Comparing fast-acting interventions for treatment-resistant depression: An explorative study of accelerated HF-rTMS versus intranasal esketamine

Predictors of response to accelerated rTMS in the treatment of treatment-resistant depression

Accelerated repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for treatment-resistant depression (TRD). We aimed to investigate the existence of clinical predictive factors in response to accelerated rTMS in the treatment of TRD. In total, 119 TRD patients who received accelerated rTMS were included in this study. The stimulation protocol was 15 Hz stimulation over the the left dorsolateral prefrontal cortex. The protocol consisted of 25 sessions, each session lasting 30 min for a total of 3000 pulses. Five sessions were applied per day for 5 consecutive days. At baseline (T0), day 5 (immediately after treatment) (T1), 4 weeks after treatment (T2), depression severity was evaluated using the 17-item Hamilton Depression Rating Scale (HAMD-17), cognitive function was evaluated using Wisconsin Card Sorting Test (WCST), the intensity of suicidal ideation was evaluated using the Columbia-Suicide Severity Rating Scale (C-SSRS). Systemic immune-inflammation index (SII) was calculated at T0 and T2. The HAMD-17 scores, WCST performance, the C-SSRS scores at T1 and T2 were improved from T0 (P < 0.01). The SII at T2 was lower than at T0 (P < 0.01). The response rates at T1 and T2 were 57.98% (69/119) and 48.74% (58/119), respectively. The results of binary logistic analysis showed that shorter course of depression, two failed antidepressant trials, no history of ECT treatment, and lower levels of SII were predictive factors for accelerated rTMS treatment response at T1 and T2 (P < 0.05), while not having a history of hospitalization was a predictive factor for response at T2 (P < 0.05) but not at T1 (P > 0.05). Based on ROC curve analysis, the optimal cut-off values of SII for discriminating responders from non-responders at T1 and T2 were < 478.56 and < 485.03, respectively. The AUC of SII at T0 predicting response for T1 and T2 were 0.729 and 0.797. We found several clinical predictors of better responses to the accelerated rTMS. Identifying clinical predictors of response is relevant to personalize and adapt rTMS protocols in TRD patients.

Maintenance Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder: A Meta-analysis

ABSTRACT

Repetitive transcranial magnetic stimulation (rTMS) is an effective therapy for acute treatment of major depressive disorder (MDD). However, the efficacy and optimal strategy of delivering maintenance rTMS beyond acute treatment remains unclear. This meta-analysis aims to quantify the treatment effect of maintenance rTMS therapy in MDD and compares the difference in treatment effect between the fixed and rescue maintenance rTMS protocols. We conducted a meta-analysis of 14 studies (N = 705) comparing depression rating scores before and after maintenance rTMS. Standardized mean scores adjusted for sample size (Hedges g) were used as the effect size. Subgroup analysis was performed to compare the fixed and rescue maintenance rTMS treatment. Maintenance rTMS was associated with a statistically significant improvement in depression scores (standardized mean difference [SMD] = 0.75; confidence interval [CI] = -1.25 to -0.25). The random effects model had the Q value = 142.67 (P < 0.0001) and I2 = 90%, supporting significant heterogeneity among studies. The prediction interval yielded a possible effect size from -2.54 to 1.05. The subgroup analysis showed a stronger treatment effect for rescue maintenance protocol (SMD = -1.17; CI = -2.13 to -0.21) compared to fixed maintenance protocol (SMD = -0.45; CI = -1.00.16). Although not statistically significant (Q-between = 2.56, df-between = 1, P = 0.1096), a large difference in effect size was observed between subgroups. Maintenance rTMS appears to be an effective strategy for maintaining remission and preventing relapse in MDD. Significant heterogeneity among the studies warrants caution in interpreting the results. These findings suggest the need for standard protocols and consensus guidelines for the optimal delivery of maintenance rTMS treatment.

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

Dopaminergic dysfunction in the left putamen of patients with major depressive disorder

INTRODUCTION

Dopaminergic transmission impairment has been identified as one of the main neurobiological correlates of both depression and clinical symptoms commonly associated with its spectrum such as anhedonia and psychomotor retardation.

OBJECTIVES

We examined the relationship between dopaminergic deficit in the striatum, as measured by 123I-FP-CIT SPECT imaging, and specific psychopathological dimensions in patients with major depressive disorder.

METHODS

To our knowledge this is the first study with a sample of >120 subjects. After check for inclusion and exclusion criteria, 121 (67 females, 54 males) patients were chosen retrospectively from an extensive 1106 patients database of 123I-FP-CIT SPECT scans obtained at the Nuclear Medicine Unit of Fondazione Policlinico Universitario Agostino Gemelli IRCCS in Rome. These individuals had undergone striatal dopamine transporter (DAT) assessments based on the recommendation of their referring clinicians, who were either neurologists or psychiatrists. At the time of SPECT imaging, each participant underwent psychiatric and psychometric evaluations. We used the following psychometric scales: Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Snaith Hamilton Pleasure Scale, and Depression Retardation Rating Scale.

RESULTS

We found a negative correlation between levels of depression (p = 0.007), anxiety (p = 0.035), anhedonia (p = 0.028) and psychomotor retardation (p = 0.014) and DAT availability in the left putamen. We further stratified the sample and found that DAT availability in the left putamen was lower in seriously depressed patients (p = 0.027) and in patients with significant psychomotor retardation (p = 0.048).

CONCLUSION

To our knowledge this is the first study to have such a high number of sample. Our study reveals a pivotal role of dopaminergic dysfunction in patients with major depressive disorder. Elevated levels of depression, anxiety, anhedonia, and psychomotor retardation appear to be associated with reduced DAT availability specifically in the left putamen.

Unveiling potential adverse events associated with escitalopram oxalate: A real-world analysis based FDA adverse event reporting system database

OBJECTIVE

The study aimed to conduct a multidimensional evaluation of potential adverse events (AEs) of escitalopram oxalate based on the FDA adverse event reporting system (FAERS) database.

METHODS

This study utilized the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma-poisson shrinker (MGPS) to mine and analyze data from the FAERS database from the first quarter of 2004 to the second quarter of 2023.

RESULTS

There was a total of 19,854 AE reports related to escitalopram oxalate, extracting 625 preferred terms (PTs), and covering 27 system organ classes (SOCs). The results showed that the number of reports by females was significantly higher than males, accounting for 57.68%. The reporting number was higher in 2018 and 2019, accounting for 9.50% and 10.18% of the total reports, respectively. The main reporters were consumers and other health professionals, accounting for 26.99% and 26.75% respectively. The majority of the reports were primarily from the United States. Newly emerging AE signals such as intentional overdose (n = 691, ROR 8.51, PRR 8.45, IC 3.05, Empirical Bayesian Geometric Mean (EBGM) 8.35), suicide attempt (n = 665, ROR 8.58, PRR 8.52, IC 3.06, EBGM 8.42), serum serotonin (n = 5, ROR 1044.78, PRR 1044.71, IC 2.56, EBGM 392.39), anti-actin antibody positive (n = 5, ROR 626.87, PRR 626.83, IC 2.56, EBGM 313.91), among others, were not mentioned in the drug's label.

CONCLUSION

While escitalopram oxalate has clear benefits in the treatment of depression and other mental health disorders, the presence of AEs also suggests risks associated with its use. Particularly concerning are risks of suicide and changes in serum serotonin levels.

The rapid antidepressant effectiveness of repeated dose of intravenous ketamine and intranasal esketamine: A post-hoc analysis of pooled real-world data

INTRODUCTION

Intravenous ketamine (KET-IV) and intranasal esketamine (ESK-NS) are effective in the acute treatment of Treatment-Resistant Depression (TRD). Studies comparing KET-IV and ESK-NS concerning their action, safety, and tolerability are currently lacking.

MATERIALS AND METHODS

We combined patients' data from two unipolar TRD cohorts that received KET-IV (n = 171) at the Canadian Rapid Treatment Center of Excellence in Toronto, Canada, or ESK-NS (n = 140) at several TRD clinics in Italy. The Quick Inventory for Depression Symptomatology-Self-Report-16/QIDS-SR16 in the KET-IV group and Montgomery-Åsberg Depression Rating Scale/MADRS in the ESK-NS group measured depressive symptoms at baseline (T0) and after the acute treatment phase (T1) (i.e., four infusions of KET-IV and eight administrations of ESK-NS). As different scales were used, the primary outcome was to compare the improvement in depression severity in the two cohorts by measuring effect sizes, response and remission rates. Finally, we compare side effects and discontinuation rates.

RESULTS

At T1, KET-IV and ESK-NS significantly reduced depressive symptoms (respectively: QIDS-SR16 mean reduction = 5.65, p < 0.001; MADRS mean reduction = 11.41, p = 0.025). KET-IV showed larger effect sizes compared to ESK-NS (1.666 vs. 1.244). KET-IV had higher response rates (36 % vs. 25 %; p = 0.042) but not superior remission rates (13 % vs. 12 %; p = 0.845) than ESK-NS at T1. Despite more reported side effects, KET-IV did not cause more discontinuations for adverse events (4.6 % vs. 2.12 %; p = 0.228) than ESK-NS.

CONCLUSION

KET-IV showed a higher short-term antidepressant effect, whereas ESK-NS exhibited lower side effects. Both were generally well tolerated. Future head-to-head studies should consider the long-term efficacy of these treatments.

Stanford neuromodulation therapy for treatment-resistant depression: a systematic review

Objective

This systematic review of randomized controlled studies (RCTs) and observational studies evaluated the efficacy and safety of stanford neuromodulation therapy (SNT) for patients with treatment-resistant depression (TRD).

Methods

A systematic search (up to 25 September, 2023) of RCTs and single-arm prospective studies was conducted.

Results

One RCT (n = 29) and three single-arm prospective studies (n = 34) met the study entry criteria. In the RCT, compared to sham, active SNT was significantly associated with higher rates of antidepressant response (71.4% versus 13.3%) and remission (57.1% versus 0%). Two out of the three single-arm prospective studies reported the percentage of antidepressant response after completing SNT, ranging from 83.3% (5/6) to 90.5% (19/21). In the three single-arm prospective studies, the antidepressant remission rates ranged from 66.7% (4/6) to 90.5% (19/21). No severe adverse events occurred in all the four studies.

Conclusion

This systematic review found SNT significantly improved depressive symptoms in patients with TRD within 5 days, without severe adverse events.

Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING)

Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.