Preventive effect of aripiprazole once-monthly on relapse into mood episodes in bipolar disorder: A multicenter, one-year, retrospective, mirror image study.

BACKGROUND: We conducted a one-year, retrospective, mirror-image study to investigate the clinical effectiveness and safety of aripiprazole once monthly (AOM) in patients with bipolar disorder (BD). We compared pre-treatment conditions with outcomes after 12 months of AOM treatment. METHODS: Seventy-five bipolar patients were recruited from 12 hospitals in Korea. We included 75 patients with BD who had received at least three AOM treatments from September 2019 to September 2022 and had accessible electronic medical record (EMRs) for the year before and after the baseline visit. RESULTS: The overall number of mood episodes significantly decreased from a mean of 1.5 ± 1.2 episodes pre-AOM to 0.5 ± 1.2 episodes post-AOM. Manic episodes significantly decreased from 0.8 ± 0.8 episodes pre-AOM to 0.2 ± 0.5 episodes post-AOM, and depressive episodes significantly decreased from 0.5 ± 0.8 episodes pre-AOM to 0.2 ± 0.6 episodes post-AOM (p = 0.017). Moreover, the number of psychiatric medications and pills and the proportion of patients treated with complex polypharmacy were significantly decreased post-AOM. LIMITATIONS: The small sample size was insufficient to fully represent the entire population of individuals with BD, and potential selection bias was introduced due to only including subjects who received AOM three or more times. CONCLUSION: The results of this study suggest that AOM can reduce mood episode relapse and may be clinically beneficial in the treatment of BD patients, potentially reducing issues associated with polypharmacy in some individuals.

Deep Learning Denoising of Low-Dose Computed Tomography Chest Images: A Quantitative and Qualitative Image Analysis.

PURPOSE: To assess deep learning denoised (DLD) computed tomography (CT) chest images at various low doses by both quantitative and qualitative perceptual image analysis. METHODS: Simulated noise was inserted into sinogram data from 32 chest CTs acquired at 100 mAs, generating anatomically registered images at 40, 20, 10, and 5 mAs. A DLD model was developed, with 23 scans selected for training, 5 for validation, and 4 for test.Quantitative analysis of perceptual image quality was assessed with Structural SIMilarity Index (SSIM) and Fréchet Inception Distance (FID). Four thoracic radiologists graded overall diagnostic image quality, image artifact, visibility of small structures, and lesion conspicuity. Noise-simulated and denoised image series were evaluated in comparison with one another, and in comparison with standard 100 mAs acquisition at the 4 mAs levels. Statistical tests were conducted at the 2-sided 5% significance level, with multiple comparison correction. RESULTS: At the same mAs levels, SSIM and FID between noise-simulated and reconstructed DLD images indicated that images were closer to a perfect match with increasing mAs (closer to 1 for SSIM, and 0 for FID).In comparing noise-simulated and DLD images to standard-dose 100-mAs images, DLD improved SSIM and FID. Deep learning denoising improved SSIM of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in SSIM from 0.91 to 0.94, 0.87 to 0.93, 0.67 to 0.87, and 0.54 to 0.84, respectively. Deep learning denoising improved FID of 40-, 20-, 10-, and 5-mAs simulations in comparison with standard-dose 100-mAs images, with change in FID from 20 to 13, 46 to 21, 104 to 41, and 148 to 69, respectively.Qualitative image analysis showed no significant difference in lesion conspicuity between DLD images at any mAs in comparison with 100-mAs images. Deep learning denoising images at 10 and 5 mAs were rated lower for overall diagnostic image quality ( P < 0.001), and at 5 mAs lower for overall image artifact and visibility of small structures ( P = 0.002), in comparison with 100 mAs. CONCLUSIONS: Deep learning denoising resulted in quantitative improvements in image quality. Qualitative assessment demonstrated DLD images at or less than 10 mAs to be rated inferior to standard-dose images.

Methods and Impact for Using Federated Learning to Collaborate on Clinical Research.

BACKGROUND: The development of accurate machine learning algorithms requires sufficient quantities of diverse data. This poses a challenge in health care because of the sensitive and siloed nature of biomedical information. Decentralized algorithms through federated learning (FL) avoid data aggregation by instead distributing algorithms to the data before centrally updating one global model. OBJECTIVE: To establish a multicenter collaboration and assess the feasibility of using FL to train machine learning models for intracranial hemorrhage (ICH) detection without sharing data between sites. METHODS: Five neurosurgery departments across the United States collaborated to establish a federated network and train a convolutional neural network to detect ICH on computed tomography scans. The global FL model was benchmarked against a standard, centrally trained model using a held-out data set and was compared against locally trained models using site data. RESULTS: A federated network of practicing neurosurgeon scientists was successfully initiated to train a model for predicting ICH. The FL model achieved an area under the ROC curve of 0.9487 (95% CI 0.9471-0.9503) when predicting all subtypes of ICH compared with a benchmark (non-FL) area under the ROC curve of 0.9753 (95% CI 0.9742-0.9764), although performance varied by subtype. The FL model consistently achieved top three performance when validated on any site's data, suggesting improved generalizability. A qualitative survey described the experience of participants in the federated network. CONCLUSION: This study demonstrates the feasibility of implementing a federated network for multi-institutional collaboration among clinicians and using FL to conduct machine learning research, thereby opening a new paradigm for neurosurgical collaboration.

Deploying deep learning models on unseen medical imaging using adversarial domain adaptation.

The fundamental challenge in machine learning is ensuring that trained models generalize well to unseen data. We developed a general technique for ameliorating the effect of dataset shift using generative adversarial networks (GANs) on a dataset of 149,298 handwritten digits and dataset of 868,549 chest radiographs obtained from four academic medical centers. Efficacy was assessed by comparing area under the curve (AUC) pre- and post-adaptation. On the digit recognition task, the baseline CNN achieved an average internal test AUC of 99.87% (95% CI, 99.87-99.87%), which decreased to an average external test AUC of 91.85% (95% CI, 91.82-91.88%), with an average salvage of 35% from baseline upon adaptation. On the lung pathology classification task, the baseline CNN achieved an average internal test AUC of 78.07% (95% CI, 77.97-78.17%) and an average external test AUC of 71.43% (95% CI, 71.32-71.60%), with a salvage of 25% from baseline upon adaptation. Adversarial domain adaptation leads to improved model performance on radiographic data derived from multiple out-of-sample healthcare populations. This work can be applied to other medical imaging domains to help shape the deployment toolkit of machine learning in medicine.

Impact of Interpersonal Relationships and Acquired Capability for Suicide on Suicide Attempts: A Cross-Sectional Study.

OBJECTIVE: This study examined the path model predicting suicide attempts (SA) by interpersonal need for suicide desire, acquired capability for suicide, the emotion dysregulation, and depression symptoms in people admitted to hospitals for medical treatment. METHODS: A total of 344 participants (200 depressed patients with attempted suicide, 144 depressed patients with suicidal ideation) were enrolled for this study. Depression, anxiety, emotion regulation, interpersonal needs, and acquired capability for suicide were evaluated. A model with pathways from emotion regulation difficulties and interpersonal needs to SA was proposed. Participants were divided into two groups according to the presence of SA or suicidal ideation. RESULTS: Acquired capability for suicide mediated the path from depression to SA. In the path model, difficulties in emotion regulation and interpersonal needs predicted depression significantly. Although depression itself was not significantly related to acquired capability for suicide, depression was significantly related to acquired capability for suicide in suicide attempter group. CONCLUSION: Interventions with two factors affecting SA will clarify the suicide risk and contribute to finding risk factors.

Alteration of cortical functional networks in mood disorders with resting-state electroencephalography.

Studies comparing bipolar disorder (BD) and major depressive disorder (MDD) are scarce, and the neuropathology of these disorders is poorly understood. This study investigated source-level cortical functional networks using resting-state electroencephalography (EEG) in patients with BD and MDD. EEG was recorded in 35 patients with BD, 39 patients with MDD, and 42 healthy controls (HCs). Graph theory-based source-level weighted functional networks were assessed via strength, clustering coefficient (CC), and path length (PL) in six frequency bands. At the global level, patients with BD and MDD showed higher strength and CC, and lower PL in the high beta band, compared to HCs. At the nodal level, compared to HCs, patients with BD showed higher high beta band nodal CCs in the right precuneus, left isthmus cingulate, bilateral paracentral, and left superior frontal; however, patients with MDD showed higher nodal CC only in the right precuneus compared to HCs. Although both MDD and BD patients had similar global level network changes, they had different nodal level network changes compared to HCs. Our findings might suggest more altered cortical functional network in patients with BD than in those with MDD.

Electrophysiological Changes Related to Childhood Trauma in Patients with Major Depressive Disorder: An Event-related Potential Study.

OBJECTIVE: Childhood trauma is the most important environmental factor for several psychiatric disorders. Depressed patients with childhood trauma appear to have severe symptoms that frequently recur. This study investigated whether depressed patients with childhood trauma showed attenuated Nogo event-related potentials (ERPs) and source activity during response-inhibition tasks. METHODS: Forty-four patients patients with major depressive disorder (MDD) were instructed to perform a Go/Nogo task during electroencephalography. Sensors and source activities of N2 and P3 of the Nogo ERPs were analyzed. The participants' clinical symptoms were assessed using the Childhood Trauma Questionnaire (CTQ), Beck Depression Inventory, State-Trait Anxiety Inventory, Barratt Impulsivity Scale, and Affective Lability Scale. The participants were divided into two groups (low and high), based on their total CTQ scores. RESULTS: MDD subjects with high CTQ scores showed significantly decreased Nogo P3 amplitudes at the frontal, frontocentral, central, and parietal electrodes than those with low CTQ scores (all p < 0.01). In Nogo P3, the source activities of the right cuneus, right posterior cingulate cortex, right precuneus, left supramarginal gyrus, and left lingual gyrus were significantly lower in the high CTQ group than in the low one (all p < 0.01). There were significant negative correlations between the total CTQ scores and the Nogo P3 amplitudes in the frontocentral (p = 0.03) and parietal regions (p = 0.02), which showed lower source activity in the Nogo P3 condition. CONCLUSION: Depressed patients with severe childhood trauma showed different Nogo-ERP characteristics, which might reflect inhibitory failure and dysfunction in related brain regions.

Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity.

Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01% accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN reflects the adult ADHD patients' pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD.

Auditory evoked potentials and suicidal behaviors in patients with major depressive disorders.

Loudness dependence of auditory evoked potentials (LDAEP) has been proposed as a biological marker of central serotonergic activity related to suicides. This study's objective was to analyze the difference in LDAEP between depressed patients with suicide attempts (SA) and suicidal ideation (SI). It included 130 participants (45 depressed patients with SA, 49 depressed patients with SI, and 36 healthy controls) aged > 18 years who exhibited LDAEP during electroencephalography. Psychological characteristics and event-related potentials of the three groups were compared. There was no significant difference in LDAEP between major depressive disorder (MDD) patients with SA and SI (p = 0.59). MDD patients with SI, who attempted suicide had significantly lower LDAEP than healthy controls (p = 0.01 and p = 0.01, respectively). However, the significance disappeared when psychological characteristics were controlled. Our results suggest that LDAEP might not be possible biomarkers for suicidal behaviors in patients with MDD. Further studies to assess the biological basis of suicide and identify the underlying dimensions that mediate the relationship between the biological basis and suicidal behaviors will be needed.

Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department.

PURPOSE: To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338; median age, 39 years; 210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161; median age, 60 years; 98 men) for both younger (age range, 21-50 years; n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs. RESULTS: The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. CONCLUSION: The combination of imaging and clinical information improves outcome predictions.Supplemental material is available for this article.© RSNA, 2020.

Combinatorial glucose, nicotinic acid and N-acetylcysteine therapy has synergistic effect in preclinical C. elegans and zebrafish models of mitochondrial complex I disease.

Mitochondrial respiratory chain disorders are empirically managed with variable antioxidant, cofactor and vitamin 'cocktails'. However, clinical trial validated and approved compounds, or doses, do not exist for any single or combinatorial mitochondrial disease therapy. Here, we sought to pre-clinically evaluate whether rationally designed mitochondrial medicine combinatorial regimens might synergistically improve survival, health and physiology in translational animal models of respiratory chain complex I disease. Having previously demonstrated that gas-1(fc21) complex I subunit ndufs2-/-C. elegans have short lifespan that can be significantly rescued with 17 different metabolic modifiers, signaling modifiers or antioxidants, here we evaluated 11 random combinations of these three treatment classes on gas-1(fc21) lifespan. Synergistic rescue occurred only with glucose, nicotinic acid and N-acetylcysteine (Glu + NA + NAC), yielding improved mitochondrial membrane potential that reflects integrated respiratory chain function, without exacerbating oxidative stress, and while reducing mitochondrial stress (UPRmt) and improving intermediary metabolic disruptions at the levels of the transcriptome, steady-state metabolites and intermediary metabolic flux. Equimolar Glu + NA + NAC dosing in a zebrafish vertebrate model of rotenone-based complex I inhibition synergistically rescued larval activity, brain death, lactate, ATP and glutathione levels. Overall, these data provide objective preclinical evidence in two evolutionary-divergent animal models of mitochondrial complex I disease to demonstrate that combinatorial Glu + NA + NAC therapy significantly improved animal resiliency, even in the face of stressors that cause severe metabolic deficiency, thereby preventing acute neurologic and biochemical decompensation. Clinical trials are warranted to evaluate the efficacy of this lead combinatorial therapy regimen to improve resiliency and health outcomes in human subjects with mitochondrial disease.

Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

BACKGROUND: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. OBJECTIVE: We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. METHODS: Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. RESULTS: The LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. CONCLUSIONS: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Altered cortical functional network in drug-naive adult male patients with attention-deficit hyperactivity disorder: A resting-state electroencephalographic study.

Relatively little is known about the neurophysiology of adult Attention-deficit/hyperactivity disorder (ADHD). Brain network analysis can yield important insights into the neuropathology in adult ADHD. The objective of this study was to investigate source-level cortical functional network using resting-state electroencephalography (EEG) in drug-naive adult patients with ADHD. Resting-state EEG was performed for 30 adult male patients with ADHD and 27 male healthy controls. Source-level weighted functional networks based on graph theory were evaluated, including strength, clustering coefficient (CC) and path length (PL) in six frequency bands. At the global level, strength (η2 = 0.167) and CC (η2 = 0.156) were lower while PL (η2 = 0.159) was higher for the high beta band in the ADHD patient group compared to healthy controls. At the nodal level, CCs of the high beta band were lower in the left middle temporal gyrus (η2 = 0.244), right inferior occipital cortex (η2 = 0.214), right posterior transverse collateral sulcus (η2 = 0.237), and right anterior occipital sulcus (η2 = 0.251) for the adult ADHD group. Furthermore, the nodal-level high beta band CCs of the left middle temporal gyrus and right anterior occipital sulcus were significantly negatively correlated with ADHD symptoms. The altered cortical functional network showed inefficient connectivity in the left middle temporal gyrus, belonging to the default mode network, the right inferior occipital cortex, belonging to the extrastriate visual resting state network, the right posterior transverse collateral sulcus, belonging to the visual network, and the anterior occipital sulcus, reflecting visual attention, which might affect the pathophysiology of ADHD. Taken together, these attenuated network inefficiencies in adult patients with ADHD may lead to suboptimal information processing and affect symptoms of ADHD, such as inattention and hyperactivity. Our findings should be further replicated using longitudinal study designs.

The Impact of Emotional Exhaustion on Psychological Factors in Workers with Secondary Traumatic Experiences: A Multi-Group Path Analysis.

OBJECTIVE: The objective of the present study was to explore causal pathways to understand how second traumatic experiences could affect the development of emotional exhaustion and psychiatric problems. METHODS: A total of 582 workers who had jobs vulnerable to secondary traumatic experiences were enrolled for this study. Emotional exhaustion, secondary trauma, resilience, perceived stress, depression, anxiety, and sleep problems were evaluated. A model with pathways from secondary traumatic experience score to depression and anxiety was proposed. The participants were divided into three groups according to the resilience: the low, middle and high resilience group. RESULTS: Resilience was a meaningful moderator between secondary traumatic experiences and psychiatric problems. In the path model, the secondary trauma and perceived stress directly and indirectly predicted perceived stress, emotional exhaustion, depression, anxiety, and sleep problems in all three groups. Direct effects of perceived stress on depression and anxiety were the largest in the low resilience group. However, direct effects of secondary trauma on perceived stress and emotional exhaustion were the largest in the high resilience group. CONCLUSION: Understanding the needs of focusing for distinct psychological factors offers a valuable direction for the development of intervention programs to prevent emotional exhaustion among workers with secondary traumatic experiences.

The Relationship Between Response-Inhibitory Event-Related Potentials and Symptoms of Attention-Deficit/Hyperactivity Disorder in Adult Patients with Major Depressive Disorder.

OBJECTIVE: Attention-deficit and poor impulse control have frequently been observed in major depressive disorder (MDD) and attention-deficit and hyperactivity disorder (ADHD). Altered event-related potential (ERP) performance, such as GoNogo tasks, has been regarded as a neurocognitive process associated with attention and behavioral inhibition. The aim of this study was to investigate the association between Nogo ERP and adult ADHD in MDD. METHODS: A total of 64 participants with MDD (32 comorbid with ADHD) and 32 healthy controls aged 19-45 years were recruited; they performed GoNogo paradigms during electroencephalogram measurement. Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI), and the Adult ADHD Self-Report Scale (ASRS) were evaluated. Clinical measures and GoNogo ERP were compared between three groups: depression with ADHD, depression without ADHD, and healthy controls. RESULTS: MDD subjects with ADHD showed significantly decreased Nogo P3 amplitude at frontal electrode, compared with those without ADHD and healthy controls. MDD subjects with ADHD showed significantly longer Nogo N2 latency at frontal and frontocentral electrodes, compared with those without ADHD and healthy controls. In MDD subjects with ADHD, the Nogo P3 amplitude at the frontal electrode was negatively correlated with the ASRS score and inattention. The Nogo N2 latency at the frontal electrode was positively correlated with false alarm rate. CONCLUSION: The decreased Nogo P3 amplitude in the frontal area might be a potential biological marker for inattention in depressed patients with ADHD.

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.

Mismatch negativity indices and functional outcomes in unipolar and bipolar depression.

The aim of the study was to explore the association between functional outcomes and mismatch negativity (MMN) activity in participants with mood disorders. The study participants were 27 subjects with major depressive disorder (MDD), 29 subjects with bipolar disorder (BD), and 33 healthy controls who performed a passive auditory oddball paradigm while electroencephalography (EEG) was recorded. Peak amplitudes and source activity of the MMN were compared across groups. Mood and anxiety symptoms were evaluated. The functional levels were the lowest in the BD group, followed by the MDD and healthy control groups. The subjects with BD had significantly lower MMN amplitudes at the frontal and frontocentral electrodes than the healthy controls. The source activity of the MMN from the left anterior cingulate cortex, inferior frontal gyrus, and middle frontal gyrus was significantly increased in the BD group compared to the MDD group. Significant correlations were detected between the functional outcomes and MMN amplitudes at frontal and frontocentral sites. The functional outcome was significantly correlated with left frontal regions. In conclusion, MMN activity appears to be a promising candidate as an evaluation tool for functional outcomes in mood disorders.

Mismatch Negativity Indices as a Prognostic Factor for Remission in Schizophrenia.

OBJECTIVE: Mismatch negativity (MMN) is known to be associated with neuro-cognition and functional outcomes. Remission and recovery rates are related to the neuro-cognition of patients with schizophrenia. The present study explored the relationship of MMN with remission in patients with schizophrenia. METHODS: Forty patients with schizophrenia were recruited and divided into two groups, with or without remission, according to the Remission in Schizophrenia Working Group criteria (RSWGcr). Symptom severity (Positive and Negative Syndrome Scale, PANSS), cognitive function, functional outcome, and MMN of the patients were evaluated. A regression analysis was used to identify the factors that significantly predicted symptom improvement and remission including MMN at frontal site assessed at baseline, and anticipated clinical variables as predictive factors. RESULTS: MMN amplitudes in frontal sites were further decreased in the groups without remission compared to the groups with remission. MMN amplitude was significantly correlated with measures of symptom change and functional outcome measurements in patients with schizophrenia. Regression analysis revealed that symptom severity and MMN significantly predicted remission in patients with schizophrenia. Symptom improvement significantly predicted PANSS at baseline, illness duration, and antipsychotic dose, as did MMN amplitude at frontal site. CONCLUSION: Our results suggest that MMN reflected symptom improvement and remission in patients with schizophrenia. MMN indices appear to be promising candidates as predictive factors for schizophrenia remission.

Clinical correlates associated with the long-term response of bipolar disorder patients to lithium, valproate or lamotrigine: A retrospective study.

BACKGROUND: Although mood stabilizers such as lithium (LIT), valproate (VAL), and lamotrigine (LMT) appear to be efficacious treatments for bipolar disorder (BD) in research settings, the long-term response to these mood stabilizers in clinical practice is highly variable among individuals. Thus, the present study examined the characteristics associated with good or insufficient responses to long-term treatment with LIT, VAL, or LMT for BD. METHODS: This study retrospectively analyzed the medical records of patients who visited an outpatient clinic with a diagnosis of BD I or II. Data from patients who were treated with one of three mood stabilizing medications (LIT, VAL, or LMT) for more than 6 months were selected, and the long-term treatment responses were evaluated using the Alda scale. For the purposes of this study, two response categories were formed: insufficient response (ISR), including non-response or poor response (Alda total score ≤ 6), and good response (GR; Alda total score ≥ 7). RESULTS: Of the 645 patients included in the present study, 172 were prescribed LIT, 320 were prescribed VAL, and 153 were prescribed LMT for at least 6 months. A binary logistic regression analysis revealed that a diagnosis of BD II (odds ratio [OR], 8.868; 95% confidence interval [CI], 1.123-70.046; p = 0.038), comorbid alcohol/substance use disorder (OR, 4.238; 95% CI, 1.154-15.566; p = 0.030), and a history of mixed episodes (OR, 4.363; 95% CI, 1.191-15.985; p = 0.026) were significant predictors of LIT-ISR. Additionally, a depressive-predominant polarity significantly predicted LMT-GR (OR, 8.586; 95% CI, 2.767-26.644; p < 0.001). CONCLUSION: The present findings demonstrated that patients with a diagnosis of BD II, a comorbid alcohol/substance problem, or a history of mixed episodes were not likely to respond to LIT treatment. Additionally, LMT might be a better treatment choice for patients with a depressive-predominant polarity.

Minithoracotomy and Beating Heart Strategy for Mitral Surgery in Secondary Mitral Regurgitation.

BACKGROUND: In patients with secondary mitral regurgitation (MR) associated with low ejection fraction or previous heart surgery, minimally invasive mitral valve surgery without aortic cross-clamp (MIMVS-WAC) has shown promising results. We report our experience for this strategy in our centers. METHODS: Between August 2011 and April 2017, 46 patients (mean age 69 ± 11 years, 76% males) received MIMVS-WAC. Indications for this technique were prior coronary bypass surgery (26%), severe or recent left ventricular (LV) dysfunction (30%), or both (39%). The mean EuroSCORE II was 12 ± 10. RESULTS: For each procedure, we conducted right minithoracotomy and hypothermic cardiopulmonary bypass (CPB) after peripheral cannulation. Mean CPB time was 159 ± 39 minutes. A mitral valve replacement (MVR) was performed in 23 cases (50%), an annuloplasty in 22 cases (48%), and a prosthesis pannus removal in 1 case (2%). Mean hospital length of stay was 12 ± 5.4 days. We report no sternotomy conversions, six reoperations for bleeding, and three deaths at 30 days. Transfusion was requested in 62% (mean infusion 2 ± 2.4 packed red blood cells). The postoperative echocardiography showed an LV function preservation in 69% of cases and a reduction of pulmonary arterial pressure in 73% of cases. Four additional deaths occurred in the long-term follow-up (mean 637 ± 381 days, median 593 days). No mitral reoperation was required, with a MR ≤ 2 in 90% of patients. CONCLUSION: In high-risk patients, the MIMVS-WAC is a safe technique. It avoids hard dissections while ensuring excellent preservation of cardiac function.