DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses

Analysis of inferior nasal turbinate width and concha bullosa in subjects with nasal septum deviation: a cone beam tomography study

Background

In individuals with nasal septal deviation (NSD), compensatory hypertrophy of the nasal turbinates occurs as a protective mechanism of the nasal passage from dry and cold air. NSD associated nasal turbinate hypertrophy is usually recurrent, requiring repetitive imaging. Therefore, a multiplanar imaging modality with a low radiation dose is best suited for long-term follow-up of this condition. This study aimed to evaluate the association of width of inferior turbinates and presence of concha bullosa with the degree of NSD using Cone beam computed tomography (CT).

Methods

The CBCT scans of 100 patients with NSD were selected as per convenience sampling and were evaluated by two maxillofacial radiologists. The width of the non-hypertrophied inferior turbinate (NHT) on the convex side of the NSD, and hypertrophic inferior turbinates (HT) on the concave side of the NSD were measured at three locations. The septal deviation angle (SDA) and the presence of concha bullosa (CB) were determined.

Results

A significant difference was observed in the anterior, middle, posterior, and mean widths between HT and NHT (p < 0.001). There was a significant difference in the widths of the HT and NHT among different types of NSD. A strong positive correlation (r = 0.71, p < 0.001) was found between SDA and the mean width of the HT. Age (P = 0.71) and gender (P = 0.65) had no significant difference among different types of NSD. Regression analysis revealed that the presence of CB (p = 0.01) and middle width of the HT (p < 0.001) are significant predictors of SDA and type of NSD.

Conclusion

The results of the present study reveal that the middle width of the HT and the presence of CB influence the degree of NSD. The present study results recommend the use of CBCT as a substitutive low radiation dose imaging modality for evaluation of NSD, CB, and associated inferior turbinate hypertrophy.

Improved sizing with image-based robotic-assisted system compared to image-free and conventional techniques in medial unicompartmental knee arthroplasty

AIMS

Ideal component sizing may be difficult to achieve in unicompartmental knee arthroplasty (UKA). Anatomical variants, incremental implant size, and a reduced surgical exposure may lead to over- or under-sizing of the components. The purpose of this study was to compare the accuracy of UKA sizing with robotic-assisted techniques versus a conventional surgical technique.

METHODS

Three groups of 93 medial UKAs were assessed. The first group was performed by a conventional technique, the second group with an image-free robotic-assisted system (Image-Free group), and the last group with an image-based robotic arm-assisted system, using a preoperative CT scan (Image-Based group). There were no demographic differences between groups. We compared six parameters on postoperative radiographs to assess UKA sizing. Incorrect sizing was defined by an over- or under-sizing greater than 3 mm.

RESULTS

There was a higher rate of tibial under-sizing posteriorly in the conventional group compared to robotic-assisted groups (47.3% (n = 44) in conventional group, 29% (n = 27) in Image-Free group, 6.5% (n = 6) in Image-Based group; p < 0.001), as well as a higher rate of femoral under-sizing posteriorly (30.1% (n = 28) in conventional group, 7.5% (n = 7) in Image-Free group, 12.9% (n = 12) in Image-Based group; p < 0.001). The posterior femoral offset was more often increased in the conventional group, especially in comparison to the Image-Based group (43% (n = 40) in conventional group, 30.1% (n = 28) in Image-Free group, 8.6% (n = 8) in Image-Based group; p < 0.001). There was no significant overhang of the femoral or tibial implant in any groups.

CONCLUSION

Robotic-assisted surgical techniques for medial UKA decrease the risk of tibial and femoral under-sizing, particularly with an image-based system using a preoperative CT scan. Cite this article: Bone Joint J 2021;103-B(4):610-618.

A Family Socialization Model of Transdiagnostic Risk for Psychopathology in Preschool Children

This longitudinal study examined the presence of general (P) and specific internalizing (INT) and externalizing (EXT) psychopathology factors in a community sample of preschool children. We assessed child and contextual correlates of P, INT, and EXT, and tested a model connecting socioeconomic risk to these factors through family socialization processes and child cognitive abilities. Participants were 501 children recruited at birth and followed up at 18 months and 3 years. Child and family functioning were measured using parental reports, observation, and standardized assessments. Both mothers and their partners reported on children's mental health, permitting the estimation of a trifactor model of psychopathology that captured caregivers' shared and unique perspectives with respect to P, INT, and EXT. Results revealed several transdiagnostic correlates of the common-perspective P factor, including family income, maternal education, maternal depression, and maternal responsiveness, as well as marginal associations with sibling negativity and children's language and theory of mind abilities. Several shared and unique correlates of INT and EXT were also observed. Structural equation modelling revealed that the effects of family income and maternal education on P operated indirectly through maternal responsiveness, while the effects of maternal education on INT and EXT operated through maternal reflective capacity, albeit in opposite directions. Together, these results suggest that the effects of socioeconomic disadvantage on general psychopathology are organized in a temporal cascade from distal to proximal risk.

Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory

Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.

Taxonomy and utility in the diagnostic classification of mental disorders

OBJECTIVE

One strategy for improving the clinical utility of mental health diagnostic systems is to better align them with how clinicians conceptualize psychopathology in practice. This approach was used in International Classification of Diseases 11th Revision (ICD-11) development, but its underlying assumption-a link between taxonomic "fit" and clinical utility-remains untested.

METHODS

Using data from global mental health clinician samples (combined N = 5404), we investigated the association between taxonomic fit and clinical utility in mental disorder categories.

RESULTS

The overall association between fit and utility was positive (r = 0.19) but statistically not different from zero (95% confidence interval [CI]: -0.06, 0.43) in this small sample (N = 39 ICD/DSM categories). However, a positive association became clear after correcting for outliers (r = 0.34 [0.05, 0.58] or higher). Further insights were apparent for specific diagnoses given their locations in the scatterplot.

CONCLUSIONS

Results suggest a positive link between taxonomic fit and clinical utility in mental disorder diagnoses, highlighting future research directions.

Reliability and Clinical Utility of Gender Identity-Related Diagnoses: Comparisons Between the ICD-11, ICD-10, DSM-IV, and DSM-5

Purpose: The World Health Organization general assembly approved the 11th revision of the International Classification of Diseases (ICD) in 2019 which will be implemented in 2022. Gender identity-related diagnoses were substantially reconceptualized and removed from the mental health chapter so that the distress criterion is no longer a prerequisite. The present study examined reliability and clinical utility of gender identity-related diagnoses of the ICD-11 in comparison with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5, ICD-10, and DSM-IV. Methods: Sixty-four health care providers assessed six videos of two children, two adolescents, and two adults referred for gender incongruence. Each provider rated one pair of videos with three of the four classification systems (ICD-11, DSM-5, ICD-10, and DSM-IV-TR). This resulted in 72 ratings for the adolescent and adult diagnoses and 59 ratings for the children's diagnoses. Results: Interrater agreement rates for each instrument ranged from 65% to 79% for the adolescence/adulthood diagnoses and from 67% to 94% for the childhood diagnoses and were comparable regardless of the system used. Only agreement rates for ICD-11 were significantly better than those for DSM-5 for both age categories. Clinicians evaluated all four systems as convenient and easy to use. Conclusion: In conclusion, both classification systems (DSM and ICD) and both editions (DSM-IV and DSM-5 and ICD-10 and ICD-11) of gender identity-related diagnoses seem reliable and convenient for clinical use.

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis

Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model accuracy to standard machine learning (SML). Fifty-three articles were included for qualitative analysis, primarily investigating autism spectrum disorder (ASD; n = 22), schizophrenia (SZ; n = 22) and attention-deficit/hyperactivity disorder (ADHD; n = 9). Thirty-two of the thirty-five studies that directly compared DL to SML reported a higher accuracy for DL. Only sixteen studies could be included in a meta-regression to quantitatively compare DL and SML performance. This showed a higher odds ratio for DL models, though the comparison attained significance only for ASD. Our results suggest that deep learning of neuroimaging data is a promising tool for the classification of individual psychiatric patients. However, it is not yet used to its full potential: most studies use pre-engineered features, whereas one of the main advantages of DL is its ability to learn representations of minimally processed data. Our current evaluation is limited by minimal reporting of performance measures to enable quantitative comparisons, and the restriction to ADHD, SZ and ASD as current research focusses on large publicly available datasets. To truly uncover the added value of DL, we need carefully designed comparisons of SML and DL models which are yet rarely performed.

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease

OBJECTIVE

We developed and investigated the feasibility of a machine learning-based automated rating for the 2 cardinal symptoms of Parkinson disease (PD): resting tremor and bradykinesia.

METHODS

Using OpenPose, a deep learning-based human pose estimation program, we analyzed video clips for resting tremor and finger tapping of the bilateral upper limbs of 55 patients with PD (110 arms). Key motion parameters, including resting tremor amplitude and finger tapping speed, amplitude, and fatigue, were extracted to develop a machine learning-based automatic Unified Parkinson's Disease Rating Scale (UPDRS) rating using support vector machine (SVM) method. To evaluate the performance of this model, we calculated weighted κ and intraclass correlation coefficients (ICCs) between the model and the gold standard rating by a movement disorder specialist who is trained and certified by the Movement Disorder Society for UPDRS rating. These values were compared to weighted κ and ICC between a nontrained human rater and the gold standard rating.

RESULTS

For resting tremors, the SVM model showed a very good to excellent reliability range with the gold standard rating (κ 0.791; ICC 0.927), with both values higher than that of nontrained human rater (κ 0.662; ICC 0.861). For finger tapping, the SVM model showed a very good reliability range with the gold standard rating (κ 0.700 and ICC 0.793), which was comparable to that for nontrained human raters (κ 0.627; ICC 0.797).

CONCLUSION

Machine learning-based algorithms that automatically rate PD cardinal symptoms are feasible, with more accurate results than nontrained human ratings.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that machine learning-based automated rating of resting tremor and bradykinesia in people with PD has very good reliability compared to a rating by a movement disorder specialist.

Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How?

A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.

A Cross-Sectional Community Readiness Assessment for Implementing School-Based Comprehensive Sexuality Education in Islamabad, Pakistan

Evidence indicates that school-based sexuality education empowers children and adolescents with the skills, values, and attitudes that will enable them to appreciate their health and well-being, nourish respectful social and sexual relationships, understand their rights, and to make informed choices. Owing to organized community resistance and prevalent misconceptions, promoting sexual and reproductive health has been challenging, especially in conservative settings like Pakistan. This study aimed at systematically exploring communities' perceptions regarding implementing school-based comprehensive sexuality education by conducting a cross-sectional community readiness assessment in Islamabad, Pakistan. A total of 35 semi-structured interviews were conducted with community key informants. Following the guidelines of the community readiness handbook, the interviews were transcribed and scored by two independent raters. The results indicate that, overall, the Islamabad community is at stage two of community readiness, the denial/resistance stage. Individual dimension scores indicate that knowledge of efforts, resources for efforts, knowledge about the issue, and leadership dimensions are at the denial/resistance stage. Only community climate was rated at stage three of community readiness, the vague awareness stage. This indicates that, for promoting sexuality education in the Pakistani context, it is essential to tackle resistance by sensitizing the community and the stakeholders through awareness campaigns.

Das überarbeitete Konzept der Persönlichkeitsstörungen nach ICD-11: Neuerungen und mögliche Konsequenzen für die forensisch-psychiatrische Tätigkeit

Mit der Einführung der ICD-11 (International Statistical Classification of Diseases and Related Health Problems, 11. Auflage) wird die Diagnostik von Persönlichkeitsstörungen grundlegend verändert. Die Notwendigkeit einer Abkehr von der traditionellen typologischen Auffassung und Beschreibung von Persönlichkeitsstörungen wurde aufgrund folgender Problemstellungen gesehen: Das kategoriale Konzept einer Persönlichkeitsstörung nach ICD-10 (International Statistical Classification of Diseases and Related Health Problems, 10. Auflage) begünstige die Stigmatisierung Betroffener, was dazu beitrage, dass die Diagnose in der klinischen Praxis zu selten vergeben werde. Wenn sie gestellt werde, komme es zu einer (zu) hohen Prävalenz der Persönlichkeitsstörung „nicht näher bezeichnet“ bzw. zu hohen Komorbiditäten zwischen verschiedenen Persönlichkeitsstörungen, was Zweifel an der Reliabilität der Persönlichkeitsstörungen begründe. Außerdem sei das in der ICD-10 geforderte Kriterium der Zeitstabilität unter Berücksichtigung von Verlaufsstudien nicht mehr sinnvoll anwendbar. Der Artikel skizziert die Hintergründe für die Überarbeitung der Konzeption von Persönlichkeitsstörungen, um nachfolgend das aktuelle diagnostische Verfahren nach ICD-11 zu illustrieren. Abschließend werden die Implikationen der neuen diagnostischen Vorgaben für die forensisch-psychiatrische Schuldfähigkeitsbegutachtung diskutiert und anhand von Persönlichkeitsprofilen beispielhaft die Auswirkungen der Neukonzeption für die Therapieplanung bzw. -prognose dargestellt und diskutiert.

Identifying Adolescents at Risk for Depression: A Prediction Score Performance in Cohorts Based in 3 Different Continents

OBJECTIVE

Prediction models have become frequent in the medical literature, but most published studies are conducted in a single setting. Heterogeneity between development and validation samples has been posited as a major obstacle for the generalization of models. We aimed to develop a multivariable prognostic model using sociodemographic variables easily obtainable from adolescents at age 15 to predict a depressive disorder diagnosis at age 18 and to evaluate its generalizability in 2 samples from diverse socioeconomic and cultural settings.

METHOD

Data from the 1993 Pelotas Birth Cohort were used to develop the prediction model, and its generalizability was evaluated in 2 representative cohort studies: the Environmental Risk (E-Risk) Longitudinal Twin Study and the Dunedin Multidisciplinary Health and Development Study.

RESULTS

At age 15, 2,192 adolescents with no evidence of current or previous depression were included (44.6% male). The apparent C-statistic of the models derived in Pelotas ranged from 0.76 to 0.79, and the model obtained from a penalized logistic regression was selected for subsequent external evaluation. Major discrepancies between the samples were identified, impacting the external prognostic performance of the model (Dunedin and E-Risk C-statistics of 0.63 and 0.59, respectively). The implementation of recommended strategies to account for this heterogeneity among samples improved the model's calibration in both samples.

CONCLUSION

An adolescent depression risk score comprising easily obtainable predictors was developed with good prognostic performance in a Brazilian sample. Heterogeneity among settings was not trivial, but strategies to deal with sample diversity were identified as pivotal for providing better risk stratification across samples. Future efforts should focus on developing better methodological approaches for incorporating heterogeneity in prognostic research.

Measuring diagnostic heterogeneity using text-mining of the lived experiences of patients

BACKGROUND

The diagnostic system is fundamental to any health discipline, including mental health, as it defines mental illness and helps inform possible treatment and prognosis. Thus, the procedure to estimate the reliability of such a system is of utmost importance. The current ways of measuring the reliability of the diagnostic system have limitations. In this study, we propose an alternative approach for verifying and measuring the reliability of the existing system.

METHODS

We perform Jaccard's similarity index analysis between first person accounts of patients with the same disorder (in this case Major Depressive Disorder) and between those who received a diagnosis of a different disorder (in this case Bulimia Nervosa) to demonstrate that narratives, when suitably processed, are a rich source of data for this purpose. We then analyse 228 narratives of lived experiences from patients with mental disorders, using Python code script, to demonstrate that patients with the same diagnosis have very different illness experiences.

RESULTS

The results demonstrate that narratives are a statistically viable data resource which can distinguish between patients who receive different diagnostic labels. However, the similarity coefficients between 99.98% of narrative pairs, including for those with similar diagnoses, are low (< 0.3), indicating diagnostic Heterogeneity.

CONCLUSIONS

The current study proposes an alternative approach to measuring diagnostic Heterogeneity of the categorical taxonomic systems (e.g. the Diagnostic and Statistical Manual, DSM). In doing so, we demonstrate the high Heterogeneity and limited reliability of the existing system using patients' written narratives of their illness experiences as the only data source. Potential applications of these outputs are discussed in the context of healthcare management and mental health research.

[Evaluation of medical insurance consultant reports within the German Statutory Accident Insurance-Methodology and results of a peer review procedure]

BACKGROUND

Accident insurance consultants (D-physicians) are qualified specialists with particular expertise in occupational medicine. Within the medical treatment procedure of the German Statutory Accident Insurance (DGUV), D‑physicians must make a report on the medical care after occupational accidents. This nationwide evaluation aimed to systematically measure the quality of documentation of these medical reports. Peer review is a common method to ensure process quality.

MATERIAL AND METHODS

For each included D‑physician 30 reports of more severe cases from 2017 were randomly selected. The reports were anonymized and randomly assigned to a peer reviewer. Peer reviewers used a web-based checklist with nine rating categories and dichotomous response format (deficiency/no deficiency). To evaluate overall quality each report was rated with an overall grade from 1 (very good) to 6 (insufficient).

RESULTS

A total of 30,384 reports were evaluated by 82 peer reviewers. One third of the reports contained no deficiencies. Most deficiencies were found in the category on information about the accident. The mean overall grade for each D‑physician was 2.6 and ranged from 1.5 (best) to 4.1 (worst). All evaluated D‑physicians were given an individual quality report which described the main findings.

CONCLUSION

The first nationwide peer review of the DGUV proved to be a practical and valid quality assurance procedure to evaluate the medical reports of D‑physicians. The quality of the reports was in general good. The DGUV plans to repeat the peer review process taking further groups of D‑physicians into consideration.

The Emerging Role of SPECT Functional Neuroimaging in Schizophrenia and Depression

Over the last three decades, the brain's functional and structural imaging has become more prevalent in psychiatric research and clinical application. A substantial amount of psychiatric research is based on neuroimaging studies that aim to illuminate neural mechanisms underlying psychiatric disorders. Single-photon emission computed tomography (SPECT) is one of those developing brain imaging techniques among various neuroimaging technologies. Compared to PET, SPECT imaging is easy, less expensive, and practical for radioligand use. Current technologies increased the spatial accuracy of SPECT findings by combining the functional SPECT images with CT images. The radioligands bind to receptors such as 5-hydroxytryptamine 2A, and dopamine transporters can help us comprehend neural mechanisms of psychiatric disorders based on neurochemicals. This mini-review focuses on the SPECT-based neuroimaging approach to psychiatric disorders such as schizophrenia and major depressive disorder (MDD). Research-based SPECT findings of psychiatric disorders indicate that there are notable changes in biochemical components in certain disorders. Even though many studies support that SPECT can be used in psychiatric clinical practice, we still only use subjective diagnostic criteria such as the Diagnostic Statistical Manual of Mental Disorders (DSM-5). Glimpsing into the brain's biochemical world via SPECT in psychiatric disorders provides more information about the pathophysiology and future implication of neuroimaging techniques.

Predicting change in diagnosis from major depression to bipolar disorder after antidepressant initiation

We aimed to develop and validate classification models able to identify individuals at high risk for transition from a diagnosis of depressive disorder to one of bipolar disorder. This retrospective health records cohort study applied outpatient clinical data from psychiatry and nonpsychiatry practice networks affiliated with two large academic medical centers between March 2008 and December 2017. Participants included 67,807 individuals with a diagnosis of major depressive disorder or depressive disorder not otherwise specified and no prior diagnosis of bipolar disorder, who received at least one of the nine antidepressant medications. The main outcome was at least one diagnostic code reflective of a bipolar disorder diagnosis within 3 months of index antidepressant prescription. Logistic regression and random forests using diagnostic and procedure codes as well as sociodemographic features were used to predict this outcome, with discrimination and calibration assessed in a held-out test set and then a second academic medical center. Among 67,807 individuals who received at least one antidepressant medication, 925 (1.36%) subsequently received a diagnosis of bipolar disorder within 3 months. Models incorporating coded diagnoses and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging from 0.73 to 0.80). Standard supervised machine learning methods enabled development of discriminative and transferable models to predict transition to bipolar disorder. With further validation, these scores may enable physicians to more precisely calibrate follow-up intensity for high-risk patients after antidepressant initiation.

Les progrès dans la réalisation de la classification quantitative de la psychopathologie☆

Shortcomings of approaches to classifying psychopathology based on expert consensus have given rise to contemporary efforts to classify psychopathology quantitatively. In this paper, we review progress in achieving a quantitative and empirical classification of psychopathology. A substantial empirical literature indicates that psychopathology is generally more dimensional than categorical. When the discreteness versus continuity of psychopathology is treated as a research question, as opposed to being decided as a matter of tradition, the evidence clearly supports the hypothesis of continuity. In addition, a related body of literature shows how psychopathology dimensions can be arranged in a hierarchy, ranging from very broad "spectrum level" dimensions, to specific and narrow clusters of symptoms. In this way, a quantitative approach solves the "problem of comorbidity" by explicitly modeling patterns of co-occurrence among signs and symptoms within a detailed and variegated hierarchy of dimensional concepts with direct clinical utility. Indeed, extensive evidence pertaining to the dimensional and hierarchical structure of psychopathology has led to the formation of the Hierarchical Taxonomy of Psychopathology (HiTOP) Consortium. This is a group of 70 investigators working together to study empirical classification of psychopathology. In this paper, we describe the aims and current foci of the HiTOP Consortium. These aims pertain to continued research on the empirical organization of psychopathology; the connection between personality and psychopathology; the utility of empirically based psychopathology constructs in both research and the clinic; and the development of novel and comprehensive models and corresponding assessment instruments for psychopathology constructs derived from an empirical approach.

Distribution of agitation and related symptoms among hospitalized patients using a scalable natural language processing method

BACKGROUND

Agitation is a common feature of many neuropsychiatric disorders.

OBJECTIVE

Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases.

METHODS

We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined.

RESULTS

Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype.

CONCLUSION

Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Counterpoint. Early intervention for psychosis risk syndromes: Minimizing risk and maximizing benefit

BACKGROUND

Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis.

METHOD

Response to points of critique.

RESULTS

We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma.

DISCUSSION

Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

A multi-institutional study assessing general surgery faculty teaching evaluations

BACKGROUND

Resident evaluation of faculty teaching is an important metric in general surgery training, however considerable variability in faculty teaching evaluation (FE) instruments exists.

STUDY DESIGN

Twenty-two general surgery programs provided their FE and program demographics. Three clinical education experts performed blinded assessment of FEs, assessing adherence 2018 ACGME common program standards and if the FE was meaningful.

RESULTS

Number of questions per FE ranged from 1 to 29. The expert assessments demonstrated that no evaluation addressed all 5 ACGME standards. There were significant differences in the FEs effectiveness of assessing the 5 ACGME standards (p < 0.001), with teaching abilities and professionalism rated the highest and scholarly activities the lowest.

CONCLUSION

There was wide variation between programs regarding FEs development and adhered to ACGME standards. Faculty evaluation tools consistently built around all suggested ACGME standards may allow for a more accurate and useful assessment of faculty teaching abilities to target professional development.

Validation of a Histologic Scoring Index for C3 Glomerulopathy

RATIONALE & OBJECTIVE

A previous study that evaluated associations of kidney biopsy findings with disease progression in patients with C3 glomerulopathy (C3G) proposed a prognostic histologic index (C3G-HI) that has not yet been validated. Our objective was to validate the performance of the C3G-HI in a new patient population.

STUDY DESIGN

Multicenter, retrospective cohort study.

SETTING & PARTICIPANTS

111 patients fulfilling diagnostic criteria of C3G between January 1995 and December 2019, from 33 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases (GLOSEN).

PREDICTORS

Demographic, clinical parameters, C3G-HI total activity score, and the C3G-HI total chronicity score.

OUTCOME

Time to kidney failure.

ANALYTICAL APPROACH

Intraclass correlation coefficients and κ statistic were used to summarize inter-rater reproducibility for assessment of histopathology in kidney biopsies. The nonlinear relationships of risk of kidney failure with the total activity score and total chronicity score were modeled using Cox proportional hazards analysis that incorporated cubic splines.

RESULTS

The study group included 93 patients with C3 glomerulonephritis and 18 with dense-deposit disease. Participants had an overall meanage of 35±22 (SD) years. Forty-eight patients (43%) developed kidney failure after a mean follow-up of 65±27 months. The overall inter-rater reproducibility was very good for the total activity score (intraclass correlation coefficient [ICC]=0.63) and excellent for total chronicity score (ICC=0.89). Baseline estimated glomerular filtration rate (eGFR), 24-hour proteinuria, and treatment with immunosuppression were the main determinants of kidney failure in a model with only clinical variables. Only tubular atrophy and interstitial fibrosis were identified as predictors in a model with histological variables. When the total activity score and total chronicity score were added to the model, only the latter was identified as an independent predictor of kidney failure.

LIMITATIONS

Only a subset of the kidney biopsies was centrally reviewed. Residual confounding.

CONCLUSIONS

We validated the performance of C3G-HI as a predictor of kidney failure in patients with C3G. The total chronicity score was the principal histologic correlate of kidney failure.

A multidisciplinary approach to insanity assessment as a way to reduce cognitive biases

Insanity assessment requires the evaluation of the psychopathological condition that underlies the mens rea. Psychopathological evaluation may be quite challenging due to (i) absence of biomarkers; (ii) low inter-rater reliability; (iii) presence of cognitive bias. This intrinsic low reliability of forensic psychiatric diagnosis does impact on insanity assessment, leading to arbitrary and unjust legal outcomes for the examinee. Thus, strategies to improve the reliability of insanity evaluation are strongly needed. A multidisciplinary approach has been proposed as a way to enrich clinical diagnosis with reliable and biologically founded data, thus minimizing subjectivity, reducing controversies and increasing inter-subject concordance in insanity assessment. By discussing a real case, here we show how the convergence of multiple indices can produce evidence that cannot be denied without introducing logical fallacies. Applying this approach, the forensic discussion will move from the presence/absence of psychopathology to the impact of psychopathology on insanity. This article illustrates how a multidisciplinary evaluation, which integrates neuroscientific methods with the classical insanity assessment, may lead to a more accurate approach in insanity evaluation. Critically, this approach will minimize the impact of cognitive bias on insanity opinion and thus result in an improvement of the whole criminal justice process.

Generalizable brain network markers of major depressive disorder across multiple imaging sites

Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.

Questionable Agreement: The Experience of Depression and DSM-5 Major Depressive Disorder Criteria

Immediately before the release of DSM-5, a group of psychiatric thought leaders published the results of field tests of DSM-5 diagnostic criteria. They characterized the interrater reliability for diagnosing major depressive disorder by two trained mental health practitioners as of "questionable agreement." These field tests confirmed an open secret among psychiatrists that our current diagnostic criteria for diagnosing major depressive disorder are unreliable and neglect essential experiences of persons in depressive episodes. Alternative diagnostic criteria exist, but psychiatrists rarely encounter them, forestalling the discipline's epistemological crisis. In Alsadair MacIntyre's classic essay, such crises occur in science when a person encounters a rival schemata that is incompatible with their current schemata and subsequently constructs a narrative that allows them to reconstruct their own tradition. In search of rival schemata that are in conversation with their own tradition, psychiatric practitioners can utilize alternative diagnostic criteria like the Cultural Formulation Interview, embrace an epistemologically humble psychiatry, and attend to the narrative experience of a person experiencing a depressive episode.

[New insights into diagnostics and therapy of personality disorders-Changes in ICD-11]

Personality disorders (PD) occur frequently and show high remission rates in the long term, while psychosocial recovery remains unsuccessful in a substantial proportion of cases. In ICD-11 the traditional view that PDs have a high stability is abandoned. Instead, the minimum duration is 2 years. The diagnostic process differentiates between three degrees of severity (mild, moderate, severe) and five prominent personality trait domains. Optionally, a borderline qualifying factor can be additionally codified. There is sufficient empirical evidence only for the treatment of borderline PD (BPD). Disorder-specific psychotherapy, in particular dialectic behavioral therapy (DBT) and mentalization-based therapy (MBT) have proven to be effective. Therapy modules targeting functional impairments and prominent personality trait domains could close the existing gaps in the disorder-specific treatment of PD.

The prospects for sustaining evidence-based responses to the US opioid epidemic: state leadership perspectives

BACKGROUND

The US 21st Century Cures Act provided $7.5 billion in grant funding to states and territories for evidence-based responses to the opioid epidemic. Currently, little is known about optimal strategies for sustaining these programs beyond this start-up funding.

METHODS

Using an inductive, conventional content analysis, we conducted key informant interviews with former and current state leaders (n = 16) about barriers/facilitators to sustainment and strategies for sustaining time-limited grants.

RESULTS

Financing and reimbursement, service integration, and workforce capacity were the most cited barriers to sustainment. Status in state government structure, public support, and spending flexibility were noted as key facilitators. Effective levers to increase chances for sustainment included strong partnerships with other state agencies, workforce and credentialing changes, and marshalling advocacy through public awareness campaigns.

CONCLUSIONS

Understanding the strategies that leaders have successfully used to sustain programs in the past can inform how to continue future time-limited, grant-funded initiatives.

Non-psychiatric hospitalization length-of-stay for patients with psychotic disorders: A mixed methods study

OBJECTIVE

Patients with psychotic disorders experience higher rates of chronic and acute non-psychotic diseases and have frequent non-psychiatric hospitalizations which result in both longer and more varied length-of-stay (LoS) than other patients. This study seeks to use a patient-centered perspective to examine LoS.

METHODS

This article reports Phase Two of a mixed methods, exploratory sequential study on non-psychiatric hospitalizations for individuals with psychotic disorders. Patients' experiences were used to guide a quantitative analysis of LoS using a general linear model.

RESULTS

Medical comorbidities were the patient characteristics which had the largest effect on LoS. Certain processes of care highlighted by patients from Phase One were also associated with longer LoS, including: physical restraints (105%), psychiatric consults (20%) and continuous observation (133%). Only recent in-system outpatient appointments were associated with shorter LoS. Data integration highlighted that factors which were important to patients such as partner support, were not always quantitatively significant, while others like medical comorbidities and use of physical restraints were points of congruence.

CONCLUSIONS

Medical comorbidities were highly associated with LoS but processes relating to longer LoS are those that are used to manage symptoms of acute psychosis. Clinicians should develop policies and procedures that address psychosis symptoms effectively during non-psychiatric hospitalizations. Further research is needed to understand which patients with psychotic disorders are at highest risk of extended length-of-stay.

Test-retest & familial concordance of MDD symptoms

Psychopathology research has increasingly sought to study the etiology and treatment of individual symptoms, rather than categorical diagnoses. However, it is unclear whether commonly used measures have adequate psychometric properties for assessing individual symptoms. This study examined the test-retest reliability and familial concordance (an indicator of validity) of the symptoms of Major Depressive Disorder (MDD), a disorder consisting of nine core symptoms, most of which are aggregated (e.g., symptom 7 of the DSM criteria for MDD is worthlessness or guilt). Lifetime MDD symptoms were measured in 504 young adults (237 sibling pairs) using the Structured Clinical Interview for DSM-5 (SCID). Fifty-one people completed a second SCID within three weeks of their first SCID. Results indicated that aggregated and unaggregated symptoms demonstrated moderate to substantial test-retest reliability and generally significant, but slight to fair familial concordance (with the highest familial concordance being for markedly diminished interest or pleasure and its unaggregated components - decreased interest and decreased pleasure). Given the increasing focus on the differential validity of individual MDD symptoms, the present study suggests that interview-based assessments of depression can assess most individual symptoms with adequate levels of reliability and validity.

Voices: Are They Dissociative or Psychotic?

Auditory hallucinations are widely regarded as symptoms of brain disease treated with medications. In an alternative paradigm, voices are understood as trauma-driven dissociated, disowned, or disavowed aspects of self; the goal is not to suppress them but to integrate them during psychotherapy. Auditory hallucinations are common in dissociative identity disorder, borderline personality disorder, and complex posttraumatic stress disorder and are not specific to psychosis. The features that differentiate psychotic from dissociative voices include the qualities of the voices themselves, as well as other symptoms: for example, compared with dissociative voices, psychotic voices are accompanied by less sociability, more formal thought disorder, more negative symptoms including blunted affect, and more delusions. The author proposes that the psychotherapy of dissociative voices can be indicated trans-diagnostically, including in a subgroup of individuals with diagnoses of schizophrenia. Psychotherapeutic strategies are illustrated with a case example.

Antidepressant and anti-amnesic effects of the aqueous lyophilisate of the leaves of Leptadenia arborea on an animal model of cognitive deficit associated depression

Leptadenia arborea (Asclepiadaceae) is a plant used in traditional medicine to treat syphilis, migraine, and mental illnesses. The aim of our study was to investigate possible antidepressant and anti-amnesic effects of the aqueous lyophilisate of the leaves of Leptadenia arborea in an animal model of cognitive deficit associated depression. Swiss albino adult mice of both sexes were used for this study. A 14-day combined stress model was used to induce depression with early cognitive deficits. The forced swimming test, the open field test and plasma corticosterone level were used to assess antidepressant-like effect. The novel object recognition task (NORT), the Morris Water Maze (MWM) and neurochemical analysis of hippocampal acetylcholinesterase activity was also carried out to assess memory integrity. The aqueous lyophelisate of L. arborea increased swimming time and decreased immobility time in the forced swimming test. In the open field test they was no difference in the number of lines crossed between groups, and the lyophilisate-treated mice spent more time in the centre compared to the control. The lyophilisate decreased the plasma level of corticosterone compared to the control. The lyophilisate decreased the latency to reach the hidden platform and increased the time spent in the target quadrant in the MWM. The lyophilisate also increased the time of exploration of the novel object in the NORT and decreased the acetylcholinesterase activity in the hippocampus. L. arborea effects were decreased when it was co-administered with pCPA. Results suggest that the aqueous lyophilisate of the leaves of L. arborea possess antidepressant-like and anti-amnesic effects.

Icelandic translation and reliability data on the DSM-5 version of the schedule for affective disorders and schizophrenia for school-aged children - present and lifetime version (K-SADS-PL)

BACKGROUND

The Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) is a semi-structured interview based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), and assesses past and present symptoms in children aged 6-18 years old. It has been translated into more than 20 languages and is widely used in clinical work. The K-SADS-PL has recently been revised by adopting new DSM-5 disorders and modifying probes accordingly. However, research on this revision is critically limited. The objective of this study was to examine the inter-rater reliability of the DSM-5 K-SADS-PL when administered by postgraduate students after receiving thorough training and supervision.

METHOD

The DSM-5 K-SADS-PL was applied in a clinical sample of two outpatient units: The outpatient unit of the Department of Child and Adolescent Psychiatry at the Landspítali University Hospital in Reykjavík, Iceland and The Icelandic Anxiety Centre for Children, Adolescents and Young Adults.

RESULTS

Fourty-one (80%) consecutive patients aged 6-18 years consented and were included. Cohen's κ was calculated to estimate inter-rater reliability, with estimates ranging from fair to excellent (κ = 0.57-0.90), with most diagnoses in the excellent range (κ > 0.75).

CONCLUSIONS

These results indicate that the Icelandic translation of the DSM-5 K-SADS-PL can be reliably administered by postgraduate students.

Fractures in the framework: limitations of classification systems in psychiatry

This article examines the limitations of existing classification systems from the historical, cultural, political, and legal perspectives. It covers the evolution of classification systems with particular emphasis on the DSM and ICD systems. While pointing out the inherent Western bias in these systems, it highlights the potential of misuse of these systems to subserve other agendas. It raises concerns about the reliability, validity, comorbidity, and heterogeneity within diagnostic categories of contemporary classification systems. Finally, it postulates future directions in alternative methods of diagnosis and classification factoring in advances in artificial intelligence, machine learning, genetic testing, and brain imaging. In conclusion, it emphasizes the need to go beyond the limitations inherent in classifications systems to provide more relevant diagnoses and effective treatments.


Heterogeneity and Subtyping in Attention-Deficit/Hyperactivity Disorder-Considerations for Emerging Research Using Person-Centered Computational Approaches

Few if any experts believe that existing psychiatric diagnostic categories included in DSM and ICD are actually discrete disease entities. Attention-deficit/hyperactivity disorder (ADHD) is emblematic of the problems in the existing psychiatric classification system. ADHD symptoms reliably cluster into two correlated dimensions in factor analysis. However, children with ADHD vary considerably in their symptom profiles, symptom trajectories, clinical outcomes, and biological and psychological correlates. Thus, the field has sought alternative approaches that harness the dimensions of emotional, cognitive, and behavioral functioning that underlie ADHD and other existing psychiatric categories to create informative phenotypes that improve clinical prediction and clarify etiology. Within ADHD, cognitive (neuropsychological) and temperament/personality features have received considerable attention. In some cases, subphenotypes based on these features appear to improve on existing classifications and could eventually be translated into clinical practice. This review summarizes findings from subphenotyping efforts in ADHD that use cognitive, emotion-related, and other features to highlight major considerations for research applying person-oriented approaches to inform an improved psychiatric nosology. Considerations related to feature selection, validation of newly proposed divisions, defining populations of interest, and incorporating a developmental perspective are discussed.

Methods and Challenges for Assessing Heterogeneity

The widely acknowledged homogeneity assumption limits progress in refining clinical diagnosis, understanding mechanisms, and developing new treatments for mental health disorders. This homogeneity assumption drives both a comorbidity and a heterogeneity problem, where two different approaches tackle the problems. One, a unifying approach, tackles the comorbidity problem by assuming that a single general psychopathology factor underlies multiple disorders. Another, a multifactorial approach, tackles the heterogeneity problem by assuming that disorders comprise multiple subtypes driven by multiple discrete factors. We show how each of these approaches can make useful contributions to mental health-related research and clinical practice. For example, the unifying approach can develop a rapid assessment tool that may be clinically valuable for triaging cases. The multifactorial approach can reveal subtypes that are differentially responsive to treatments and highlight distinct mechanisms leading to similar phenotypes. Because both approaches tackle different problems, both have different limitations. We describe the statistical frameworks that incorporate and adjudicate between both approaches (e.g., the bifactor model, normative modeling, and the functional random forest). Such frameworks can identify whether sets of disorders are more affected by heterogeneity or comorbidity. Therefore, future studies that incorporate such frameworks can provide further insight into the nature of psychopathology.

Assessing the Educational Quality of Facebook Videos as an Informative Resource on Otitis Media

OBJECTIVE

This study analyzes the quality and reliability of otitis media (OM) videos on Facebook and investigates whether the videos shared within the Facebook community are considered to be a valuable educational tool. The results of this study are important for providing clinicians with the necessary understanding about the video content that their patients may be exposed to.

STUDY DESIGN

Cross-sectional analysis of video content.

SETTING

A new Facebook account was created to carry out a search for videos on OM.

METHODS

Inclusion criteria were as follows: videos intended for educating patients or guardians on OM, videos in the English language, and videos with at least 1 share.

RESULTS

A total of 364 videos were screened, and 62 fit our inclusion criteria for analysis. The majority (56%) of OM videos on Facebook focused on complementary and alternative medication without mentioning any current guidelines. A limited amount of videos (29%) made any mention to surgical treatment options for OM. There was a strong positive correlation (rho = 0.8419, P < .001) between a video's content and its reliability. There was no correlation seen between a video's content and its shares (rho = -0.142, P = .1359).

CONCLUSIONS

The majority of OM videos on Facebook are inadequate for educational value. Clinicians should know about the existence of videos on OM and the quality of information that parents are exposed to.

Psychometric Properties of Persian Version of Structured Clinical Interview for DSM-5-Research Version (SCID-5-RV): A Diagnostic Accuracy Study

Background: The structured clinical interview for DSM-5 has recently been revised to reflect the new findings in the diagnostic criteria of psychological disorders. Objectives: The present study aimed to evaluate the reliability and validity of the Persian translation of structured clinical interview for diagnostic and statistical manual of mental disorders-fifth edition (DSM-5)-research version (SCID-5-RV) on Iranian adult population. Methods: In the current diagnostic accuracy study a total of 305 clinical samples were admitted to fifteen adult clinical settings and a subsample of these participants (n = 50, with a mean age of 34.31 and a standard deviation of 11.96) was recruited to evaluate test-retest reliability, and 40 non-clinical participants were recruited to examine construct validity. All participants completed the Millon Clinical Multiaxial inventory-III (MCMI-III) and Brief Symptom inventory (BSI). Results: SCID psychometric properties indicated an acceptable range for internal consistency (0.95 - 0.99), test-retest reliability (0.60 - 0.79), and Kappa reliability (0.57 - 0.72). Further, the agreement between interviewer and psychiatrist diagnoses was assessed using the Kappa index, and the result was satisfactory. The current diagnostic accuracy study used sensitivity and specificity indexes to assess the diagnostic validity of SCID by positive predictive value and also negative predictive value under the “likelihood ratio” domain. Specificity values for most psychiatric disorders were high; the sensitivity values were to somewhat lower. Furthermore, SCID-5-RV categorical diagnoses demonstrated an acceptable construct validity based on the significant differences between the clinical and non-clinical samples in all subscales of BSI except for phobia as well as all clinical subscales of MCMI-III. Conclusions: In general, the Persian translation of SCID-5-RV represented acceptable reliability and validity for various categorical diagnoses in different clinical settings.

Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum

The Hierarchical Taxonomy of Psychopathology (HiTOP) is a scientific effort to address shortcomings of traditional mental disorder diagnoses, which suffer from arbitrary boundaries between psychopathology and normality, frequent disorder co-occurrence, heterogeneity within disorders, and diagnostic instability. This paper synthesizes evidence on the validity and utility of the thought disorder and detachment spectra of HiTOP. These spectra are composed of symptoms and maladaptive traits currently subsumed within schizophrenia, other psychotic disorders, and schizotypal, paranoid and schizoid personality disorders. Thought disorder ranges from normal reality testing, to maladaptive trait psychoticism, to hallucinations and delusions. Detachment ranges from introversion, to maladaptive detachment, to blunted affect and avolition. Extensive evidence supports the validity of thought disorder and detachment spectra, as each spectrum reflects common genetics, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, biomarkers, and treatment response. Some of these characteristics are specific to one spectrum and others are shared, suggesting the existence of an overarching psychosis superspectrum. Further research is needed to extend this model, such as clarifying whether mania and dissociation belong to thought disorder, and explicating processes that drive development of the spectra and their subdimensions. Compared to traditional diagnoses, the thought disorder and detachment spectra demonstrated substantially improved utility: greater reliability, larger explanatory and predictive power, and higher acceptability to clinicians. Validated measures are available to implement the system in practice. The more informative, reliable and valid characterization of psychosis-related psychopathology offered by HiTOP can make diagnosis more useful for research and clinical care.

Prevalence and correlates of generalized anxiety disorder among elderly people in primary health care

ABSTRACT Objective To examine the prevalence of generalized anxiety disorder (GAD) and its associations with sociodemographic and health factors. Methods A cross-sectional study with a population-based sample of 578 individuals aged 60 years or older from the Family Health Strategy (FHS) program of Porto Alegre, RS, Brazil. Home visit screening and general data collection were made by trained Community Health Workers (CHWs). Diagnoses of psychiatric disorders were made by board-certified psychiatrists using the Mini International Neuropsychiatric Interview plus (MINIplus) in the Hospital São Lucas of the Pontifical University of Rio Grande do Sul (PUCRS). Results GAD was found in 9% of the sample (n = 52; CI 95% = 6.9-11.6). The main results of the multivariate analysis show associations between GAD and retirement (PR: 0.43, CI: 0.25-0.76), history of falls (PR: 2.52, CI: 1.42-4.49), cohabitation with four or more people (PR: 1.80, CI: 1.04-3.13), having more than one hospitalization in the last year (PR: 2.53, CI: 1.17-5.48) and self-perception of health as regular (PR: 2.75, CI: 1.02-7.47). Retirement in the elderly shows 2.32x less risk of GAD, although confounding factors may have overestimated this finding and underestimated the association with female gender (PR: 1.61, CI: 0.83-3.10). Conclusions We estimate a high prevalence of GAD in this population. Associations were found between GAD and health self-perceived as regular, cohabitation with four or more people, history of falls and more than one hospitalization in the last year. These epidemiological data from the Family Health Strategy are important to develop further strategies for this age group that could improve the health care practice.

Evaluating and Validating GBI Mania and Depression Short Forms for Self-Report of Mood Symptoms

Objective: To evaluate short forms of free self-report mania and depression scales, evaluating their reliability, content coverage, criterion validity, and diagnostic accuracy.Method: Youths age 11 to 18 years seeking outpatient mental health services at either an Academic medical clinic (N = 427) or urban Community mental health center (N = 313), completed the General Behavior Inventory (GBI) and other rating scales. Youths and caregivers completed semi-structured interviews to establish diagnoses and mood symptom severity, with GBI scores masked during diagnosis. Ten- and seven-item short forms, psychometric projections, and observed performance were tested first in the Academic sample and then externally cross-validated in the Community sample.Results: All short forms maintained high reliability (all alphas >.80 across both samples), high correlations with the full-length scales (r.85 to.96), excellent convergent and discriminant validity with mood, behavior, and demographic criteria, and diagnostic accuracy undiminished compared to using the full-length scales. Ten-item scales showed advantages in terms of coverage; the 7 Up showed slightly weaker performance.Conclusions: Present analyses evaluated and externally cross-validated short forms that maintain high reliability and content coverage, and show strong criterion validity and diagnostic accuracy - even when used in an independent sample with very different demographics and referral patterns. The short forms appear useful in clinical applications including initial evaluation, as well as in research settings where they offer an inexpensive quantitative score. Short forms are available in more than two dozen languages. Future work should further evaluate sensitivity to treatment effects and cultural invariance.

Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders

A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.

Symptom-based selection of antidepressants

Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.

Representational Rényi Heterogeneity

A discrete system’s heterogeneity is measured by the Rényi heterogeneity family of indices (also known as Hill numbers or Hannah–Kay indices), whose units are the numbers equivalent. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems with ill-defined categories or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational Rényi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the Rényi heterogeneity is both tractable and semantically relevant. This method requires neither a priori binning nor definition of a distance function on the observable space. We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices.

Population neuroscience: challenges and opportunities for psychiatric research in low- and middle-income countries

Objective:

Population neuroscience is an emerging field that combines epidemiology and neuroscience to study how genes and the environment shape typical and atypical brain functioning. The objective of this study was to review key studies on population neuroscience from low- and middle-income countries (LMICs) and to identify potential gaps vis-à-vis studies conducted in high-income countries.

Methods:

We conducted a systematic review to search for longitudinal cohort studies investigating the development of psychiatric disorders in children and adolescents in LMICs. We performed an electronic search in the EMBASE and MEDLINE databases from inception to July 5th, 2019.

Results:

We found six cohorts from four countries that met our search criteria: three cohorts from Brazil, one from China, one from South Africa, and one from Mauritius. Relevant examples of findings from these studies are reported.

Conclusion:

Our results demonstrate the impact of the valuable science output these cohort designs promote, allowing LMICs to have a share in frontline global psychiatry research. National and international funding agencies should invest in LMIC population neuroscience in order to promote replication and generalization of research from high-income countries.