Symptom Profile and Clinical Course of Inpatients with Unipolar versus Bipolar Depression

Clinical distinctions in symptomatology and psychiatric comorbidities between misdiagnosed bipolar I and bipolar II disorder versus major depressive disorder

BACKGROUND

To explore the demographic and clinical features of current depressive episode that discriminate patients diagnosed with major depressive disorder (MDD) from those with bipolar I (BP-I) and bipolar II (BP-II) disorder who were misdiagnosed as having MDD .

METHODS

The Mini-International Neuropsychiatric Interview (MINI) assessment was performed to establish DSM-IV diagnoses of MDD, and BP-I and BP-II, previously being misdiagnosed as MDD. Demographics, depressive symptoms and psychiatric comorbidities were compared between 1463 patients with BP-I, BP-II and MDD from 8 psychiatric settings in mainland China. A multinomial logistic regression model was performed to assess clinical correlates of diagnoses.

RESULTS

A total of 14.5% of the enrolled patients initially diagnosed with MDD were eventually diagnosed with BP. Broad illness characteristics including younger age, higher prevalence of recurrence, concurrent dysthymia, suicidal attempts, agitation, psychotic features and psychiatric comorbidities, as well as lower prevalence of insomnia, weight loss and somatic symptoms were featured by patients with BP-I and/or BP-I, compared to those with MDD. Comparisons between BP-I and BP-II versus MDD indicated distinct symptom profiles and comorbidity patterns with more differences being observed between BP-II and MDD, than between BP-I and MDD .

CONCLUSION

The results provide evidence of clinically distinguishing characteristics between misdiagnosed BP-I and BP- II versus MDD. The findings have implications for guiding more accurate diagnoses of bipolar disorders.

Inter-Rater Reliability between Structured and Non-Structured Interviews Is Fair in Schizophrenia and Bipolar Disorders-A Systematic Review and Meta-Analysis

We aimed to find agreement between diagnoses obtained through standardized (SDI) and non-standardized diagnostic interviews (NSDI) for schizophrenia and Bipolar Affective Disorder (BD).

METHODS

A systematic review with meta-analysis was conducted. Publications from 2007 to 2020 comparing SDI and NSDI diagnoses in adults without neurological disorders were screened in MEDLINE, ISI Web of Science, and SCOPUS, following PROSPERO registration CRD42020187157, PRISMA guidelines, and quality assessment using QUADAS-2.

RESULTS

From 54231 entries, 22 studies were analyzed, and 13 were included in the final meta-analysis of kappa agreement using a mixed-effects meta-regression model. A mean kappa of 0.41 (Fair agreement, 95% CI: 0.34 to 0.47) but high heterogeneity (β = 92%) were calculated. Gender, mean age, NSDI setting (Inpatient vs. Outpatient; University vs. Non-university), and SDI informant (Self vs. Professional) were tested as predictors in meta-regression. Only SDI informant was relevant for the explanatory model, leaving 79% unexplained heterogeneity. Egger's test did not indicate significant bias, and QUADAS-2 resulted in "average" data quality.

CONCLUSIONS

Most studies using SDIs do not report the original sample size, only the SDI-diagnosed patients. Kappa comparison resulted in high heterogeneity, which may reflect the influence of non-systematic bias in diagnostic processes. Although results were highly heterogeneous, we measured a fair agreement kappa between SDI and NSDI, implying clinicians might operate in scenarios not equivalent to psychiatry trials, where samples are filtered, and there may be more emphasis on maintaining reliability. The present study received no funding.

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

BACKGROUND

Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.

METHODS

The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.

RESULTS

The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.

CONCLUSIONS

Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care.