A network meta-analysis of non-invasive brain stimulation interventions for autism spectrum disorder: Evidence from randomized controlled trials.

The efficacy and acceptability of various non-invasive brain stimulation (NIBS) interventions for autism spectrum disorder remain unclear. We carried out a systematic review for randomized controlled trials (RCTs) regarding NIBS for reducing autistic symptoms (INPLASY202370003). Sixteen articles (N = 709) met the inclusion criteria for network meta-analysis. Effect sizes were reported as standardized mean differences (SMDs) or odds ratios with 95 % confidence intervals (CIs). Fourteen active NIBS interventions, including transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation, and transcranial pulse stimulation were analyzed. Only anodal tDCS over the left dorsolateral prefrontal cortex paired with cathodal tDCS over an extracephalic location (atDCS_F3 + ctDCS_E) significantly improved autistic symptoms compared to sham controls (SMD = - 1.40, 95 %CIs = - 2.67 to - 0.14). None of the NIBS interventions markedly improved social-communication symptoms or restricted/repetitive behaviors in autistic participants. Moreover, no active NIBS interventions exhibited significant dropout rate differences compared to sham controls, and no serious adverse events were reported for any intervention.

Efficacy and acceptability of noninvasive brain stimulation for treating posttraumatic stress disorder symptoms: A network meta-analysis of randomized controlled trials.

INTRODUCTION: Despite its high lifetime prevalence rate and the elevated disability caused by posttraumatic stress disorder (PTSD), treatments exhibit modest efficacy. In consideration of the abnormal connectivity between the dorsolateral prefrontal cortex (DLPFC) and amygdala in PTSD, several randomized controlled trials (RCTs) addressing the efficacy of different noninvasive brain stimulation (NIBS) modalities for PTSD management have been undertaken. However, previous RCTs have reported inconsistent results. The current network meta-analysis (NMA) aimed to compare the efficacy and acceptability of various NIBS protocols in PTSD management. METHODS: We systematically searched ClinicalKey, Cochrane Central Register of Controlled Trials, Embase, ProQuest, PubMed, ScienceDirect, Web of Science, and ClinicalTrials.gov to identify relevant RCTs. The targeted RCTs was those comparing the efficacy of NIBS interventions, such as transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation (rTMS), and transcutaneous cervical vagal nerve stimulation, in patients with PTSD. The NMA was conducted using a frequentist model. The primary outcomes were changes in the overall severity of PTSD and acceptability (to be specific, rates of dropouts for any reason). RESULTS: We identified 14 RCTs that enrolled 686 participants. The NMA demonstrated that among the investigated NIBS types, high-frequency rTMS over bilateral DLPFCs was associated with the greatest reduction in overall PTSD severity. Further, in comparison with the sham controls, excitatory stimulation over the right DLPFC with/without excitatory stimulation over left DLPFC were associated with significant reductions in PTSD-related symptoms, including depression and anxiety symptoms, and overall PTSD severity. CONCLUSIONS: This NMA demonstrated that excitatory stimulation over the right DLPFC with or without excitatory stimulation over left DLPFC were associated with significant reductions in PTSD-related symptoms. TRIAL REGISTRATION: PROSPERO CRD42023391562.

Comparing different non-invasive brain stimulation interventions for bipolar depression treatment: A network meta-analysis of randomized controlled trials.

Non-invasive brain stimulation (NIBS) is a promising treatment for bipolar depression. We systematically searched for randomized controlled trials on NIBS for treating bipolar depression (INPLASY No: 202340019). Eighteen articles (N = 617) were eligible for network meta-analysis. Effect sizes were reported as standardized mean differences (SMDs) or odds ratios (ORs) with 95% confidence intervals (CIs). Anodal transcranial direct current stimulation over F3 plus cathodal transcranial direct current stimulation over F4 (a-tDCS-F3 +c-tDCS-F4; SMD = -1.18, 95%CIs = -1.66 to -0.69, N = 77), high-definition tDCS over F3 (HD-tDCS-F3; -1.17, -2.00 to -0.35, 25), high frequency deep transcranial magnetic stimulation (HF-dTMS; -0.81, -1.62 to -0.001, 25), and high frequency repetitive TMS over F3 plus low frequency repetitive TMS over F4 (HF-rTMS-F3 +LF-rTMS-F4; -0.77, -1.43 to -0.11, 38) significantly improved depressive symptoms compared to sham controls. Only a-tDCS-F3 +c-tDCS-F4 (OR = 4.53, 95%CIs = 1.51-13.65) and HF-rTMS-F3 +LF-rTMS-F4 (4.69, 1.02-21.56) showed higher response rates. No active NIBS interventions exhibited significant differences in dropout or side effect rates, compared with sham controls.

Valproic acid monitoring: Serum prediction using a machine learning framework from multicenter real-world data.

BACKGROUND: Our study employs machine learning to predict serum valproic acid (VPA) concentrations, aiming to contribute to the development of non-invasive assays for therapeutic drug monitoring. METHODS: Medical records from 2002 to 2019 were obtained from the Taiwan Chang Gung Research Database. Using various machine learning algorithms, we developed predictive models to classify serum VPA concentrations into two categories (1-50 µg/ml or 51-100 µg/ml) and predicted the exact concentration value. The models were trained on 5142 samples and tested on 644 independent samples. Accuracy was the main metric used to evaluate model performance, with a tolerance of 20 µg/ml for continuous variables. Furthermore, we identified important features and developed simplified models with fewer features. RESULTS: The models achieved an average accuracy of 0.80-0.86 for binary outcomes and 0.72-0.88 for continuous outcome. Ten top features associated with higher serum VPA levels included higher VPA last and daily doses, bipolar disorder or schizophrenia spectrum disorder diagnoses, elevated levels of serum albumin, calcium, and creatinine, low platelet count, low percentage of segmented white blood cells, and low red cell distribution width-coefficient of variation. The simplified models had an average accuracy of 0.82-0.86 for binary outcome and 0.70-0.86 for continuous outcome. LIMITATIONS: The study's predictive model lacked external test data from outside the hospital for validation. CONCLUSIONS: Machine learning models have the potential to integrate real-world data and predict VPA concentrations, providing a promising tool for reducing the need for frequent monitoring of serum levels in clinical practice.

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm.

Introduction: Post-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data. Methods: We collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models' performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models. Results: In the study's database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83-0.91 and 0.30-0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke. Discussion: Machine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients.

Comparative effectiveness of valproic acid in different serum concentrations for maintenance treatment of bipolar disorder: A retrospective cohort study using target trial emulation framework.

Background: There is a lack of consensus on the optimal serum valproic acid (VPA) concentration for maintenance therapy in bipolar disorder (BD). We aimed to investigate the association between serum VPA levels and risk of mood episode recurrence. Methods: We enrolled patients with BD from multiple medical institutions in Taiwan between January 1, 2001 and December 31, 2019. Patients were divided into three groups according to their serum VPA concentrations (< 50 µg/ml, 50-74 µg/ml, and 75-104 µg/ml). Adjusted hazard ratios (aHR) with 95% confidence intervals (CIs) compared times to mood episode recurrence using the < 50 µg/ml group as reference. A systematic review found relevant articles published before February 2022 (PROSPERO: CRD42022309661), and corresponding results were compared. Findings: This cohort included 896 patients for an intention-to-treat analysis. Compared with the < 50 µg/ml group, a non-significantly lower risk of mood episode recurrence was found in the 50-74 µg/ml (aHR, 95% CI: 0·86, 0·71-1·05) and 75-104 µg/ml (0·91, 0·71-1·18) groups. A per-protocol analysis of 481 patients found a significant risk reduction in the 50-74 µg/ml group (0·76, 0·60-0·97), with inconclusive results in the ≥ 75 µg/ml group (1·03, 0·73-1·46). A meta-analysis including two studies (254 patients) and our cohort found a similar significantly lower risk of mood episode recurrence in the 50-74 µg/ml group (HR, 95% CI: 0·83, 0·69-0·99), while risk reduction in the 75-99 µg/ml group (0·62, 0·26-1·48) did not differ significantly from that in the < 50 µg/ml group or the 50-74 µg/ml group. Interpretation: The combined results of this cohort and previous studies suggest that VPA between 50-74 µg/ml may be a more effective concentration to prevent acute mood episodes during maintenance therapy in patients with BD compared with VPA < 50 µg/ml. Funding: Ministry of Science and Technology, Taiwan, and Chang Gung Medical Research Project.