Abstract

Objective To verify the notion that mirtazapine exerts early improvement (EI) in depression treatment, and further to evaluate reliability of EI on the symptom clusters of 17-item Hamilton Depression Rating Scale (HDRS-17) for predicting treatment outcomes. Methods The 4-week, open-label clinic trial included 82 eligible patients with depression. All received mirtazapine monotherapy. Efficacy was measured by HDRS-17, which was divided into 3 symptom clusters (mood, psychic anxiety, and somatic anxiety). By using the receiver operating characteristic (ROC) analysis, areas under ROC curve (AUC) were calculated to estimate the predictive values of EIs of HDRS-17 total score and its factors at week 1 and 2 (namely AUC1 and AUC2) for prognosticating treatment outcomes at week 4, and pair-wise comparisons between the AUCs were finally conducted. Results After 2-week treatment, 67(81.7%) patients gained early onset, and 18(22.0%) gained early response. At endpoint, 57(69.5%) and 33(40.2%) patients achieved treatment response and remission respectively. ROC analysis either on HDRS-17 total score or its factors showed reliable predictive effects (0.5<AUC<0.9, P<0.05), including that: 1)AUC for HDRS-17 total score gave the markedly higher values at week 2 than at week 1 (AUC1=0.69, AUC2=0.87, Z=5.547, P<0.01 for predicting later response and AUC1=0.65, AUC2=0.74, Z=3.475, P<0.01 for predicting later remission), 2)only mood factor among the 3 factors denoted equal AUC values to that for HDRS-17 total score, 3)mood cluster was superior to the others because of its higher AUC values (P<0.05), and 4)the clusters of mood, psychic anxiety and somatic anxiety presented declined values in turn in sensitivity, negative predictive value and accuracy, but low specificity. Conclusion Mirtazapine is confirmed to act an early onset in depression treatment. Three factors of HDRS-17 are reliably predictive of final outcomes (4-week), and F1 factor is better. Key words: Depression; Mirtazapine; Predict factor; Treatment outcome; ROC analysis

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