Journal of Affective Disorders, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Journal of Affective Disorders, Год журнала: 2024, Номер unknown
Опубликована: Сен. 1, 2024
Язык: Английский
ALPHA PSYCHIATRY, Год журнала: 2024, Номер 24(6), С. 270 - 272
Опубликована: Янв. 4, 2024
Язык: Английский
Процитировано
33Psychiatry Research, Год журнала: 2023, Номер 327, С. 115378 - 115378
Опубликована: Июль 29, 2023
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 FDA, but data about predictors of response are still lacking. Thus, tool that can predict the individual patients' probability to ESK-NS is needed. This study investigates sociodemographic features predicting responses in patients using machine learning techniques. In retrospective, multicentric, real-world involving 149 subjects, psychometric (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 one month/T1 three months/T2 post-treatment initiation. We trained different random forest classifiers, able accuracies 68.53% T1 66.26% T2 remission 68.60% accuracy. Features like anhedonia, anxious distress, mixed symptoms as well bipolarity found positively remission. At same time, benzodiazepine usage severity linked delayed responses. Despite some limitations (i.e., retrospective study, lack biomarkers, correct interrater-reliability across centers), these findings suggest potential personalized intervention TRD.
Язык: Английский
Процитировано
28Journal of Affective Disorders, Год журнала: 2023, Номер 348, С. 314 - 322
Опубликована: Дек. 23, 2023
Язык: Английский
Процитировано
25Frontiers in Psychiatry, Год журнала: 2023, Номер 14
Опубликована: Дек. 13, 2023
Objective This systematic review of randomized controlled studies (RCTs) and observational evaluated the efficacy safety stanford neuromodulation therapy (SNT) for patients with treatment-resistant depression (TRD). Methods A search (up to 25 September, 2023) RCTs single-arm prospective was conducted. Results One RCT ( n = 29) three 34) met study entry criteria. In RCT, compared sham, active SNT significantly associated higher rates antidepressant response (71.4% versus 13.3%) remission (57.1% 0%). Two out reported percentage after completing SNT, ranging from 83.3% (5/6) 90.5% (19/21). studies, ranged 66.7% (4/6) No severe adverse events occurred in all four studies. Conclusion found improved depressive symptoms TRD within 5 days, without events.
Язык: Английский
Процитировано
11Journal of Affective Disorders, Год журнала: 2024, Номер 357, С. 107 - 115
Опубликована: Апрель 16, 2024
Язык: Английский
Процитировано
4Psychiatry Research Case Reports, Год журнала: 2025, Номер unknown, С. 100250 - 100250
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Reviews in the Neurosciences, Год журнала: 2025, Номер unknown
Опубликована: Фев. 13, 2025
Abstract Major depressive disorder is a common mental disorder, and significant number of patients exhibit poor response to conventional antidepressant treatments, known as treatment-resistant depression (TRD). However, the definition TRD globally remains unclear, impeding clinical research, treatment development, outcome implementation, policy-making. A wealth research confirms that rTMS demonstrates promising efficacy in TRD. This paper elucidates TRD, summarizes potential targets for treating comprehensively elaborates on unique mechanisms, efficacy, side effects outlines considerations special populations receiving well other modalities Through these studies, we aim provide more scientifically grounded recommendations undergoing
Язык: Английский
Процитировано
0Journal of Affective Disorders Reports, Год журнала: 2025, Номер unknown, С. 100905 - 100905
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Journal of Psychopharmacology, Год журнала: 2024, Номер 38(6), С. 567 - 578
Опубликована: Апрель 27, 2024
Objective: The study aimed to conduct a multidimensional evaluation of potential adverse events (AEs) escitalopram oxalate based on the FDA event reporting system (FAERS) database. Methods: This utilized odds ratio (ROR), proportional (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma-poisson shrinker (MGPS) mine analyze data from FAERS database first quarter 2004 second 2023. Results: There was total 19,854 AE reports related oxalate, extracting 625 preferred terms (PTs), covering 27 organ classes (SOCs). results showed that number by females significantly higher than males, accounting for 57.68%. in 2018 2019, 9.50% 10.18% reports, respectively. main reporters were consumers other health professionals, 26.99% 26.75% majority primarily United States. Newly emerging signals such as intentional overdose ( n = 691, ROR 8.51, PRR 8.45, IC 3.05, Empirical Geometric Mean (EBGM) 8.35), suicide attempt 665, 8.58, 8.52, 3.06, EBGM 8.42), serum serotonin 5, 1044.78, 1044.71, 2.56, 392.39), anti-actin antibody positive 626.87, 626.83, 313.91), among others, not mentioned drug’s label. Conclusion: While has clear benefits treatment depression mental disorders, presence AEs also suggests risks associated with its use. Particularly concerning are changes levels.
Язык: Английский
Процитировано
1Frontiers in Psychiatry, Год журнала: 2024, Номер 15
Опубликована: Июль 17, 2024
Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD’s clinical manifestations neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide choices TRD, herein we introduce SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) conducting preliminary validation 2/WP2) computational tool (SelecTool) that integrates data, neurophysiological (EEG) peripheral (blood sample) through machine-learning framework designed optimize TRD protocols. project aims enhance decision-making by enabling selection personalized It leverages multi-modal data analysis navigate towards two validated therapeutic options TRD: esketamine nasal spray (ESK-NS) accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with will be randomized receive either ESK-NS or arTMS, comprehensive evaluations encompassing (EEG), (psychometric scales), samples) assessments both at baseline (T0) one month post-treatment initiation (T1). WP2 utilize collected WP1 train algorithm, followed its application second, out-of-sample cohort 20 subjects, assigning treatments based tool’s recommendations. Ultimately, this seeks revolutionize employing advanced machine learning strategies thorough analysis, aimed unraveling complex landscape depression. effort is expected provide pivotal insights promote development more individually tailored strategies, thus addressing significant void current management potentially reducing profound societal burdens.
Язык: Английский
Процитировано
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