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.

scholarly journals Major depression among men and women in the US during the COVID-19 pandemic

2021 ◽  
Author(s):  
Areen Omary
Keyword(s):  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Regression Analysis ◽  
Health Status ◽  
Major Depression ◽  
Depressive Disorder ◽  
Logistic Regression Analysis ◽  
Major Depressive ◽  
Men And Women ◽  
The Us

A multidimensional index that measures the health status of individuals during the COVID-19 pandemic has been developed. The COVID-19 Health Status Scale (CHSS), a combination of previously studied and newly created health status scales, assesses the physical, mental, and social well-being of adults during the COVID-19 pandemic. The current study aimed to examine the differences between men and women in self-reported past major depressive disorder diagnosis during the COVID-19 lockdown in the United States using the CHSS self administered questionnaire. Participants were recruited using convenience sampling performed online through the SurveyMonkey Audience. The self-administered CHSS questionnaire has been pilot tested in an adult population during the COVID-19 pandemic lockdown in the US. The study sample size included 173 participants aged 18 years and older. Results revealed that almost one-third of the study participants (31.2%) reported being diagnosed with past major depressive disorder, whereas 68.8% reported no past major depressive disorder diagnosis. The results of the estimated coefficients of the logistic regression analysis test showed that men were less likely to report major depression during the COVID-19 lockdown (Exp (B) = 0.45 for men; p < 0.05) than women. Although the results showed that almost two thirds of the participants reported no major depression diagnosis during the COVID-19 lockdown, the logistic regression analysis confirmed that the chances of men reporting major depressive disorder diagnosis were lesser than that of women and this difference was statistically significant.

Self-Reported Inability to Cry as a Symptom of Anhedonic Depression in Outpatients with a Major Depressive Disorder

2011 ◽  
Vol 108 (3) ◽  
pp. 874-882 ◽  
Author(s):  
Roberta Steer
Keyword(s):  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Regression Analysis ◽  
Depressive Disorder ◽  
Multiple Logistic Regression Analysis ◽  
The Other ◽  
Multiple Logistic Regression ◽  
Major Depressive ◽  
Dsm Iv ◽  
Anhedonic Depression

To ascertain whether self-reported inability to cry would be associated with symptoms of anhedonic depression, the 21-item Beck Depression Inventory-II was administered to 1,050 outpatients diagnosed with a DSM-IV-TR major depressive disorder. 219 (21%) patients reported on the BDI-II Crying item that they were unable to cry, and 831 (79%) patients reported they were able to cry. Only BDI-II Loss of Interest was significantly associated with the inability to cry after the other BDI-II symptoms were controlled for using a multiple logistic-regression analysis. The inability to cry was discussed as an indicator of anhedonic depression.

scholarly journals Plasma phospholipid n-3 polyunsaturated fatty acids and major depressive disorder in Japanese elderly: the Japan Public Health Center-based Prospective Study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kei Hamazaki ◽  
Yutaka J. Matsuoka ◽  
Taiki Yamaji ◽  
Norie Sawada ◽  
Masaru Mimura ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Regression Analysis ◽  
Depressive Disorder ◽  
Multivariate Logistic Regression Analysis ◽  
Plasma Phospholipid ◽  
Multivariate Logistic Regression ◽  
Major Depressive ◽  
Cross Sectional

AbstractThe beneficial effects of n-3 polyunsaturated fatty acids (PUFAs) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) on depression are not definitively known. In a previous population-based prospective cohort study, we found a reverse J-shaped association of intake of fish and docosapentaenoic acid (DPA), the intermediate metabolite of EPA and DHA, with major depressive disorder (MDD). To examine the association further in a cross-sectional manner, in the present study we analyzed the level of plasma phospholipid n-3 PUFAs and the risk of MDD in 1,213 participants aged 64–86 years (mean 72.9 years) who completed questionnaires and underwent medical check-ups, a mental health examination, and blood collection. In multivariate logistic regression analysis, odds ratios and 95% confidence intervals were calculated for MDD according to plasma phospholipid n-3 PUFA quartiles. MDD was diagnosed in 103 individuals. There were no significant differences in any n-3 PUFAs (i.e., EPA, DHA, or DPA) between individuals with and without MDD. Multivariate logistic regression analysis showed no significant association between any individual n-3 PUFAs and MDD risk. Overall, based on the results of this cross-sectional study, there appears to be no association of plasma phospholipid n-3 PUFAs with MDD risk in the elderly Japanese population.

scholarly journals Sex-Disaggregated Data on Clinical Characteristics and Outcomes of Hospitalized Patients With COVID-19: A Retrospective Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Mengdie Wang ◽  
Nan Jiang ◽  
Changjun Li ◽  
Jing Wang ◽  
Heping Yang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Clinical Characteristics ◽  
Random Effect ◽  
Hospitalized Patients ◽  
Male And Female ◽  
Female Patients ◽  
Disease Outcomes ◽  
Male Patients

BackgroundSex and gender are crucial variables in coronavirus disease 2019 (COVID-19). We sought to provide information on differences in clinical characteristics and outcomes between male and female patients and to explore the effect of estrogen in disease outcomes in patients with COVID-19.MethodIn this retrospective, multi-center study, we included all confirmed cases of COVID-19 admitted to four hospitals in Hubei province, China from Dec 31, 2019 to Mar 31, 2020. Cases were confirmed by real-time RT-PCR and were analyzed for demographic, clinical, laboratory and radiographic parameters. Random-effect logistic regression analysis was used to assess the association between sex and disease outcomes.ResultsA total of 2501 hospitalized patients with COVID-19 were included in the present study. The clinical manifestations of male and female patients with COVID-19 were similar, while male patients have more comorbidities than female patients. In terms of laboratory findings, compared with female patients, male patients were more likely to have lymphopenia, thrombocytopenia, inflammatory response, hypoproteinemia, and extrapulmonary organ damage. Random-effect logistic regression analysis indicated that male patients were more likely to progress into severe type, and prone to ARDS, secondary bacterial infection, and death than females. However, there was no significant difference in disease outcomes between postmenopausal and premenopausal females after propensity score matching (PSM) by age.ConclusionsMale patients, especially those age-matched with postmenopausal females, are more likely to have poor outcomes. Sex-specific differences in clinical characteristics and outcomes do exist in patients with COVID-19, but estrogen may not be the primary cause. Further studies are needed to explore the causes of the differences in disease outcomes between the sexes.

scholarly journals CLASSIFICATION OF BIPOLAR DISORDER, MAJOR DEPRESSIVE DISORDER, AND HEALTHY STATE USING VOICE

2018 ◽  
Vol 11 (15) ◽  
pp. 89 ◽  
Author(s):  
Masakazu Higuchi ◽  
Shinichi Tokuno ◽  
Mitsuteru Nakamura ◽  
Shuji Shinohara ◽  
Shunji Mitsuyoshi ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mental Disease ◽  
Healthy Individuals ◽  
Major Depressive ◽  
Healthy State ◽  
Polytomous Logistic Regression

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis.Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features.Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy.Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression.

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