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

Journal of Affective Disorders, Год журнала: 2023, Номер 348, С. 314 - 322

Опубликована: Дек. 23, 2023

Язык: Английский

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TSF-MDD: A Deep Learning Approach for Electroencephalography-Based Diagnosis of Major Depressive Disorder with Temporal–Spatial–Frequency Feature Fusion

Bioengineering, Год журнала: 2025, Номер 12(2), С. 95 - 95

Опубликована: Янв. 21, 2025

Major depressive disorder (MDD) is a prevalent mental illness characterized by persistent sadness, loss of interest in activities, and significant functional impairment. It poses severe risks to individuals’ physical psychological well-being. The development automated diagnostic systems for MDD essential improve accuracy efficiency. Electroencephalography (EEG) has been extensively utilized research. However, studies employing deep learning methods still face several challenges, such as difficulty extracting effective information from EEG signals data leakage due experimental designs. These issues result limited generalization capabilities when models are tested on unseen individuals, thereby restricting their practical application. In this study, we propose novel approach, termed TSF-MDD, which integrates temporal, spatial, frequency-domain information. TSF-MDD first applies reconstruction scheme obtain four-dimensional temporal–spatial–frequency representation signals. then processed model based 3D-CNN CapsNet, enabling comprehensive feature extraction across domains. Finally, subject-independent partitioning strategy employed during training testing eliminate leakage. proposed approach achieves an 92.1%, precision 90.0%, recall 94.9%, F1-score 92.4%, respectively, the Mumtaz2016 public dataset. results demonstrate that exhibits excellent performance.

Язык: Английский

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New trends in personalized treatment of depression

Current Opinion in Psychiatry, Год журнала: 2023, Номер 37(1), С. 3 - 8

Опубликована: Окт. 19, 2023

Purpose of review Major depressive disorder (MDD) is a common and burdensome severe mental disorder, which expected to become the leading cause disease burden worldwide. Most patients with MDD remain untreated/undertreated. For many decades “a trial error” approach has been adopted for selecting best treatment plan each individual patient, but more recently personalized proposed, by taking into account several clinical factors (e.g., stage, comorbidity, duration illness). Therefore, aim this study address most relevant innovations in MDD. Recent findings In recent years, pharmacological nonpharmacological have introduced As regards treatments, newly developed drugs an innovative mechanism action, targeting glutamatergic systems. These are highly effective improving symptoms, good level safety tolerability. interventions, include both new strategies different domains lifestyle interventions aiming improve physical symptoms depression or virtual reality) classical provided through mechanisms web-based psychotherapies use digital approaches). Patients globally report acceptability these interventions. Summary Depression heterogeneous, complex multidimensional representing one causes disability The final management functional recovery, can be achieved using personalized, integrated recovery-oriented Several treatments now available; should selected on basis patient's needs preferences order tailor treatment, according shared decision-making approach.

Язык: Английский

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Use of ketamine for treatment resistant depression: updated review of literature and practical applications to a community ketamine program in Edmonton, Alberta, Canada

Frontiers in Psychiatry, Год журнала: 2024, Номер 14

Опубликована: Янв. 8, 2024

Background Though intravenous (IV) ketamine and intranasal (IN) esketamine are noted to be efficacious for treatment-resistant depression (TRD), access each of these treatments within healthcare systems is limited due cost, availability, and/or monitoring requirements. IV has been offered at two public hospital sites in Edmonton, Canada since 2015. Since then, demand maintenance grown. This required creative solutions safe, accessible, evidence-based patient care. Objectives Aims this paper twofold. First, we will provide a synthesis current knowledge with regards the clinical use TRD. Consideration given regarding; off-label racemic uses versus FDA-approved esketamine, populations treated, inclusion/exclusion criteria, dosing, assessing response, concomitant medications, tolerability/safety. Second, describe our experience as community case study applying treatment. We application literature review programming, particular focus on cost-effective treatments, long-term safety concerns, routes administration other than via intravenous, cautious prescribing outside clinically monitored settings. Methodology conducted TRD up June 30, 2023. Key findings reviewed, their program. Conclusion Evidence resistant grown recent years, evolving data support direct its use. There an increasing body evidence guide judicious various circumstances, population patients high burden suffering morbidity. While large-scale, randomized controlled trials, comparative studies, longer-term treatment outcomes lacking, illustrates that currently available can applied real-world settings complex patients. As cost often significant barrier accessing initial or programs may incorporate SL IN sustainable accessible model. Three such models described.

Язык: Английский

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Anhedonia in bipolar depression treated with ketamine

Bipolar Disorders, Год журнала: 2024, Номер 26(4), С. 356 - 363

Опубликована: Фев. 4, 2024

Abstract Background Bipolar depression is the major cause of morbidity in patients with bipolar disorder. It affects psychosocial functioning and markedly impairs occupational productivity. Anhedonia one most debilitating symptoms contributing to treatment resistance. correlates suicidality, low quality life, social withdrawal, poor response. Currently, there no approved specifically targeting anhedonia. Emerging evidence suggests that ketamine possesses anti‐anhedonic properties individuals depression. Objectives The aim this naturalistic open‐label study was investigate effect add‐on on anhedonia resistant Methods Our main interest change patient‐reported (Snaith‐Hamilton Pleasure Scale) rater‐based measure (Montgomery–Åsberg Depression Rating Scale‐anhedonia subscale). secondary analyze score three Inventory Depressive Symptomatology‐Self Report (IDS‐SR) domains: mood/cognition, anxiety/somatic, sleep. Patients underwent assessments at several time points, including baseline, after third, fifth, seventh infusions. Additionally, a follow‐up assessment conducted 1 week following final administration. Results We found improvement according both measures. IDS‐SR domains prominent anxiety/somatic factor mood/cognition factor, sleep not observed. No serious adverse events occurred. Conclusion Add‐on seems be good choice for also showed reducing anxiety group patients. Considering unmet needs detrimental anxiety, more studies are needed

Язык: Английский

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The Role of Artificial Intelligence in Managing Bipolar Disorder: A New Frontier in Patient Care

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(7), С. 2515 - 2515

Опубликована: Апрель 7, 2025

Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients healthcare providers. Traditional treatment methods, including medication therapy, remain vital, but there increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has potential improve mood episode prediction, personalize plans, provide real-time support, offering new opportunities managing more effectively. Our primary objective was explore role transforming management BD, specifically tracking, personalized regimens. Methods: To management, we conducted review recent literature using key search terms. We included studies discussed applications personalization. The were selected based on their relevance AI's with attention PICO criteria: Population-individuals diagnosed BD; Intervention-AI tools personalization, support; Comparison-traditional methods (when available); Outcome-measures effectiveness, improvements patient care. Results: findings from research reveal promising developments use Studies suggest AI-powered can enable proactive care, improving outcomes reducing burden professionals. ability analyze data wearable devices, smartphones, even social media platforms provides valuable insights early detection dynamic adjustments. Conclusions: While still its stages, it presents transformative However, further development are crucial fully realize supporting optimizing efficacy.

Язык: Английский

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Proximity-based solutions for optimizing autism spectrum disorder treatment: integrating clinical and process data for personalized care

Frontiers in Psychiatry, Год журнала: 2025, Номер 15

Опубликована: Янв. 22, 2025

Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, presenting challenges in social communication, repetitive behaviors, and sensory processing. Despite its prevalence, diagnosis can be lengthy, access to appropriate treatment varies greatly. This project utilizes the power Artificial Intelligence (AI), particularly Machine Learning (ML) Deep (DL), improve treatment. A central data hub, Master Data Plan (MDP), will aggregate analyze information from diverse sources, feeding AI algorithms that identify risk factors for ASD, personalize plans based on individual needs, even predict potential relapses. Furthermore, incorporates a patient-facing chatbot provide support. By integrating patient data, empowering with supporting healthcare professionals, this platform aims transform care accessibility, approaches, optimize entire journey. Rigorous governance measures ensure ethical secure management. care, treatments better outcomes, shorten wait times, boost involvement, raise ASD awareness, leading resource allocation. marks transformative shift toward data-driven, patient-centred Italy. enhances outcomes provides scalable model into mental health, establishing new benchmark personalized care. Through integration collaborative efforts, it redefine standards, enhancing well-being ASD.

Язык: Английский

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Application of the Random Forest Algorithm for Accurate Bipolar Disorder Classification

Life, Год журнала: 2025, Номер 15(3), С. 394 - 394

Опубликована: Март 3, 2025

Bipolar disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression, posing significant challenges for accurate timely diagnosis. This study explores the use Random Forest (RF) algorithm as machine learning approach to classify patients with BD healthy controls based on electroencephalogram (EEG) data. A total 330 participants, including euthymic controls, were analyzed. EEG recordings processed extract key features, power in frequency bands complexity metrics such Hurst Exponent, which measures persistence or randomness time series, Higuchi’s Fractal Dimension, used quantify irregularity brain signals. The RF model demonstrated robust performance, achieving an average accuracy 93.41%, recall specificity exceeding 93%. These results highlight algorithm’s capacity handle complex, noisy datasets while identifying features relevant classification. Importantly, provided interpretable insights into physiological markers associated BD, reinforcing clinical value diagnostic tool. findings suggest that reliable accessible method supporting diagnosis complementing traditional practices. Its ability reduce delays, improve classification accuracy, optimize resource allocation make it promising tool integrating artificial intelligence care. represents step toward precision psychiatry, leveraging technology understanding management mental health disorders.

Язык: Английский

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Esketamine Nasal Spray Compassionate Use Program in the Netherlands: an open Label, Multi-Center Cohort Study in Severe Treatment-Resistant Depression

Journal of Affective Disorders Reports, Год журнала: 2025, Номер unknown, С. 100905 - 100905

Опубликована: Апрель 1, 2025

Язык: Английский

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Efficacy of perioperative esketamine on postoperative depression: a systematic review and meta-analysis

Frontiers in Psychiatry, Год журнала: 2025, Номер 16

Опубликована: Апрель 15, 2025

Background Postoperative depression (POD) represents a serious complication in surgical patients, exacerbating morbidity and mortality rates while imposing substantial economic burden on healthcare systems. Despite its widespread clinical use, the role of esketamine, an NMDA receptor antagonist with rapid antidepressant effects, remains understudied perioperative settings. Therefore, we conducted systematic review meta-analysis to assess efficacy esketamine postoperative depression. To evaluate effect incidence severity different types surgery by randomized controlled trial, investigate whether can effectively reduce score short long term after promote application analgesia-antidepressant combination. Method Searched PubMed, Cochrane Library, Web Science, Medline identify trials using drug analyzed data Review Manager 5.3. Results We included total 8 involving 1724 patients who met criteria. The revealed that treatment, compared control groups, significantly reduced POD. Improvements were observed at 1 week (RD -0.09, 95% CI [-0.13, -0.05], P < 0.0001, I²=84%), 2 weeks -0.08, -0.03], 0.00001, I²=97%), long-term follow-up -0.06, [-0.10, -0.02], P=0.0002, I²=79%). Conclusion Esketamine demonstrates reducing POD severity, although use is associated increased risk adverse effects. Also, method injection, duration administration number doses may have results. further exploration appropriate dosing regimens multi-modal strategies necessary mitigate Systematic registration https://www.crd.york.ac.uk/PROSPERO/ , identifier CRD42024506329.

Язык: Английский

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