Consistency checks to improve measurement with the Hamilton Rating Scale for Anxiety (HAM-A)

Integrating EEG and Ensemble Learning for Accurate Grading and Quantification of Generalized Anxiety Disorder: A Novel Diagnostic Approach

Current assessments for generalized anxiety disorder (GAD) are often subjective and do not rely on a standardized measure to evaluate the GAD across its severity levels. The lack of objective and multi-level quantitative diagnostic criteria poses as a significant challenge for individualized treatment strategies. To address this need, this study aims to establish a GAD grading and quantification diagnostic model by integrating an electroencephalogram (EEG) and ensemble learning. In this context, a total of 39 normal subjects and 80 GAD patients were recruited and divided into four groups: normal control, mild GAD, moderate GAD, and severe GAD. Ten minutes resting state EEG data were collected for every subject. Functional connectivity features were extracted from each EEG segment with different time windows. Then, ensemble learning was employed for GAD classification studies and brain mechanism analysis. Hence, the results showed that the Catboost model with a 10 s time window achieved an impressive 98.1% accuracy for four-level classification. Particularly, it was found that those functional connections situated between the frontal and temporal lobes were significantly more abundant than in other regions, with the beta rhythm being the most prominent. The analysis framework and findings of this study provide substantial evidence for the applications of artificial intelligence in the clinical diagnosis of GAD.

Consistency checks to improve measurement with the Young Mania Rating Scale (YMRS)

BACKGROUND

Mitigating rating inconsistency can improve measurement fidelity and detection of treatment response.

METHODS

The International Society for CNS Clinical Trials and Methodology convened an expert Working Group that developed logical consistency (LC) checks for ratings of the Young Mania Rating Scale (YMRS), which is widely used in studies of mood and bipolar disorders. LC and statistical outlier-response pattern checks (SC) were applied to 63,228 YMRS administrations from 14 clinical trials evaluating treatments for bipolar disorder. Checks were also applied to Monte Carlo-simulated data as a proxy for their use under conditions of inconsistency.

RESULTS

42 LC flags were developed, and four SC flags were created from the data set (n = 14). Almost 20 % of the rating administrations had at least one LC flag, 6.7 % had two or more, 1.7 % had three or more; 17.3 % percent of the administrations had at least one SC flag and 4.6 % percent had two or more. Overall, 31 % of administrations had at least one flag of any type, 12.1 % had two or more and 5.3 % had three or more. In acute antimanic treatment trials (n = 10) there were more flags of any type compared to relapse prevention trials (n = 4).

LIMITATIONS

Flagged ratings may represent less-common presentations assessed correctly.

CONCLUSIONS

Using established methods, we illustrate development and application of consistency flags for YMRS ratings. Applying flags and mitigation during trials may improve the value of YMRS data, help focus attention on rater training, and improve reliability and validity of trial data.

A Hierarchical Multi-Dimensional Cognitive Training Program for Preventive Cognitive Decline in Acute Ischemic Stroke Patients: Study Protocol for a Randomized Controlled Trial

Background

One of the most popular ways to address cognitive decline is cognitive training. The fact that cognitive deterioration is permanent is one of the main issues. This issue might be resolved by preventive cognitive training when it is acute. As a result, this study aims to design and assess how well stroke patients respond to hierarchical, multi-dimensional preventative cognitive training.

Objective

To describe the study design of this center implementation trial.

Methods

Participants in the study will be recruited from a hospital in China and randomly assigned to the intervention group or the usual care group. Interventions will include four-week hierarchical multi-dimensional preventive cognitive training through a WeChat program. for Primary outcome measures will be the Montreal Cognitive Assessment, Mini-Mental State Examination, and Post-Stroke Cognitive Impairment (PSCI) Incidence. The secondary outcome measure will include the Hamilton Depression Scale, Hamilton Anxiety Scale, Modified Barthel Index, and National Institutes of Health Neurological Deficit Score. Outcomes will be measured at baseline, 12 weeks, and 24 weeks from the baseline.

Results

We expect that the hierarchical multi-dimensional preventive cognitive training program will be easy to implement, and the cognitive function, cognitive psychology, ability of daily living will vary in each setting.

Conclusions

The results will provide evidence highlighting differences in a new strategy of cognitive training through the WeChat program, which allows the home-based practice, puts forward an advanced idea of preventive cognitive training in the acute stage, and has the highest effectiveness of reducing cognitive impairment, and Alzheimer's disease.