Usefulness of Hamilton rating scale for depression subset scales and full versions for electroconvulsive therapy

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials with standardized outcome measures is useful for training machine learning models; however, combining data across trials poses numerous challenges. There is also persistent concern that machine learning models can propagate harmful biases. We have created a methodology for organizing and preprocessing depression clinical trial data such that transformed variables harmonized across disparate datasets can be used as input for feature selection. Using Bayesian optimization, we identified an optimal multi-layer dense neural network that used data from 21 clinical and sociodemographic features as input in order to perform differential treatment benefit prediction. With this combined dataset of 5032 individuals and 6 drugs, we created a differential treatment benefit prediction model. Our model generalized well to the held-out test set and produced similar accuracy metrics in the test and validation set with an AUC of 0.7 when predicting binary remission. To address the potential for bias propagation, we used a bias testing performance metric to evaluate the model for harmful biases related to ethnicity, age, or sex. We present a full pipeline from data preprocessing to model validation that was employed to create the first differential treatment benefit prediction model for MDD containing 6 treatment options.

Efficacy of empagliflozin as adjunctive therapy to citalopram in major depressive disorder: a randomized double-blind, placebo-controlled clinical trial

BACKGROUND

Major depressive disorder is one of the most common psychiatric disorders, which is associated with a high disease burden. Current treatments using antidepressants have limitations, so using medication with neuromodulating and anti-inflammatory properties alongside them could be helpful. In a clinical trial, we studied the effectiveness of empagliflozin, a blood sugar-lowering drug, as an adjunctive therapy to reduce the severity of depression symptoms.

METHODS

A number of outpatients with moderate to severe depression (Hamilton Depression Rating Scale (HDRS) > = 17) who were not under related medication or had not taken medication for at least the last two months, had an age range of 18-60 years and had written informed consent to enter the study (N = 90) were randomly divided into two groups receiving placebo or empagliflozin (10 mg daily) combined with citalopram (40 mg daily) based on permuted block randomization method in an 8-week randomized, double-blind, placebo-controlled clinical trial. They were evaluated using the HDRS in weeks 0, 4, and 8.

RESULTS

HDRS scores were equal to 28.42(± 3.83), 20.20(± 3.82), and 13.42(± 3.42) in the placebo group during weeks 0,4, and 8, respectively. These scores were 27.36(± 3.77), 13.76(± 1.40), and 7.00(± 1.13), respectively, for the group treated with empagliflozin. Compared to the control group, patients treated with empagliflozin using repeated-measures ANOVA showed greater improvement in reducing the severity of depression symptoms over time (p value = 0.0001).

CONCLUSIONS

Considering the promising findings in this clinical trial, further study of empagliflozin as adjunctive therapy in MDD with larger sample sizes and longer follow-ups is recommended.

The potential predictors for treatment-resistance depression after selective serotonin reuptake inhibitors therapy in Han Chinese: A resting-state functional magnetic resonance imaging study

AIM

Selective serotonin reuptake inhibitors (SSRIs) are among the most important antidepressants. However, there is limited research on predicting the occurrence of treatment-resistant depression (TRD) after 5 years. Examining the predictive effect of TRD occurrence using resting-state fMRI in patients initiating SSRIs treatment at the onset of major depressive disorder (MDD) could potentially enhance TRD management.

METHODS

A total of 60 first-episode drug-naive MDD patients who met the criteria, along with 41 healthy controls of Han Chinese ethnicity, were recruited. All MDD patients received SSRIs as the initial treatment for relieving depressive symptoms. Resting-state fMRI scans were conducted for all subjects. Follow-up assessments were conducted over a period of five years, during which MDD patients were categorized into treatment-resistant depression (TRD) and non-treatment-resistant depression (NRD) groups based on disease progression. Amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and Regional Homogeneity (ReHo) values were calculated and compared among the three groups. Additionally, receiver operating characteristic (ROC) curves were employed to identify potential predictors.

RESULTS

After 5 years of follow-up, it was found that 43 MDD patients were classified as NRD, while 17 were classified as TRD. In comparison to TRD, NRD exhibited decreased ALFF in the left middle cingulum gyrus (MCG.L) and in the right middle frontal gyrus (MFG.R), as well as decreased ReHo in MCG.L. Furthermore, NRD showed increased fALFF in the left precuneus (PCUN.L). The area under the curve (AUC) values were as follows: 0.724 (MCG.L by ALFF), 0.732 (MFG.R), 0.767 (PCUN.L), 0.774 (MCG.L by ReHo), 0.878 (combined), 0.547 (HAMD), and 0.408 (HAMA) respectively.

CONCLUSION

The findings suggest that PCUN.L, MFG.R, MCG.L, and the combined measures may indicate the possibility of developing TRD after 5 years when SSRIs are used as the initial therapy for relieving depressive symptoms in MDD patients.

Consanguinity and Nonsuicidal Self-Injury in Depressed Patients: New Risk Factors and Risk Prediction Models

Objective

The aim of the study was to identify the risk factors associated with nonsuicidal self-injurious (NSSI) behavior in patients with depressive disorders and develop predictive models utilizing these influencing factors as predictors, followed by validation of the constructed models for their efficacy.

Methods

Patients with depression disorders admitted to Wuhan Mental Health Center from 2020 to 2021 were included using retrospective analysis. Patients who exhibited one or more items on the NSSI behavior rating questionnaire were categorized into the NSSI group, while those without any such behaviors were assigned to the non-NSSI group. Patients in both groups were categorized separately based on gender, age, personality traits, and interpersonal relationships. The above data were analyzed using multiple logistic regression analysis. Prediction models were constructed, receiver operating characteristic (ROC) curves were produced and model accuracy was calculated.

Results

A total of 237 patients were included in this study, with 122 patients assigned to the NSSI group and 115 patients assigned to the non-NSSI group. By comparing the baseline data of the patients in the 2 groups, the results revealed statistically significant differences between the 2 groups in terms of age, grades at school, early childhood parenting style, Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Experiences in Close Relationships Scale (ECRS) (P＜.05). However, no statistically significant differences were observed for the remaining indicators (P＞.05). The results of the multiple logistic regression model showed that grades at school, early childhood parenting style, HAMD, HAMA, and ECRS scores were risk factors. The ROC model was constructed using school performance, childhood parenting style, HAMD, HAMA, and ECRS scores as predictors. The findings indicated that the ECRS score was the best predictor of NSSI, and it had a sensitivity of 91.8% and specificity of 70.5% for an area of 0.967.

Conclusion

ECRS was utilized as a predictor to evaluate the NSSI inclination of depressed patients with commendable sensitivity and specificity. Furthermore, early childhood parenting style, HAMD, HAMA, and ECRS scores were identified as risk factors for NSSI. For individuals at high risk who exhibit these aforementioned risk factors, clinical diagnosis and treatment should be approached with caution.

Effect of internet multiple linkage mode-based extended care combined with in-hospital comfort care on colorectal cancer patients undergoing colostomy

BACKGROUND

Patients with colorectal cancer may need postoperative nursing to improve prognosis, and conventional nursing is not effective. Clinical research is needed to explore nursing methods that can more effectively improve postoperative conditions on colorectal cancer patients undergoing colostomy.

AIM

To explore the effect of internet multiple linkage mode-based extended care combined with in-hospital comfort care on colorectal cancer patients undergoing colostomy.

METHODS

Data from 187 patients with colostomy treated in our hospital from May 2019 to March 2022 were collected and divided into three groups, A (n = 62), B (n = 62) and C (n = 63), according to different intervention methods. Group A received internet multiple linkage mode-based extended care combined with in-hospital comfort care. Group B received internet multiple linkage mode-based extended care. Group C received usual care intervention. Complications were compared among the three groups. The stoma self-efficacy scale, Hamilton Anxiety Scale, Hamilton Depression Scale, Brief Fatigue Inventory and City of Hope-quality of Life-ostomy Questionnaire before and after intervention were compared among the three groups.

RESULTS

The complication rate of group A, B and C (16.13%, 20.97% and 60.32%, respectively) was significantly different (all P < 0.05). The incidence of complications in groups A and B was lower than that in group C, and there was no significant difference between groups A and B (P > 0.05). After intervention, the scores of ostomy care, social contact, diet choice, confidence in maintaining vitality, confidence in self-care of ostomy, confidence in sexual life, confidence in sexual satisfaction and confidence in physical labor in the three groups were all higher than before intervention, and the scores of groups A and B were higher than those of group C, with statistical significance (P < 0.05). The Hamilton Anxiety Scale and Hamilton Depression Scale scores of the three groups after intervention were lower than those before intervention. The scores of groups A and B were lower than those of group C, and the score of group A was lower than that of group B, all with statistical significance (all P < 0.05). There was a statistically significant difference in cancer-induced fatigue among the three groups (P < 0.05). After intervention, the scores of physical health, psychological health, social health and mental health of the three groups were lower than before the intervention. The scores of group A and B were lower than that of group C; and the score of group A was lower than that of group B, all with statistical significance (all P < 0.05).

CONCLUSION

Internet multiple linkage mode-based extended care combined with in-hospital comfort care can effectively improve self-efficacy, bad mood, cancer-related fatigue and life quality of colorectal cancer patients undergoing colostomy.

Diagnostic value of MicroRNAs for depression: A systematic review and meta-analysis

BACKGROUND

Currently, depression is diagnosed on the basis of neuropsychological examinations and clinical symptoms, and there is no objective diagnostic method. Several studies have explored the application of microRNAs as potential biomarkers diagnostic for depression. This study aims to determine the diagnostic value of microRNAs for depression.

METHODS

PubMed, Embase, the Cochrane Library, the Web of Science, Wanfang Database, SINOMED, China Science and Technology Journal Databaseand China National Knowledge Infrastructure were searched up to 11 January 2022. Stata (version 16.0) and RevMan (version 5.3) software were used for meta-analysis. The pooled sensitivity, pooled specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR) were calculated; the summary receiver operating characteristic (SROC) curve was plotted, and the area under the curve (AUC) was calculated. Moreover, meta-regression analyses were performed to determine the source of heterogeneity. Deeks' funnel plot test was used to assess publication bias.

RESULTS

In total, 677 patients were enrolled, including 364 patients with depression and 313 healthy controls. Meta-analysis results showed that the pooled sensitivity, specificity, and DOR of microRNAs for the diagnosis of depression were 0.82 [95% confidence intervals(CI): 0.76, 0.87], 0.70 (95% CI: 0.62, 0.77), and 11 (95% CI: 6, 20), respectively, and the AUC of the SROC was 0.84 (95% CI: 0.80, 0.87).

CONCLUSIONS

MicroRNAs have high sensitivity and specificity in diagnosing depression and are potential diagnostic biomarkers for depression.

REGISTRATION NUMBER

PROSPERO CRD42022303616.

Nonremission After Electroconvulsive Therapy in Individuals With Major Depression: Role of Borderline Personality Disorder

OBJECTIVES

The aim of the present study was to investigate the risk of nonremission following electroconvulsive therapy (ECT), as associated with borderline personality disorder, in individuals with major depression in the context of the contradictory data available in the literature.

METHODS

We analyzed demographic and clinical data from 210 individuals with major depression who were treated with ECT. Study participants were recruited from the medical records database of the Psychiatry Department at Erasme Hospital. Only individuals with major depression who were in remission, as demonstrated during the systematic psychiatric interview performed at the end of ECT (ie, with a >60% reduction in their 24-item Hamilton Depression Rating Scale score, combined with a score of <10), were included in the "remission" group. Logistic regression analyses were used to determine the risk of nonremission following ECT.

RESULTS

Nonremission following ECT occurred frequently (42.9%) in our sample. Moreover, after adjusting for major confounding factors, multivariate logistic regression analyses demonstrated that borderline personality disorder was a risk factor for nonremission following ECT in individuals with major depression.

CONCLUSIONS

We demonstrated that borderline personality disorder was associated with a higher risk of nonremission following ECT in individuals with major depression. This finding seems to justify more systematic screening as well as more adequate management of this personality disorder in individuals with major depression who are treated with ECT to allow for attaining better remission rates in this subpopulation.