About a year and a half after publishing ICD-11, we aim to gather initial feedback, comments, opinions, and even recent study results from experts in the relevant fields through this collection. We ...hope to facilitate a preliminary summary of whether the new classification truly represents progress, and how it has changed treatment, and research of mental illnesses.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives
Predictors for unfavorable treatment outcome in major depressive disorder (MDD) applicable for treatment selection are still lacking. The database of a longitudinal multicenter study on ...1079 acutely depressed patients, performed by the German research network on depression (GRND), allows supervised and unsupervised learning to further elucidate the interplay of clinical and psycho‐sociodemographic variables and their predictive impact on treatment outcome phenotypes.
Experimental Procedures
Treatment response was defined by a change of HAM‐D 17‐item baseline score ≥50% and remission by the established threshold of ≤7, respectively, after up to eight weeks of inpatient treatment. After hierarchical symptom clustering and stratification by treatment subtypes (serotonin reuptake inhibitors, tricyclic antidepressants, antipsychotic, and lithium augmentation), prediction models for different outcome phenotypes were computed with random forest in a cross‐center validation design. In total, 88 predictors were implemented.
Results
Clustering revealed four distinct HAM‐D subscores related to emotional, anxious, sleep, and appetite symptoms, respectively. After feature selection, classification models reached moderate to high accuracies up to 0.85. Highest accuracies were observed for the SSRI and TCA subgroups and for sleep and appetite symptoms, while anxious symptoms showed poor predictability.
Conclusion
Our results support a decisive role for machine learning in the management of antidepressant treatment. Treatment‐ and symptom‐specific algorithms may increase accuracies by reducing heterogeneity. Especially, predictors related to duration of illness, baseline depression severity, anxiety and somatic symptoms, and personality traits moderate treatment success. However, prospectives application of machine learning models will be necessary to prove their value for the clinic.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
The recent release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) by the American Psychiatric Association has led to much debate. For this forum article, we ...asked BMC Medicine Editorial Board members who are experts in the field of psychiatry to discuss their personal views on how the changes in DSM-5 might affect clinical practice in their specific areas of psychiatric medicine. This article discusses the influence the DSM-5 may have on the diagnosis and treatment of autism, trauma-related and stressor-related disorders, obsessive-compulsive and related disorders, mood disorders (including major depression and bipolar disorders), and schizophrenia spectrum disorders.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Quantifying depression mainly relies on the use of depression scales, and understanding their factor structure is crucial for evaluating their validity.
This post-hoc analysis utilized prospectively ...collected data from a naturalistic study of 1014 inpatients with major depression. Confirmatory and exploratory factor analyses were performed to test the psychometric abilities of the Hamilton Depression Rating Scale, the Montgomery Asberg Depression Rating Scale, and the self-rated Beck Depression Inventory. A combined factor analysis was also conducted including all items of all scales.
All three scales showed good to very good internal consistency. The HAMD-17 had four factors: an "anxiety" factor, a "depression" factor, an "insomnia" factor, and a "somatic" factor. The MADRS also had four factors: a "sadness" factor, a neurovegetative factor, a "detachment" factor and a "negative thoughts" factor, while the BDI had three factors: a "negative attitude towards self" factor, a "performance impairment" factor, and a "somatic" factor. The combined factor analysis suggested that self-ratings might reflect a distinct illness dimension within major depression.
The factors obtained in this study are comparable to those found in previous research. Self and clinician ratings are complementary and not redundant, highlighting the importance of using multiple measures to quantify depression.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract Remission and response were suggested as the most relevant outcome criteria for the treatment of depression. There is still marked uncertainty as to what cut-offs should be used on current ...depression rating scales. The goal of the present study was to compare the validity of different HAMD, MADRS and BDI cut-offs for response and remission. The naturalistic prospective study was performed in 12 psychiatric hospitals in Germany. All evaluable patients ( n = 846) were hospitalized and had to meet DSM-IV criteria for major depressive disorder. Biweekly ratings were assessed using HAMD-21, MADRS and BDI. A CGI-S score of 1 and a CGI-I score of at least 2 was used as the primary comparative measure of remission and response, respectively. A HAMD-21 cut-off ≤ 7 (AUC: 0.92), HAMD-17 cut-of ≤ 6 (AUC: 0.90), MADRS cut-off ≤ 7 (AUC: 0.94) and BDI cut-off ≤ 12 (AUC: 0.83) were associated with a maximum of specificity and sensitivity for defining remission. A minimum decrease of 47% of the HAMD-21 (AUC: 0.90), ≤57% for HAMD-17 (AUC: 0.89), ≤ 46% for MADRS (0.91) and a decrease of 47% for the BDI baseline score (AUC: 0.78) best corresponded CGI response criteria. Our data largely confirmed currently used remission and response criteria in naturalistically treated patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Background
Research suggests that a low omega-3 index may contribute to the low heart rate variability and the increased risk of cardiovascular morbidity and mortality in bipolar disorders. However, ...so far, no intervention trial with EPA and DHA has been conducted in bipolar patients attempting to increase their heart rate variability.
Methods
119 patients with bipolar disorder according to DSM-IV were screened, with 55 euthymic bipolar patients—owing to inclusion criteria (e.g. low omega-3 index (< 6%), SDNN < 60 ms.)—being enrolled in a randomized, double-blind, 12-week parallel study design with omega-3 fatty acids (4 capsules of 530 mg EPA, 150 mg DHA) or corn oil as a placebo, in addition to usual treatment. Heart rate variability as well as the omega-3 index were measured at baseline and at the endpoint of the study.
Results
A total of 42 patients (omega-3: n = 23, corn oil: n = 19) successfully completed the study after 12 weeks. There was a significant increase in the omega-3 index (value at endpoint minus value at baseline) in the omega-3 group compared to the corn oil group (p < 0.0001). However, there was no significant difference in the change of the SDNN (value at endpoint minus value at baseline) between the treatment groups (p = 0.22). In addition, no correlation between changes in SDNN and change in the omega-3 index could be detected in the omega-3 group (correlation coefficient = 0.02, p = 0.94) or the corn oil group (correlation coefficient = − 0.11, p = 0.91). Similarly, no significant differences between corn oil and omega-3 group regarding the change of LF (p = 0.19), HF (p = 0.34) and LF/HF ratio (p = 0.84) could be demonstrated.
Conclusions
In our randomized, controlled intervention trial in euthymic bipolar patients with a low omega-3 index and reduced heart rate variability no significant effect of omega-3 fatty acids on SDNN or frequency-domain measures HF, LF and LF/HF ratio could be detected. Possible reasons include, among others, the effect of psychotropic medication present in our trial and/or the genetics of bipolar disorder itself. Further research is needed to test these hypotheses.
Trial registration
ClinicalTrials.gov, NCT00891826. Registered 01 May 2009–Retrospectively registered,
https://clinicaltrials.gov/ct2/show/NCT00891826
Aim of the study was to examine the course of schizophrenia patients within 2 years after discharge. Within a multicenter study of the German Competence Network on Schizophrenia, patients suffering ...from a schizophrenia spectrum disorder were examined regarding their psychopathological improvement, tolerability, and the treatment regime applied during hospitalization and a 2-year follow-up period. Response, remission, the level of everyday functioning, and relapse were furthermore evaluated during the follow-up period using established definitions for these outcome domains. The psychopharmacological treatment was specifically evaluated in terms of a potential association with relapse. 149 patients were available for analysis, with 65% of the patients being in response, 52% in symptomatic remission, and 64% having a satisfiable everyday functioning 2 years after their discharge from hospital. Despite these favorable outcome rates, 63% of the patients suffered from a relapse within the 2-year follow-up period with 86% of these patients being rehospitalized. Discharge non-responder and non-remitter were twice as likely to relapse during follow-up. A significant decrease of side-effects was observed with negligible rates of extrapyramidal side-effects, sedation, and weight gain during follow-up. Patients receiving treatment with atypical antipsychotics were found to have the lowest risk to relapse (
p
< 0.0001). The results highlight the natural and unsteady course of schizophrenia in most patients underlining the need to develop more specific treatment strategies ensuring ongoing stability and preventing relapse.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The PANSS (Positive and Negative Syndrome Scale) is one of the most important rating instruments for patients with schizophrenia. Nevertheless, there is a long and ongoing debate in the psychiatric ...community regarding its mathematical properties.All 30 items range from 1 to 7 leading to a minimum total score of 30, implying that the PANSS is an interval scale. For such interval scales straightforward calculation of relative changes is not appropriate. To calculate outcome criteria based on a percent change as, e.g., the widely accepted response criterion, the scale has to be transformed into a ratio scale beforehand. Recent publications have already pointed out the pitfall that ignoring the scale level (interval vs. ratio scale) leads to a set of mathematical problems, potentially resulting in erroneous results concerning the efficacy of the treatment.
A Pubmed search based on the PRISMA statement of the highest-ranked psychiatric journals (search terms "PANSS" and "response") was carried out. All articles containing percent changes were included and methods of percent change calculation were analysed.
This systematic literature research shows that the majority of authors (62%) actually appear to use incorrect calculations. In most instances the method of calculation was not described in the manuscript.
These alarming results underline the need for standardized procedures for PANSS calculations.
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Abstract Background: To assess criteria and to identify predictive factors for functional outcome. The criteria should cover all domains proposed by the Remission in Schizophrenia Working Group. ...Method: PANSS ratings were used to evaluate the symptomatic treatment outcome of 262 inpatients with schizophrenia spectrum disorders within a naturalistic multicenter trial. Functional remission was defined as a GAF score > 61 (Global Assessment of Functioning Scale), SOFAS score > 61 (Social and Occupational Functioning Scale) and a SF-36 mental health subscore > 40 (Medical Outcomes Study—Short Form Health Survey). Multivariate logistic regression and CART analyses were used to determine valid clinical and sociodemographic predictors. Results: In total, 52 patients (20%) fulfilled the criteria for functional remission, 125 patients (48%) achieved symptomatic resolution and when criteria for functional remission and symptomatic resolution were combined 33 patients (13%) achieved complete remission. Younger age, employment, a shorter duration of illness, a shorter length of current episode, less suicidality, and a lower PANSS negative and global subscore at admission were predictive of functional remission. The regression model showed a predictive value of more than 80%. Conclusions: A significant association was found between functional remission and symptomatic resolution, indicating reasonable validity of the proposed definition for functional outcome. The revealed predictors for functional treatment outcome emphasize the need for psychosocial and vocational rehabilitation in schizophrenic patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Objective: There is growing evidence of cognitive impairment as a trait factor in bipolar disorder. The generalizability of this finding is limited because previous studies have either focussed ...exclusively on bipolar I disorder or have analysed mixed patient groups. Thus, it is still largely unknown whether bipolar II patients perform differently from bipolar I patients on measures of cognitive functioning.
Methodology: A total of 65 patients with bipolar I disorder, 38 with bipolar II disorder, and 62 healthy controls participated in the study. Patients had to be euthymic for at least one month. Clinical and demographic variables were collected in a clinical interview and with the Structured Clinical Interview for DSM‐IV. Cognitive functioning was assessed using a neuropsychological battery. Univariate and multivariate analyses of variance were conducted for analyzing possible differences between the groups.
Results: The multivariate analysis of covariance (MANCOVA) indicated overall differences in neuropsychological performance between the three groups (Pillai Spur: F 1.96, p = 0.003). Post hoc comparisons revealed that patients with bipolar I disorder showed significantly lower scores in psychomotor speed, working memory, verbal learning, delayed memory, and executive functions than healthy controls. Patients with bipolar II disorder showed significant deficits in psychomotor speed, working memory, visual/constructional abilities, and executive functions compared to controls, but not on verbal learning and delayed memory. The two patient groups did not differ significantly from each other on any domain tested.
Conclusion: These results support a similar pattern of cognitive deficits in both subtypes of bipolar disorder.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
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