoxicity was common. Off-label use might increase the risk of liver damage. Well-designed large sample studies are needed to identify the risk factors in future.</p>
Subject(s)
Humans , Antifungal Agents , Chemical and Drug Induced Liver Injury , Ketoconazole , Off-Label UseABSTRACT
Objective This study aimed to provide an epidemiological modeling in evaluating the risk of developing obesity within 5 years in Taiwan population aged 30-59 years.Methods After excluding 918 individuals who were obesitive at baseline,a cohort of 14 167 non-obesity subjects aged 30-59 years in the initial year during 1998-2006,was formed to derive a Risk Score which could predict the incident obesity (IO).Multivariate logistic regression was used to derive the risk functions,using the check-up center (Taipei training cohort,n=8104) of the overall cohort.Rules based on these risk functions were evaluated in the left three centers (testing cohort,n=6063).Risk functions were produced to detect the IO on a training sample using the multivariate logistic regression models.Starting with variables that could predict the IO through univariate models,we constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables.We evaluated the predictability of the model by the area under the receiver-operating characteristic (ROC) curve (AUC) and to testify its diagnostic property on the testing sample.Once the final model was defined,the next step was to establish rules to characterize 4different degrees of risk based on the cut points of these probabilities after transforming into normal distribution by log-transformation.Results At baseline,the range of the proportion of normal weight,overweight and obesity were 50.00％ 60.00％,26.47％-31.11％ and 5.76％-7.24％ respectively in tour check-up centers of Taiwan.After excluding 918 obesity individuals at baseline,we ascertained 386 (2.73％,386/14 167) cases having IO and 2.66％-2.91％ of them having centered obesity in the four check-up centers respectivcly.Final multivariable logistic regression model would include five risk lactors:sex,age,history of diabetes,weight deduction ≥4 kg within 3 months and waist circumference.The area under the ROC curve (AUC) was 0.898 (95％CI,0.884-0.912) that could predict the development of obesity within 5 years.The curve also had adequate performance in testing the sample [AUC=0.881 (95％CI,0.862 0.900) ].After labeling the four risk degrees,16.0％ and 2.9％ of the total subjects were in the mediate and high risk populations respectively and were 7.8 and 16.6 times higher,when comparing with the population at risk in general.Conclusion The predictability and reliability of our obesity risk score model,derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database,were relatively satisfactory,with its simple and practicable predictive variables and the risk degree form.This model could help individuals to self assess the situation of risk on obesity and could also guide the community caretakers to monitor the trend of obesity development.