The aim of this study was to evaluate the concordance between claims records in the National Health Insurance Research Database and patient self-reports on clinical diagnoses, medication use, and health system utilization. In this study, we used the data of 15,574 participants collected from the 2005 Taiwan National Health Interview Survey. We assessed positive agreement, negative agreement, and Cohen's kappa statistics to examine the concordance between claims records and patient self-reports. Kappa values were 0.43, 0.64, and 0.61 for clinical diagnoses, medication use, and health system utilization, respectively. Using a strict algorithm to identify the clinical diagnoses recorded in claims records could improve the negative agreement; however, the effect on positive agreement and kappa was diverse across various conditions. We found that the overall concordance between claims records in the National Health Insurance Research Database and patient self-reports in the Taiwan National Health Interview Survey was moderate for clinical diagnosis and substantial for both medication use and health system utilization.

Background: Antipsychotics have been linked to prolongation of the QT interval. However, little is known about the risk of ventricular arrhythmia (VA) and/or sudden cardiac death (SCD) associated with individual antipsychotic drug use. This study was designed to investigate the association between specific antipsychotic drugs and the risk of VA and/or SCD . Methods and Results: We conducted a case‐crossover study using a nation‐wide population‐based sample obtained from Taiwan's National Health Insurance Research Database. A total of 17 718 patients with incident VA and/or SCD were enrolled. Conditional logistic regression models were applied to examine the effects of antipsychotic drug use on the risk of VA / SCD during various case and control time windows of 7, 14, and 28 days. The effect of the potency of a human ether‐à‐go‐go‐related gene ( hERG) potassium channel blockade was also assessed. Antipsychotic drug use was associated with a 1.53‐fold increased risk of VA and/or SCD. Antipsychotic drugs with increased risk included clothiapine, haloperidol, prochlorperazine, thioridazine, olanzapine, quetiapine, risperidone, and sulpiride. The association was significantly higher among those with short‐term use. Antipsychotics with a high potency of the hERG potassium channel blockade had the highest risk of VA and/or SCD. Conclusion: Use of antipsychotic drugs is associated with an increased risk of VA and/or SCD. Careful evaluations of the risks and benefits of antipsychotic treatment are highly recommended.

The antidepressant escitalopram (S-CIT) is metabolized by the cytochrome-P450 (CYP) enzymes CYP 2D6, 2C19 and 3A4. This study evaluated the impact of CYP2D6, 2C19 and 3A4 genetic polymorphisms on plasma concentrations of S-CIT and patient treatment response. A total of 100 patients diagnosed with major depressive disorder were recruited to the study and their depression symptoms were assessed using the Hamilton Depression Rating Scale. The genetic polymorphisms *4, *5 and *10 on CYP2D6, *2, *3 and *17 on CYP2C19, and *18 on CYP3A4 were selected based on their function and respective allele frequencies in Asian populations. Polymorphisms were analyzed using the SNPstream genotyping system, PCR and direct sequencing methods. The steady-state serum concentrations of S-CIT and its metabolites S-desmethylcitalopram and S-didesmethylcitalopram were analyzed by HPLC. According to semiquantitative gene dose (SGD) and gene dose (GD) models for allele combinations of these polymorphisms, CYP2D6 was clustered into intermediate (0.5, 1 and 1.5 SGD) and extensive (2 SGD) metabolizers, while CYP2C19 was clustered into poor (0 GD) and extensive (1 and 2 GDs) metabolizers. The group of patients with intermediate CYP2D6 metabolism (0.5 SGD) had a significantly higher frequency of remitters from major depressive disorder during the 8-week treatment (p = 0.0001). Furthermore, CYP2C19 poor metabolizers had significantly higher S-CIT serum levels than did extensive metabolizers at weeks 2, 4 and 8 (p < 0.05). The allele frequencies in CYP3A4*18 and CYP2C19*17 were too low to permit further subgroup analyses. Our results suggest that the genetic polymorphisms in CYP2C19 may be influencing S-CIT serum concentrations, and that specific CYP2D6 polymorphisms may be predicting patient treatment outcomes based on gene dosage analyses.