Categorical Diagnosis Research Articles

The Aspirations for a Paradigm Shift in DSM-5: An Oral History.

The initial plans for the DSM-5 revision envisioned a paradigm shift away from traditional diagnostic categories. However, plans for a major move from descriptive to etiologic diagnoses were quickly abandoned as infeasible. Support was much broader for adding dimensional/spectrum constructs to the categorical diagnoses, although this was interpreted in various ways. Delegation of substantial autonomy to work groups with modest central coordination was seen as problematic by some work groups and positively by others. Controversies emerged around the standards for diagnostic change, and the degree to which the same standards should be used across diagnostic groups. The Summit Group was given the final task of trying to forge a consensus among the various review groups. We conclude with thoughts about the difficulty of trying to revise an entire manual all at once and the desirability of developing clear rules for change at the outset of such a diagnostic project.

A Brief but Comprehensive Review of Research on the Alternative DSM-5 Model for Personality Disorders.

Both the Alternative DSM-5 Model for Personality Disorders (AMPD) and the chapter on personality disorders (PD) in the recent version of ICD-11 embody a shift from a categorical to a dimensional paradigm for the classification of PD. We describe these new models, summarize available measures, and provide a comprehensive review of research on the AMPD. A total of 237 publications on severity (criterion A) and maladaptive traits (criterion B) of the AMPD indicate (a) acceptable interrater reliability, (b) largely consistent latent structures, (c) substantial convergence with a range of theoretically and clinically relevant external measures, and (d) some evidence for incremental validity when controlling for categorical PD diagnoses. However, measures of criterion A and B are highly correlated, which poses conceptual challenges. The AMPD has stimulated extensive research with promising findings. We highlight open questions and provide recommendations for future research.

Anxiety, depression, and birth outcomes in a cohort of unmedicated women

Background Even though most of the systematic reviews suggest that depression and anxiety are related to poor neonatal outcomes, it is not yet clear whether a dose–response effect exists. Aim The aim of the present study is to evaluate the amount of depressive and anxiety symptoms in a cohort of pregnant women and its effect on their newborns. Methods Two hundred ninety-nine women were assessed for anxiety and depressive disorders and anxious and depressive symptoms at near monthly intervals throughout pregnancy. At the time of delivery, we collected the newborns’ gestational age, birth weight and the Apgar score at 1 and 5 min. Results Sixty-seven women were diagnosed as depressed and 43 had an anxious disorder. After controlling for confounding variables only the overall levels of anxiety during pregnancy were negatively associated with birth weight (B = −5.76; 95% CI = −10.96, −2.81), suggesting the existence of a “dose–response” effect. The birth outcomes in mildly depressed pregnant women were similar to those of nondepressed women. Conclusion Anxiety symptoms, beyond a categorical diagnosis, are associated with low birth weight and should be recognized and properly treated during pregnancy.

White Matter Microstructure in Pediatric Bipolar Disorder and Disruptive Mood Dysregulation Disorder

Disruptive mood dysregulation disorder (DMDD) codifies severe, chronic irritability. Youths with bipolar disorder (BD) also present with irritability, but with an episodic course. To date, it is not clear whether aberrant white matter microstructure-a well-replicated finding in BD-can be observed in DMDD and relates to symptoms of irritability. We acquired diffusion tensor imaging data from 118 participants (BD= 36, DMDD= 44, healthy volunteers (HV= 38). Images of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) were processed with tract-based spatial statistics controlling for age and sex. The data were also used to train Gaussian process classifiers to predict diagnostic group. In BD vs DMDD, FA in the corticospinal tract was reduced. In DMDD vs HV, reductions in FA and AD were confined to the anterior corpus callosum. In BD vs HV, widespread reductions in FA and increased RD were observed. FA in the anterior corpus callosum and corticospinal tract was negatively associated with irritability. The Gaussian process classifier could not discriminate between BD and DMDD, but achieved 68% accuracy in predicting DMDD vs HV and 75% accuracy in predicting BD vs HV. Aberrant white matter microstructure was associated with both categorical diagnosis and the dimension of irritability. Alterations in DMDD were regionally discrete and related to reduced AD. In BD, we observed widespread increases in RD, supporting the hypothesis of altered myelination in BD. These findings will contribute to the pathophysiological understanding of DMDD and its differentiation from BD. Studies of Brain Function and Course of Illness in Pediatric Bipolar Disorder; https://clinicaltrials.gov/; NCT00025935; Child & Adolescent Bipolar Disorder Brain Imaging and Treatment Study; https://clinicaltrials.gov/; NCT00006177.

Development of the 12-item questionnaire for quantitative assessment of depressive mixed state (DMX-12).

BackgroundConventional categorical criteria have limitations in assessing the prevalence and severity of depressive mixed state (DMX). Thus, we have developed a new scale for screening and quantification of DMX and examined the symptomatological structure and severity of DMX in individuals with major depressive episode (MDE).MethodsSubjects were 154 patients with MDE (57 males and 97 females; age 13–83 years). Our original Japanese version of the self-administered 12-item questionnaire to assess DMX (DMX-12), together with the Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-SR-J) and global assessment of functioning, were administered to each participant. The symptomatological structure of the DMX-12 was examined by exploratory factor analysis. Multiple regression analyses were used to analyze factors contributing to the DMX-12 scale. The relationships of this scale with categorical diagnoses (mixed depression by Benazzi and mixed features by DSM-5) were also investigated.ResultsA three-factor model of the DMX-12 was extracted from exploratory factor analysis, namely, “spontaneous instability”, “vulnerable responsiveness”, and “disruptive emotion/behavior”. Multiple regression analyses revealed that age was negatively correlated with total DMX-12 score, while bipolarity and the QIDS-SR-J score were positively correlated. A higher score on the disruptive emotion/behavior subscale was observed in patients with mixed depression and mixed features.ConclusionThe DMX-12 seems to be useful for screening DMX in conjunction with conventional categorical diagnoses. Severely depressed younger subjects with potential bipolarity are more likely to develop DMX. The disruptive emotion/behavior subscale of the DMX-12 may be the most helpful in distinguishing patients with DMX from non-mixed patients.

Predicting the course of Alzheimer\u2019s progression

Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.

Memory improvement in African Americans with amnestic mild cognitive impairment.

Amnestic mild cognitive impairment (aMCI) has an uncertain course. Valid methods to evaluate memory change will best identify predictors of course. This issue is especially relevant to older persons in minority groups, who may have encountered life course factors that adversely affect cognition. Growth curve mixture models were used to identify trajectories of memory test scores obtained every 6 months over 2 years in 221 African Americans with aMCI. Participants sorted into two classes, with clinically and statistically significant differences in memory scores over time. Class 1 (n = 28 [14.7%]) had sustained improved scores. Class 2 (n = 162 [85.3%]) scores remained low, fluctuated, or declined. Class 1 had better baseline cognition and daily function than class 2. The observed rate of improved memory is lower than reported reversion rates from aMCI to normal cognition. Evaluating trajectories of memory test scores rather than changes in categorical diagnoses of aMCI, which may depend on recalling (or not recalling) one or two words, may yield a more valid indicator of cognitive change. These approaches require further study in minority groups.

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. Additionally, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future precision medicine innovations in mental health.

Association between inflammatory cytokines and ADHD symptoms in children and adolescents with obesity: A pilot study

Whilst the association between Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity is supported by meta-analytic evidence, the mechanisms underpinning this link need to be further elucidated. Inflammatory processes may increase the risk of ADHD symptoms in individuals with obesity. This pilot study set out to start testing this hypothesis by assessing the correlation between serum levels of inflammatory cytokines and ADHD symptoms severity in a sample of children and adolescents with obesity. We measured ADHD symptoms severity in 52 children/adolescents with obesity (BMI > 95th centile) with the Conners questionnaire, revised, short version, parent (CPRS-R:S) and teacher (CTRS-R:S) versions. Additionally, a categorical diagnosis of ADHD was established using the Kiddie-SADS-PL. Serum levels of IL-6, Il-10, and TNF-alpha were also obtained. The prevalence of ADHD was 9.6%. We found a significant correlation between IL-6, as well as TNF-alpha, and hyperactivity/impulsivity subscores of the CPRS-R:S and CTRS-R:S, that held even after controlling for BMI and oppositional symptoms. This study provides a rationale for larger, longitudinal studies to gain insight into inflammatory processes underpinning the link between obesity and ADHD. This line of research has the potential to lead to novel, pathophysiologically-based management strategies for individuals with obesity and ADHD.

A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample.

Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD). Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old). The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/. The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.

Construct Validity of the PHQ-9 Depression Screen: Correlations with Substantive Scales of the MMPI-2-RF.

The PHQ-9 is a brief, 9-item, self-administered screening tool widely used in primary care medical settings to assess the potential presence of Major Depressive Disorder (MDD). Most published research on the PHQ-9 has focused on sensitivity and specificity with regard to the DSM-IV (American Psychiatric Association, in Diagnostic and statistical manual of mental disorders, 4th edn, American Psychiatric Publishing, Arlington, VA, 2000) categorical diagnosis of MDD, and, indeed, the PHQ-9 exhibits very good psychometric properties in this regard. The current research is an effort to more precisely assess what is being measured by the PHQ-9, given the notably heterogeneous nature of MDD as broad diagnostic category. Here, we provide correlations between the PHQ-9 and substantive scales of the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, in Minnesota Multiphasic Personality Inventory-Restructured Form: Manual for administration, scoring, and interpretation, University of Minnesota Press, Minneapolis, 2008/2011), a comprehensive measure of personality and psychopathology. Participants were 231 college student volunteers who completed the PHQ-9 and MMPI-2-RF as components of a broader research program. Results show that the PHQ-9 was strongly correlated with the broad Higher-Order EID-Emotional/Internalizing Dysfunction scale of the MMPI-2-RF, as well as with RCd-Demoralization, RC7-Dysfunctional Negative Emotions, NEGE-r-Negative Emotionality, and several specific facet scales. Surprisingly, the correlation with RC2-Low Positive Emotions, was not among the strongest, despite the fact that the PHQ-9 ostensibly targets this specific feature of depression (anhedonia). Substantial correlations with the somatic/cognitive scales of the MMPI-2-RF were also found. Implications for modified interpretation of the PHQ-9, and the need for more precise dimensional (rather than categorical) screening tools, are discussed.

The Utility of Event-Related Potentials in Clinical Psychology.

Event-related potentials (ERPs) are direct measures of brain activity that can be leveraged for clinically meaningful research. They can relate robustly both to continuous measures of individual difference and to categorical diagnoses in ways that clarify similarities and distinctions between apparently related disorders and traits. ERPs can be linked to genetic risk, can act as moderators of developmental trajectories and responses to stress, and can be leveraged to identify those at greater risk for psychopathology, especially when used in combination with other neural and self-report measures. ERPs can inform models of the development of, and risk for, psychopathology. Finally, ERPs can be used as targets for existing and novel interventions and prevention efforts. We provide concrete examples for each of these possibilities by focusing on programmatic research on the error-related negativity and anxiety, and thus show that ERPs are poised to make greater contributions toward the identification, prediction, treatment, and prevention of mental disorders.

Transdiagnostic psychiatry: a systematic review.

The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the "transdiagnostic" approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self-defining transdiagnostic research meets such high expectations. A multi-step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word "transdiagnostic" in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical "TRANSD"iagnostic recommendations are proposed here to guide future research in this field.

Development of an algorithm for evaluating the impact of measurement variability on response categorization in oncology trials

BackgroundRadiologic assessments of baseline and post-treatment tumor burden are subject to measurement variability, but the impact of this variability on the objective response rate (ORR) and progression rate in specific trials has been unpredictable on a practical level. In this study, we aimed to develop an algorithm for evaluating the quantitative impact of measurement variability on the ORR and progression rate.MethodsFirst, we devised a hierarchical model for estimating the distribution of measurement variability using a clinical trial dataset of computed tomography scans. Next, a simulation method was used to calculate the probability representing the effect of measurement errors on categorical diagnoses in various scenarios using the estimated distribution. Based on the probabilities derived from the simulation, we developed an algorithm to evaluate the reliability of an ORR (or progression rate) (i.e., the variation in the assessed rate) by generating a 95% central range of ORR (or progression rate) results if a reassessment was performed. Finally, we performed validation using an external dataset. In the validation of the estimated distribution of measurement variability, the coverage level was calculated as the proportion of the 95% central ranges of hypothetical second readings that covered the actual burden sizes. In the validation of the evaluation algorithm, for 100 resampled datasets, the coverage level was calculated as the proportion of the 95% central ranges of ORR results that covered the ORR from a real second assessment.ResultsWe built a web tool for implementing the algorithm (publicly available at http://studyanalysis2017.pythonanywhere.com/). In the validation of the estimated distribution and the algorithm, the coverage levels were 93 and 100%, respectively.ConclusionsThe validation exercise using an external dataset demonstrated the adequacy of the statistical model and the utility of the developed algorithm. Quantification of variation in the ORR and progression rate due to potential measurement variability is essential and will help inform decisions made on the basis of trial data.

198. Mapping Neuro-Behavioral Relationships in Dimensional Geometric Embedding (N-BRIDGE) via Pharmacology, Computation and Clinical Neuroimaging: Unifying Categories and Dimensions Along the Psychosis Spectrum

A challenge in neuropsychiatry lies in matching patients with effective treatments. Most studies operate under the assumption that categorical diagnoses and/or pre-existing clinical assessments are the ‘gold standard' for describing behavioral - and therefore neural - symptom variation. Attempts to characterize the neural substrates of these pre-defined variables yield limited success, suggesting an inadequate neuro-behavioral mapping. This problem is particularly present along the psychosis spectrum.

Variability of Sleep Apnea Severity and Risk of Atrial Fibrillation: The VARIOSA-AF Study.

This study sought to determine night-to-night variability in the severity of sleep-disordered breathing (SDB) and the dynamic intraindividual relationship to daily risk of incident atrial fibrillation (AF) by using simultaneous long-term day-by-day SDB and AF monitoring. Night-to-night variability in SDB severity may result in a dynamic exposure to SDB related conditions impacting the timing and extent of cardiovascular responses. This study was an observational cohort study. Daily data for AF burden and average respiratory disturbance index (RDI) were extracted from pacemakers capable of monitoring nightly SDB and daily AF burden in 72 patients. Nightly RDI values were grouped into quartiles of severity within each patient. AF burdens of >5 min, >1 h, and >12 h were the outcome variables. A total of 32% of patients had a mean RDI of≥20/h, indicative of overall severe SDB. There was significant night-to-night variation in RDI reflected by an absolute SD of ±6.3 events/h (range 2 to 14 events/h) within any givenpatient. Within each patient, the nights with the highest RDI (in their highest quartile) conferred a 1.7-fold (1.2to2.2; p<0.001), 2.3-fold (1.6 to 3.5; p< 0.001), and 10.2-fold (3.5 to 29.9; p< 0.001) increase risk of having atleast 5 min, 1h, and 12 h, respectively, of AF during the same day compared with the best sleep nights (in their lowestquartiles). There is considerable night-to-night variability in SDB severity which cannot be detected by 1 single overnight sleep study. SDB burden may be a better metric with which to assess the extent of dynamic SDB related cardiovascular responses such as daily AF risk than the categorical diagnosis of SDB. (Night-to-Night Variability in Severity of Sleep Apnea and Daily Dynamic Atrial Fibrillation Risk [VARIOSA-AF]; ACTRN 12618000757213).

Association of variants of prodynorphin promoter 68-bp repeats in caucasians with opioid dependence diagnosis: Effect on age trajectory of heroin use

The dynorphin/kappa opioid receptor (Dyn/KOR) system is involved in reward processing and dysphoria/anhedonia. Exposure to mu-opioid receptor agonists such as heroin increases expression of the prodynorphin gene (PDYN) in the brain. In this study in a Caucasian cohort, we examined the association of the functional PDYN 68-bp repeat polymorphism with opioid use disorders. In this case-control study, 554 subjects with Caucasian ancestry (142 healthy controls, 153 opioid-exposed, but never opioid dependent, NOD, and 259 with an opioid dependence diagnosis, OD) were examined for association of the PDYN 68-bp repeats with the diagnosis of opioid dependence (DSM-IV criteria), with a dimensional measure of heroin exposure (KMSK scale), and age trajectory parameters of heroin use (age of heroin first use, and age of onset of heaviest use). The PDYN 68-bp repeat genotype (classified as: “short-short” [SS], “long-long” [LL], and “short-long” [SL], based on the number of repeats) was not associated with categorical opioid dependence diagnoses. However, the LL genotype was associated with later age of first heroin use than the SS + SL genotype (19 versus 18 years; p < 0.01). This was also confirmed by a significant positive correlation between the number of repeats and the age of first use of heroin, in volunteers with OD (Spearman r = 0.16; p = 0.01). This suggests that the functional PDYN 68-bp repeat genotype is associated with the age of first use of heroin in Caucasians diagnosed with opioid dependence.

A Hierarchical Taxonomy of Psychopathology Can Transform Mental Health Research

For more than a century, research on psychopathology has focused on categorical diagnoses. Although this work has produced major discoveries, growing evidence points to the superiority of a dimensional approach to the science of mental illness. Here we outline one such dimensional system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical patterns of co-occurrence among psychological symptoms. We highlight key ways in which this framework can advance mental-health research, and we provide some heuristics for using HiTOP to test theories of psychopathology. We then review emerging evidence that supports the value of a hierarchical, dimensional model of mental illness across diverse research areas in psychological science. These new data suggest that the HiTOP system has the potential to accelerate and improve research on mental-health problems as well as efforts to more effectively assess, prevent, and treat mental illness.

Measurement of pathological personality traits according to the DSM-5: A Polish adaptation of the PID-5. Part I - theoretical foundations.

The latest edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) proposes a novel hybrid system of personality disorder diagnosis in addition to the one pre-viously laid down in the DSM-IV-TR. This alternative diagnostic system, published within Section III of the DSM-5, was hoped to overcome the inherent limitations of categorical diagnosis by integrating the categorical and dimensional approaches to personality disorders. As such, it constitutes a bridge between psychiatric pathology classifications and findings from psychological research on the structure of normal personality.At the core of the hybrid DSM-5 system lies a new model of pathological personality traits, operationalized using the Personality Inventory for DSM-5 (PID-5). This paper outlines the background and main features of the DSM-5 hybrid system of personality disorder diagnosis with a focus on the dimensional model of pathological traits and definitions thereof. The cur-rent status, application potential and limitations of the DSM-5 diagnostic system and the pathological traits model are also discussed. In another paper, the authors present the PID-5 inventory and report on a study investigating a Polish adaptation of this instrument.

Number of Borderline Personality Disorder Criteria and Depression Predict Poor Functioning and Quality of Life in Outpatient Youth.

This study aimed to investigate which factors contribute to poor functioning and poor quality of life in youth (aged 15-25 years) with borderline personality disorder (BPD), and whether the number of BPD criteria might be an independent predictor of these outcomes. A sample of 499 help-seeking outpatient youth, aged 15-25 years, was assessed. Stepwise multiple regression analyses showed that the number of BPD criteria was the best predictor of poor functioning, followed by number of mental health visits in the past month, female sex, and a current diagnosis of depression. Current depression was the best predictor of Assessment of Quality of Life utility score, followed by the number of BPD criteria. These findings underscore the clinical significance of DSM-IV BPD features (even when subthreshold for a categorical diagnosis) in youth and their effects upon social and occupational functioning and quality of life early in the course of BPD.