Efficacy and dose-response relationships of antidepressants in the acute treatment of major depressive disorders: a systematic review and network meta-analysis.

BACKGROUND: The optimal antidepressant dosages remain controversial. This study aimed to analyze the efficacy of antidepressants and characterize their dose-response relationships in the treatments of major depressive disorders (MDD). METHODS: We searched multiple databases, including the Embase, Cochrane Central Register of Controlled Trials, PubMed, and Web of Science, for the studies that were conducted between January 8, 2016, and April 30, 2023. The studies are double-blinded, randomized controlled trials (RCTs) involving the adults (≥18 years) with MDD. The primary outcomes were efficacy of antidepressant and the dose-response relationships. A frequentist network meta-analysis was conducted, treating participants with various dosages of the same antidepressant as a single therapy. We also implemented the model-based meta-analysis (MBMA) using a Bayesian method to explore the dose-response relationships. RESULTS: The network meta-analysis comprised 135,180 participants from 602 studies. All the antidepressants were more effective than the placebo; toludesvenlafaxine had the highest odds ratio (OR) of 4.52 (95% confidence interval [CI]: 2.65-7.72), and reboxetine had the lowest OR of 1.34 (95%CI: 1.14-1.57). Moreover, amitriptyline, clomipramine, and reboxetine showed a linear increase in effect size from low to high doses. The effect size of toludesvenlafaxine increased significantly up to 80 mg/day and subsequently maintained the maximal dose up to 160 mg/day while the predictive curves of nefazodone were fairly flat in different dosages. CONCLUSIONS: Although most antidepressants were more efficacious than placebo in treating MDD, no consistent dose-response relationship between any antidepressants was observed. For most antidepressants, the maximum efficacy was achieved at lower or middle prescribed doses, rather than at the upper limit.

Putative Risk Biomarkers of Bipolar Disorder in At-risk Youth.

Bipolar disorder is a highly heritable and functionally impairing disease. The recognition and intervention of BD especially that characterized by early onset remains challenging. Risk biomarkers for predicting BD transition among at-risk youth may improve disease prognosis. We reviewed the more recent clinical studies to find possible pre-diagnostic biomarkers in youth at familial or (and) clinical risk of BD. Here we found that putative biomarkers for predicting conversion to BD include findings from multiple sample sources based on different hypotheses. Putative risk biomarkers shown by perspective studies are higher bipolar polygenetic risk scores, epigenetic alterations, elevated immune parameters, front-limbic system deficits, and brain circuit dysfunction associated with emotion and reward processing. Future studies need to enhance machine learning integration, make clinical detection methods more objective, and improve the quality of cohort studies.

Adverse effects of 21 antidepressants on sleep during acute-phase treatment in major depressive disorder: a systemic review and dose-effect network meta-analysis.

STUDY OBJECTIVES: Sleep-related adverse effects during acute treatment with antidepressants undermine adherence and impede remission. We aimed to address subtypes of sleep-related adverse effects and depict the relationship between dose and sleep-related adverse events. METHODS: We searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science for double-blind randomized controlled trials of depression published before April 30th, 2023. Eligible studies reporting sleep-related adverse effects during short-term monotherapy were included. The odds ratios (ORs) for sleep-related adverse effects were addressed with network meta-analysis. A Bayesian approach was used to depict the dose-effect relationship. Heterogeneity among studies was assessed using the τ2 and I2 statistics. Sensitivity analyses were performed without studies featuring high risk of bias. RESULTS: Studies with 64 696 patients were examined from 216 trials. Compared to placebo, 13 antidepressants showed higher ORs for somnolence, of which fluvoxamine (OR = 6.32; 95% CI: 3.56 to 11.21) ranked the top. Eleven had higher risks for insomnia, reboxetine ranked the top (OR = 3.47; 95% CI: 2.77 to 4.36). The dose-effect relationships curves between somnolence or insomnia and dose included linear shape, inverted U-shape, and other shapes. There was no significant heterogeneity among individual studies. The quality of evidence for results in network meta-analyses was rated as very low to moderate by Grading of Recommendations Assessment, Development, and Evaluation. CONCLUSIONS: Most antidepressants had higher risks for insomnia or somnolence than placebo. The diverse relationship curves between somnolence or insomnia and dose of antidepressants can guide clinicians to adjust the doses. These findings suggest clinicians pay more attention to sleep-related adverse effects during acute treatment with antidepressants.

Anterior cingulate glutamate levels associate with functional activation and connectivity during sensory integration in schizophrenia: a multimodal 1H-MRS and fMRI study.

BACKGROUND: Glutamatergic dysfunction has been implicated in sensory integration deficits in schizophrenia, yet how glutamatergic function contributes to behavioural impairments and neural activities of sensory integration remains unknown. METHODS: Fifty schizophrenia patients and 43 healthy controls completed behavioural assessments for sensory integration and underwent magnetic resonance spectroscopy (MRS) for measuring the anterior cingulate cortex (ACC) glutamate levels. The correlation between glutamate levels and behavioural sensory integration deficits was examined in each group. A subsample of 20 pairs of patients and controls further completed an audiovisual sensory integration functional magnetic resonance imaging (fMRI) task. Blood Oxygenation Level Dependent (BOLD) activation and task-dependent functional connectivity (FC) were assessed based on fMRI data. Full factorial analyses were performed to examine the Group-by-Glutamate Level interaction effects on fMRI measurements (group differences in correlation between glutamate levels and fMRI measurements) and the correlation between glutamate levels and fMRI measurements within each group. RESULTS: We found that schizophrenia patients exhibited impaired sensory integration which was positively correlated with ACC glutamate levels. Multimodal analyses showed significantly Group-by-Glutamate Level interaction effects on BOLD activation as well as task-dependent FC in a 'cortico-subcortical-cortical' network (including medial frontal gyrus, precuneus, ACC, middle cingulate gyrus, thalamus and caudate) with positive correlations in patients and negative in controls. CONCLUSIONS: Our findings indicate that ACC glutamate influences neural activities in a large-scale network during sensory integration, but the effects have opposite directionality between schizophrenia patients and healthy people. This implicates the crucial role of glutamatergic system in sensory integration processing in schizophrenia.

Disrupted Spatiotemporal Complexity of Resting-State Electroencephalogram Dynamics Is Associated With Adaptive and Maladaptive Rumination in Major Depressive Disorder.

Patients with major depressive disorder (MDD) exhibit abnormal rumination, including both adaptive and maladaptive forms. However, the neural substrates of rumination in depression remain poorly understood. We hypothesize that divergent spatiotemporal complexity of brain oscillations would be associated with the levels of rumination in MDD. We employed the multi-scale entropy (MSE), power and phase-amplitude coupling (PAC) to estimate the complexity of rhythmic dynamics from the eye-closed high-density electroencephalographic (EEG) data in treatment-naive patients with MDD (n = 24) and healthy controls (n = 22). The depressive, brooding, and reflective subscales of the Ruminative Response Scale were assessed. MDD patients showed higher MSE in timescales finer than 5 (cluster P = 0.038) and gamma power (cluster P = 0.034), as well as lower PAC values between alpha/low beta and gamma bands (cluster P = 0.002- 0.021). Higher reflective rumination in MDD was region-specifically associated with the more localized EEG dynamics, including the greater MSE in scales finer than 8 (cluster P = 0.008), power in gamma (cluster P = 0.018) and PAC in low beta-gamma (cluster P = 0.042), as well as weaker alpha-gamma PAC (cluster P = 0.016- 0.029). Besides, the depressive and brooding rumination in MDD showed the lack of correlations with global long-range EEG variables. Our findings support the disturbed neural communications and point to the spatial reorganization of brain networks in a timescale-dependent migration toward local during adaptive and maladaptive rumination in MDD. These findings may provide potential implications on probing and modulating dynamic neuronal fluctuations during the rumination in depression.

Features of cognitive impairment and related risk factors in patients with major depressive disorder: A case-control study.

BACKGROUND: Cognitive impairment (CI) is a common symptom contributing to functional loss in major depressive disorder (MDD). However, the features of CI and its related risk factors in young and middle-aged MDD patients remain unclear. METHODS: In this case-control study, 18- to 55-year-old acute-onset MDD patients and healthy controls (HCs) were recruited from nine centers in China. MDD patients were diagnosed based on the DSM-IV, the Mini-International Neuropsychiatric Interview, and a 17-item Hamilton Rating Scale for Depression score ≥ 14. Cognitive function, including attention/vigilance, learning, memory, processing speed and executive function, was assessed with a neuropsychological battery and compared between MDD patients and HCs. MDD patients scoring 1.5 SDs below the mean HC score in at least 2 domains were defined as having CI. Logistic regression analysis was used to identify risk factors for CI in MDD patients. RESULTS: Compared with HCs (n = 302), MDD patients (n = 631) showed significant impairment in all cognitive domains (P < 0.001); 168 MDD patients (26.6%) had CI. Male sex (OR: 1.712; 95% CI: 1.165-2.514; P < 0.01) was positively correlated with CI; age of first onset (OR: 0.974; 95% CI: 0.957-0.991; P < 0.05) and comorbid anxiety disorders (OR: 0.514; 95% CI: 0.332-0.797; P < 0.01) were negatively correlated with CI. LIMITATIONS: Biomarkers and neuroimaging were not used to investigate the possible biological mechanism and neural basis of CI in MDD. CONCLUSIONS: CI was prominent in adults with acute-onset MDD; male sex and younger age of first onset were independent risk factors, and comorbid anxiety disorders were a protective factor.

Hub-connected functional connectivity within social brain network weakens the association with real-life social network in schizophrenia patients.

Hubs in the brain network are the regions with high centrality and are crucial in the network communication and information integration. Patients with schizophrenia (SCZ) exhibit wide range of abnormality in the hub regions and their connected functional connectivity (FC) at the whole-brain network level. Study of the hubs in the brain networks supporting complex social behavior (social brain network, SBN) would contribute to understand the social dysfunction in patients with SCZ. Forty-nine patients with SCZ and 27 healthy controls (HC) were recruited to undertake the resting-state magnetic resonance imaging scanning and completed a social network (SN) questionnaire. The resting-state SBN was constructed based on the automatic analysis results from the NeuroSynth. Our results showed that the left temporal lobe was the only hub of SBN, and its connected FCs strength was higher than the remaining FCs in both two groups. SCZ patients showed the lower association between the hub-connected FCs (compared to the FCs not connected to the hub regions) with the real-life SN characteristics. These results were replicated in another independent sample (30 SCZ and 28 HC). These preliminary findings suggested that the hub-connected FCs of SBN in SCZ patients exhibit the abnormality in predicting real-life SN characteristics.

Altered empathy-related resting-state functional connectivity in patients with bipolar disorder.

Empathy is the ability to generate emotional responses (i.e., cognitive empathy) and to make cognitive inferences (i.e., affective empathy) to other people's emotions. Empirical evidence suggests that patients with bipolar disorder (BD) exhibit impairment in cognitive empathy, but findings on affective empathy are inconsistent. Few studies have examined the neural mechanisms of cognitive and affective empathy in patients with BD. In this study, we examined the empathy-related resting-state functional connectivity (rsFC) in BD patients. Thirty-seven patients with BD and 42 healthy controls completed the self-report Questionnaires of Cognitive and Affective Empathy (QCAE), the Yoni behavioural task, and resting-sate fMRI brain scans. Group comparison of empathic ability was conducted. The interactions between group and empathic ability on seed-based whole brain rsFC were examined. BD patients scored lower on the Online Simulation subscale of the QCAE and showed positive correlations between cognitive empathy and the rsFC of the dorsal Medial Prefrontal Cortex (dmPFC) with the lingual gyrus. The correlations between cognitive empathy and the rsFC of the temporal-parietal junction (TPJ) with the fusiform gyrus, the cerebellum and the parahippocampus were weaker in BD patients than that in healthy controls. These findings highlight the underlying neural mechanisms of empathy impairments in BD patients.

Inflammatory cytokines, cognition, and response to antidepressant treatment in patients with major depressive disorder.

BACKGROUND: Inflammation and cognition are related to major depressive disorder (MDD), but the role in treatment response remains unclear. In this study, we investigated correlation between inflammatory cytokines and cognition in MDD patients treated with antidepressant medication. METHODS: The participants were 149 MDD patients. Cytokines before therapy, cognitive assessments and severity of depression before and after therapy were tested. Logistic regression was used to explore underlying risks treatment response. RESULTS: There were significant differences in smoking, alcohol drinking, and Stroop Color Test(SCT), Stroop Color-Word Test (SCWT), and Continuous Performance Test(CPT) scores between response group (RG) and non-response group (NRG) at baseline. Performance of patients in RG improved more in Brief Assessment of Cognition in Schizophrenia (BACS), Color Trial Test-I (CTT-I), SCT and SCWT after treatment. Levels of baseline IL-18 were associated with baseline learning and memory, and executive function. Treatment response was associated with drinking, performance of CPT and SCT. CONCLUSION: MDD patients with different treatment responses have different cognitive defects, especially in speed of processing and executive function. Expression of cytokines is associated with cognition and may influence treatment response. Better speed of processing and executive function, and poorer attention at baseline may respond better to antidepressant treatment.

Trends in Psychotropic Medication Prescriptions in Urban China From 2013 to 2017: National Population-Based Study.

Purpose: Psychotropic medications are commonly used for treating mental disorders; however, there is currently no study on how commonly they are used in China. This study reported the trends in psychotropic medications prescriptions in urban China. Methods: A national population-based study was conducted using the China Health Insurance Research Association database to estimate the period prescription prevalence of 11 major classes of psychotropic medications annually during 2013-2017. The World Health Organization Anatomical Therapeutic Chemical (ATC) classification codes were used to identify psychotropic medications. Results: The prescription prevalence of any psychotropic medication increased from 8.110% (8.106-8.114%) in 2013 to 11.362% (11.357-11.366%) in 2017. The prescription prevalence of six classes increased significantly during 2013-2017, including sedatives-hypnotics (from 3.177 to 5.388%), anxiolytics (from 1.436 to 2.200%), antiepileptic drugs (from 1.416 to 2.140%), antipsychotics (from 0.809 to 1.156%), antidepressants (from 0.891 to 1.045%), and psycholeptic polypills (from 0.682 to 0.866%). The prescription prevalence of antidementia drugs increased from 0.069 to 0.122%, and mood stabilizers increased from 0.029 to 0.037%, although not statistically significant. The prescription prevalence of nootropic drugs, attention deficit hyperactivity disorder (ADHD) medications and drugs used in the treatment of addictive disorders was largely stable. Psychotropic medication prescription increased with age for all classes except for ADHD medications and mood stabilizers. Conclusion: Increasing trends in prescription prevalence were observed for most classes of psychotropic medications in urban China, although the prevalence was still lower than that in most developed countries. Further research is warranted to explore the potential treatment gap between China and most developed countries.

The effect of selective serotonin reuptake inhibitors on cognitive impairment in patients with depression: A prospective, multicenter, observational study.

Whether selective serotonin reuptake inhibitors (SSRIs) can improve cognitive function in depressed patients remains unclear. We aimed to explore the effect of SSRIs on cognitive function and the influencing factors in patients with major depressive disorder (MDD) with impaired cognitive function. We used a neurocognitive test battery to assess five cognitive domains, namely, attention/vigilance, learning, memory, processing speed and executive functioning, and calculated the Global Deficit Score (GDS). A GDS≥0.5 indicated overall cognitive impairment. The changes in the cognitive domains and overall cognitive function were analyzed in MDD patients with a baseline GDS≥0.5 who completed 8 weeks of SSRI treatment. We divided the patients into the cognitive remission and nonremission groups to explore the associated factors. In total, 160 patients (mean age 37.6 ± 10.8 years, 30.6% male) were included in the final analysis. The median (quartiles) of baseline GDS and HRSD17 were 1.4 (0.8, 1.9) and 19.5 (17.0, 23.0), respectively. All five cognitive domains improved after the treatment, while only 41 (25.6%) patients achieved cognitive remission. Recurrence and a higher baseline GDS were risk factors for cognitive nonremission. More aggressive interventions may be needed to promote cognitive remission in clinical practice, especially for patients with severe cognitive impairment and recurrent episodes.

The effects of dynamic daylight-like light on the rhythm, cognition, and mood of irregular shift workers in closed environment.

Shift workers are mostly suffered from the disruption of circadian rhythm and health problems. In this study, we designed proper light environment to maintain stable circadian rhythm, cognitive performance, and mood status of shift workers. We used five-channel light-emitting diodes to build up the dynamic daylight-like light environment. The illuminance, correlated color temperature, and circadian action factor of light were tunable in the ranges of 226 to 678 lx, 2680 to 7314 K, and 0.32 to 0.96 throughout the day (5:30 to 19:40). During the nighttime, these parameters maintained about 200 lx, 2700 K, and 0.32, respectively. In this light environment, three subjects had engaged in shift work for 38 consecutive days. We measured plasma melatonin, activity counts, continuous performance tests, and visual analogue scale on mood to assess the rhythm, cognitive performance, and mood of subjects. After 38-day shift work, the subjects' peak melatonin concentration increased significantly. Their physiological and behavioral rhythms maintained stable. Their cognitive performance improved significantly after night work, compared with that before night work. Their mood status had no significant change during the 38-day shift work. These results indicated that the light environment was beneficial to maintain circadian rhythm, cognitive performance and mood status during long-term shift work in closed environment.

Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia.

Social behaviour requires the brain to efficiently integrate multiple social processes, but it is not clear what neural substrates underlie general social behaviour. While psychosis patients and individuals with subclinical symptoms are characterized by social dysfunction, the neural mechanisms underlying social dysfunctions in schizophrenia spectrum disorders remains unclear. We first constructed a general social brain network (SBN) using resting-state functional connectivity (FC) with regions of interest based on the automatic meta-analysis results from NeuroSynth. We then examined the general SBN and its relationship with social network (SN) characteristics in 30 individuals with schizophrenia (SCZ) and 33 individuals with social anhedonia (SA). We found that patients with SCZ exhibited deficits in their SN, while SA individuals did not. SCZ patients showed decreased segregation and functional connectivity in their SBN, while SA individuals showed a reversed pattern with increased segregation and functional connectivity of their SBN. Sparse canonical correlation analysis showed that both SCZ patients and SA individuals exhibited reduced correlation between SBN and SN characteristics compared with their corresponding healthy control groups. These preliminary findings suggest that both SCZ and SA participants exhibit abnormality in segregation and functional connectivity within the general SBN and reduced correlation with SN characteristics. These findings could guide the development of non-pharmacological interventions for social dysfunction in SCZ spectrum disorders.

Machine learning to predict clinical remission in depressed patients after acute phase selective serotonin reuptake inhibitor treatment.

OBJECTIVE: Selective serotonin reuptake inhibitors (SSRIs) are suggested as the first-line treatment for patients with major depressive disorder (MDD), but the remission rate is unsatisfactory. We aimed to establish machine learning models and explore variables available at baseline to predict the 8-week outcome among patients taking SSRIs. METHODS: Data from 400 patients were used to build machine learnings. The last observation carried forward approach was used to determine the remitter/non-remitter status of the patients at week 8. Using least absolute shrinkage and selection operator (LASSO) to select features, we built 4 different machine learning algorithms including gradient boosting decision tree, support vector machine (SVM), random forests, and logistic regression with five-fold cross-validation. Then, we adopted Shapley additive explanations (SHAP) values to interpret the model output. RESULTS: The remission rate is 67.8%. We obtained 78 features from the baseline characteristics, including 25 sociodemographic characteristics, 31 clinical features, 15 psychological traits and 7 neurocognitive functions, and 13 of these features were selected to establish SVM. The accuracy of the SVM prediction is 74.49%, reaching an average area under the curve of 0.734±0.043. The sensitivity is 0.899±0.038 with a positive predictive value of 0.776±0.028. The specificity is 0.422±0.091 with a negative predictive value of 0.674±0.086. According to the SHAP values, neurocognitive functions and anxiety and hypochondriasis symptoms were important predictors. CONCLUSION: Our study supports the utilization of machine learning approaches with inexpensive and highly accessible variables to accurately predict the 8-week treatment outcome of SSRIs in patients with MDD.

The diagnostic threshold of Cornell assessment of pediatric delirium in detection of postoperative delirium in pediatric surgical patients.

BACKGROUND: Cornell assessment of pediatric delirium (CAPD) showed advantage in diagnosis of pediatric delirium in Chinese critically ill patients. But its performance in surgical patients is still unclear. The present study was designed to validate the diagnostic performance of CAPD in surgical pediatric patients. METHODS: This is a prospective validation study. Pediatric patients who underwent selective surgery and general anesthesia were enrolled. Primary outcome was the incidence of delirium within postoperative three days. CAPD Chinese version was used to evaluate if the patient had delirium one time per day. At the meantime, a psychiatrist employed Diagnostic and Statistical Manual of Mental Disorders fifth edition to diagnose delirium, which was the "gold standard", and the result was considered as reference standard. Sensitivity, specificity and area under receiver operating characteristic (ROC) curve were calculated to investigate the performance of CAPD. RESULTS: A total of 170 patients were enrolled. Median age was 4 years old. As diagnosed by psychiatrist, 23 (13.5 %) patients experienced at least one episode of delirium during the follow-up period. When diagnostic threshold was set at 9, CAPD showed the optimal sensitivity (87.0 %, 95 %CI 65.3 %-96.6 %) and specificity (98.0 %, 95 %CI 93.7 %-99.5 %) in comparison with other diagnostic thresholds. ROC analysis showed that CAPD was a good delirium assessment instrument with area under curve of 0.911 (95 % CI 0.812 to 1.000, P < 0.001). Agreement between CAPD and reference standard was 0.849 (Kappa coefficient, P < 0.001). CONCLUSIONS: This study found that Cornell assessment of pediatric delirium could be used as an effective instrument in diagnosis of delirium in pediatric surgical patients. TRIAL REGISTRATION: www.chictr.org.cn Identifier: ChiCTR-DDD-17,012,231, August 3, 2017.

Validation of the Questionnaire of Cognitive and Affective Empathy in patients with schizophrenia, major depressive disorder and bipolar disorder.

INTRODUCTION: Alteration of empathy is common in patients with psychiatric disorders. Reliable and valid assessment tools for measuring empathy of clinical samples is needed. The Questionnaire of Cognitive and Affective Empathy (QCAE) is a newly-developed instrument to capture cognitive and affective components of empathy. This study aimed to validate the QCAE and compared self-reported empathy between clinical groups with varied psychiatric diagnoses and healthy sample. METHODS: The present study performed factor analysis for the QCAE on clinical samples in the Chinese setting (n = 534), including patients with schizophrenia (n = 158), bipolar disorder (n = 213) and major depressive disorder (n = 163). Internal consistency, internal correlation and convergent validity was examined in the subsample (n = 361). Group comparison among patients with schizophrenia, bipolar disorder, major depressive disorder and healthy controls (n = 107) was conducted to assess the discriminant validity. RESULTS: Our results indicated acceptable factor model, good reliability and validity of the QCAE. Impaired cognitive empathy was found in clinical samples, especially in patients with schizophrenia, while higher affective empathy was found in patients with bipolar disorder and major depressive disorder. CONCLUSION: The QCAE is a useful tool in assessing empathy in patients with varied psychiatric diagnoses.

Investigation on entraining and enhancing human circadian rhythm in closed environments using daylight-like LED mixed lighting.

Humans can undergo circadian disruption and misalignment when living in closed environments without sufficient daylight. Therefore, it is of great significance to investigate the effects of artificial light on the circadian rhythm. In this work, the red, green, blue, warm white, and cool white (RGBWW) five-channel light-emitting diodes (LEDs) were fabricated as the only light sources in the closed environment. The LED mixed lighting showed a high color rendering index (CRI) all the time. During the day, the light simulated the daylight and increased the tunability of the circadian action factor (CAF) and correlated color temperature (CCT). At night, it maintained low CAF and CCT. Three subjects did irregular shift work in the closed environment for 38 days. Their plasma melatonin and daily activity were measured to assess the circadian rhythm. After 38 days, the subjects' peak melatonin times did not shift significantly (p = 0.676), while their peak melatonin concentrations increased apparently (p = 0.005). The start times of the least active 5-h period (L5) in one day fluctuated in a small range. The standard deviation (SD) was <15.11 min in most times. These results demonstrated that the subjects' rhythms maintained stable and were enhanced. The periods of circular cross-correlation between activity and CAF oscillated around 24 h (SD = 15.4 min), indicating the entrainment of light on the stable 24-h rhythm. It was concluded that the daylight-like LED lighting effectively entrained and enhanced the circadian rhythm in the closed environment.

Cross-cultural adaptation and validation of the 3D-CAM Chinese version in surgical ICU patients.

BACKGROUND: Accurate diagnosis of delirium is very important for prevention and treatment. Present study was designed to validate the 3-Minute Diagnostic Interview for CAM-defined Delirium Chinese version (3D-CAM-CN) in surgical ICU patients. METHODS: In this prospective diagnostic study, the 3D-CAM was translated into Chinese with culture adaption. Two interviewers (Roles A and B) independently administrated 3D-CAM-CN assessment in adult patients from postoperative days 1 to day 3. At the meantime, a panel of psychiatrists diagnosed delirium according to the Diagnostic and Statistical Manual of Mental Disorders-fifth edition as the reference standard. The sensitivity and specificity were calculated to analyze the diagnostic character of the 3D-CAM-CN. Kappa coefficient was used to evaluate interrater reliability. RESULTS: Two hundred forty-five adult patients were assessed for at least 2 days, resulting a total of 647 paired-assessments. When compared with the reference standard, the sensitivity and specificity of the 3D-CAM-CN assessment were 87.2 and 96.7%, respectively, by Role A and 84.6 and 97.4%, respectively, by Role B, with good interrater reliability (Kappa coefficient = 0.82, P < 0.001). It also performed well in patients with mild cognitive impairment, with the sensitivity from 85.7 to 100% and the specificity from 95.7 to 96.4%. CONCLUSION: Our results showed that the 3D-CAM-CN can be used as a reliable and accurate instrument for delirium assessment in surgical patients. TRIAL REGISTRATION: This trail was approved by the Clinical Research Ethic Committee of Peking University First Hospital (No. 2017-1321) and registered on Chinese clinical trial registry on July 6, 2017 (ChiCTR-OOC-17011887).

Moxibustion at 'Danzhong' (RN17) and 'Guanyuan' (RN4) for fatigue symptom in patients with depression: Study protocol clinical trial (SPIRIT Compliant).

BACKGROUND: Fatigue is one of the most prevalent and debilitating symptoms of major depressive disorder (MDD). The effective management of depression-related fatigue has an important impact on the patient's abilities, functioning, and quality of life (QOL). Moxibustion has been widely used in Traditional Chinese Medicine to manage fatigue. Recent studies have also demonstrated that moxibustion is effective for treating cancer-related fatigue and chronic fatigue syndrome. However, there is not sufficient data supporting the effect of moxibustion for depression-related fatigue. Therefore, this randomized, assessor-blinded, wait-list controlled trial is designed to evaluate the effectiveness, safety, and feasibility of moxibustion treatment for depression-related fatigue. METHODS: One hundred and seventy-six participants who meet the diagnostic criteria for depression in the International Classification of Diseases, tenth revision (ICD-10), and who also have a score of ≥1 on the 13 item of the Hamilton Depression Rating Scale-17 (HAMD-17), will be enrolled. At study entry, participants will undergo anti-depressant treatment for at least 1 month. Then those who still have a score of ≥1 on the 13 item of the HAMD-17 will be randomly allocated to either a moxibustion group or wait-list control group in a ratio of 1:1. Anti-depressants will be provided for both groups during the whole process of the study period. Participants in the moxibustion group will undergo 14 sessions of moxibustion (over 2 weeks) with anti-depressant treatment, and participants in the wait-list control group will receive only anti-depressant treatment. Subsequently, participants in the moxibustion group will be followed-up for 4 weeks. The primary outcome measure will be the Fatigue Severity Scale (FSS). The secondary outcome measure will be the HAMD-17. Safety will be assessed by monitoring adverse events during the study. Trial feasibility will also be assessed in this study. DISCUSSION: The results of this study may provide evidence for the efficacy of moxibustion as an adjunct to antidepressants for depression-related fatigue, and promote a more widespread foundation for the selection of moxibustion in the clinical setting as well as for future research in moxibustion therapy. TRIAL REGISTRATION: This study protocol was registered at the Chinese Clinical Trial Registry (ChiCTR1800016905).

Implementing machine learning in bipolar diagnosis in China.

Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.