scholarly journals Identifying predictive clinical characteristics of the treatment efficacy of mirtazapine monotherapy for major depressive disorder

2016 ◽  
Vol Volume 12 ◽  
pp. 2533-2538 ◽  
Author(s):  
Takahiro Tsutsumi ◽  
Hiroko Sugawara ◽  
Ryoko Ito ◽  
Mizuho Asano ◽  
Satoru Shimizu ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Treatment Efficacy ◽  
Clinical Characteristics ◽  
Major Depressive

scholarly journals Stigma-discrimination complex associated with major depressive disorder

2019 ◽  
Vol 67 (4) ◽  
pp. 531-532
Author(s):  
Adalberto Campo-Arias ◽  
Edwin Herazo ◽  
Guillermo Augusto Ceballos-Ospino
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Clinical Characteristics ◽  
Perceived Stigma ◽  
Negative Effects ◽  
Major Depressive ◽  
Social Sphere ◽  
Personal Stigma ◽  
The Social ◽  
The Relationship

Dear Editor:Throughout history, the stigma-discrimination complex (SDC) has been associated with serious mental disorders such as those on the spectrum of schizophrenia, where symptoms, side effects and impaired social functioning are difficult to conceal. (1) For its part, SDC related to major depressive disorder (MDD) is a growing phenomenon even though its clinical characteristics are easy to hide or are less evident in the social sphere (2,3); in these cases, said association may have more negative effects on people’s lives than the disorder itself. (4,5) Consequently, the Depression Stigma Scale (DSS) was designed to quantify the relationship between SDC and MDD (SDC-MDD). This is a Likert scale consisting of two subscales with nine items each. The first addresses the issue of attitude towards people who meet criteria for MDD, i.e. perceived stigma, and the second, the anticipated attitude for MDD, i.e. personal stigma or self-stigma. (6)

scholarly journals Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Sixiang Liang ◽  
Jinhe Zhang ◽  
Qian Zhao ◽  
Amanda Wilson ◽  
Juan Huang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Regression Analysis ◽  
Depressive Disorder ◽  
Logistic Regression Analysis ◽  
Clinical Characteristics ◽  
Information Criteria ◽  
Demographic Information ◽  
Major Depressive ◽  
Incidence Trends

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population.Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model.Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer–Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set.Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.

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