Prediction of psychosis: model development and internal validation of a personalized risk calculator

Timeframe for Conversion to Psychosis From Individuals at Clinical High-Risk: A Quantile Regression

BACKGROUND AND HYPOTHESIS

The time taken for an individual who is at the clinical high-risk (CHR) stage to transition to full-blown psychosis may vary from months to years. This temporal aspect, known as the timeframe for conversion to psychosis (TCP), is a crucial but relatively underexplored dimension of psychosis development.

STUDY DESIGN

The sample consisted of 145 individuals with CHR who completed a 5-year follow-up with a confirmed transition to psychosis within this period. Clinical variables along with functional variables such as the Global Assessment of Function (GAF) score at baseline (GAF baseline) and GAF-drop from the highest score in the past year. The TCP was defined as the duration from CHR identification to psychosis conversion. Participants were categorized into 3 groups based on TCP: "short" (≤6 months, ≤33.3%), "median" (7-17 months, 33.3%-66.6%), and "long" (≥18 months, ≥66.6%). The quantile regression analysis was applied.

STUDY RESULTS

The overall sample had a median TCP of 11 months. Significant differences among the three TCP groups were observed, particularly in GAF-drop (χ2 = 8.806, P = .012), disorganized symptoms (χ2 = 7.071, P = .029), and general symptoms (χ2 = 6.586, P = .037). Greater disorganized symptoms (odds ratio [OR] = 0.824, P = .009) and GAF-drop (OR = 0.867, P = .011) were significantly associated with a shorter TCP, whereas greater general symptoms (OR = 1.198, P = .012) predicted a longer TCP. Quantile regression analysis demonstrated a positive association between TCP and GAF baseline above the 0.7 quantile and a negative association between TCP rank and GAF drop below the 0.5 quantile.

CONCLUSIONS

This study underscores the pivotal role of functional characteristics in shaping TCP among individuals with CHR, emphasizing the necessity for a comprehensive consideration of temporal aspects in early prevention efforts.

Magnetic resonance texture analysis reveals stagewise nonlinear alterations of the frontal gray matter in patients with early psychosis

Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix - informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) - were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.

Prediction of bile duct injury after transarterial chemoembolization for hepatocellular carcinoma: Model establishment and verification

Objective

This study aimed to establish and validate a predictive model for bile duct injury in patients with hepatocellular carcinoma (HCC) after drug-eluting bead transarterial chemoembolization (DEB-TACE).

Methods

We retrospectively analyzed 284 patients with HCC treated with DEB-TACE at our hospital between January 2017 and December 2021, of whom 63 patients experienced postoperative bile duct injuries. Univariate and logistic multivariate regression analyses were performed to identify the risk factors for bile duct injury, as well as establish and internally validate the nomogram model. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness of fit test, decision curve analysis (DCA), and clinical impact curve (CIC) were used to assess the predictive power, clinical value, and practicability of the nomogram model.

Results

The incidence of bile duct injuries after DEB-TACE was 22.18% (63/284), with one injury occurring in every 2.86 sessions of DEB-TACE treatment. Univariate and logistic multivariate regression analyses indicated that a history of hepatectomy (odds ratio [OR]=2.285; 95% confidence interval [CI]=1.066-4.898; P<0.05), subjective angiographic chemoembolization endpoint level (OR=1.832; 95% CI=1.258-2.667; P<0.05), alkaline phosphatase (OR=1.005; 95% CI=1.001-1.010; P<0.05), and platelet count (OR=1.005; 95% CI=1.001-1.009; P<0.05) were independent risk factors for bile duct injury after DEB-TACE among patients with HCC. The risk nomogram model based on the above four variables was validated using the bootstrap method, showing consistency between the predicted and experimental values. Furthermore, the model performed well in the Hosmer-Lemeshow goodness-of-fit test (²=3.648; P=0.887). The AUC of this model was 0.749 (95% CI=0.682-0.817), with an overall accuracy of 69.01%, a positive predictive value of 73.02%, a negative predictive value of 67.87%, a sensitivity of 73.0%, and a specificity of 67.90%, suggesting that the nomogram model had good accuracy and discrimination. In addition, DCA and CIC revealed a high clinical value and practicability of the model.

Conclusion

Bile duct injury in patients with HCC treated with DEB-TACE is caused by multiple factors rather than a single factor. The nomogram prediction model used in this study had a good fitting degree and prediction efficacy, with high clinical value and practicability.

Triple-Network Dysconnectivity in Patients With First-Episode Psychosis and Individuals at Clinical High Risk for Psychosis

OBJECTIVE

In the triple-network model, the salience network (SN) plays a crucial role in switching between the default-mode network (DMN) and the central executive network (CEN). Aberrant patterns of triple-network connectivity have been reported in schizophrenia patients, while findings have been less consistent for patients in the early stages of psychotic disorders. Thus, the present study examined the connectivity among the SN, DMN, and CEN in first-episode psychosis (FEP) patients and individuals at clinical high risk (CHR) for psychosis.

METHODS

Thirty-nine patients with FEP, 78 patients with CHR for psychosis, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We compared the SN, DMN, and CEN connectivity patterns of the three groups. The role of the SN in networks with significant connectivity differences was examined by mediation analysis.

RESULTS

FEP patients showed lower SN-DMN and SN-CEN (cluster-level F=5.83, false discovery rate [FDR] corrected-p=0.001) connectivity than HCs. There was lower SN-DMN connectivity (cluster-level F=3.06, FDR corrected-p=0.053) at a trend level in CHR subjects compared to HCs. Between HCs and FEP patients, mediation analysis showed that SN-DMN connectivity was a mediator between group and SN-CEN connectivity. Additionally, SN-CEN connectivity functioned as a mediator between group and SN-DMN connectivity.

CONCLUSION

Aberrant connectivity between the SN and DMN/CEN suggests disrupted network switching in FEP patients, although CHR subjects showed trend-level SN-DMN dysconnectivity. Our findings suggest that dysfunctional triple-network dynamics centered on the SN can appear in patients in the early stages of psychotic disorders.

Evaluating the Social Functioning Scale modified for use in individuals at clinical high-risk for psychosis

BACKGROUND

Social functioning deficits occur prior to the onset of psychosis and predict conversion to psychosis in clinical high-risk (CHR) populations. The Social Functioning Scale (SFS), a self-report measure of social functioning, is widely used in adults with psychosis but has not been tailored to CHR individuals. CHR syndromes overlap with the adolescent/young-adult developmental period, a time with unique social demands and contexts. The current study evaluates a modified version of the SFS in CHR individuals.

METHODS

Two independent samples of CHR participants (n = 84 and n = 45) and non-CHR participants (n = 312 and n = 42) completed the SFS and a psychosis-risk interview. Resulting factors were compared across diagnostic categories (CHR, Major Depressive Disorder, Generalized Anxiety Disorder) and community controls (CC) who were not excluded for any psychopathology except psychosis, depression, and anxiety. CHR participants completed scales of negative symptoms, global social and role functioning, cognition, and finger tapping as measures of convergent and divergent validity.

RESULTS

Exploratory factor analysis identified three SFS factors (RMSEA = 0.05) which demonstrated reliability in a confirmatory analysis in an independent sample: Recreation (α = 0.82), Nightlife (α = 0.85), and Interpersonal (α = 0.69). Factors and their composite score demonstrated increased social deficits in CHR compared to CC and depression groups and showed expected convergent (r's = 0.30-0.54) and divergent (r's = -0.004-0.26) validity with appropriate measures.

CONCLUSIONS

These findings suggest that there are reliable, valid, and developmentally relevant categories of social behavior within the SFS that differentiate between CHR and MDD or CC individuals. Recommendations for future work with CHR populations are included.

Eye movement indices as predictors of conversion to psychosis in individuals at clinical high risk

Eye movement abnormalities have been established as an "endophenotype" of schizophrenia. However, less is known about the possibility of these abnormalities as biomarkers for psychosis conversion among clinical high risk (CHR) populations. In the present study, 108 CHR individuals and 70 healthy controls (HC) underwent clinical assessments and eye-tracking tests, comprising fixation stability and free-viewing tasks. According to three-year follow-up outcomes, CHR participants were further stratified into CHR-converter (CHR-C; n = 21) and CHR-nonconverter (CHR-NC; n = 87) subgroups. Prediction models were constructed using Cox regression and logistic regression. The CHR-C group showed more saccades of the fixation stability test (no distractor) and a reduced saccade amplitude of the free-viewing test than HC. Moreover, the CHR-NC group exhibited excessive saccades and an increased saccade amplitude of the fixation stability test (no distractor; with distractor) compared with HC. Furthermore, two indices could effectively discriminate CHR-C from CHR-NC with an area under the receiver-operating characteristic (ROC) curve of 0.80, including the saccade number of the fixation stability test (no distractor) and the saccade amplitude of the free-viewing test. Combined with negative symptom scores of the Scale of Prodromal Symptoms, the area was 0.81. These findings support that eye movement alterations might emerge before the onset of clinically overt psychosis and could assist in predicting psychosis transition among CHR populations.

Research Trends in Individuals at High Risk for Psychosis: A Bibliometric Analysis

The study of clinical high risk for psychosis (CHR-P) has progressed rapidly over the last decades and has developed into a significant branch of schizophrenia research. Organizing the information about this rapidly growing subject through bibliometric analysis enables us to gain a better understanding of current research trends and future directions to be pursued. Electronic searches from January 1991 to December 2020 yielded 5,601 studies, and included 1,637 original articles. After processing the data, we were able to determine that this field has grown significantly in a short period of time. It has been confirmed that researchers, institutions, and countries are collaborating closely to conduct research; moreover, these networks are becoming increasingly complex over time. Additionally, there was a shift over time in the focus of the research subject from the prodrome, recognition, prevention, diagnosis to cognition, neuroimaging, neurotransmitters, cannabis, and stigma. We should aim for collaborative studies in which various countries participate, thus covering a wider range of races and cultures than would be covered by only a few countries.

Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods

BACKGROUND

Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.

METHODS

Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.

RESULTS

A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.

CONCLUSIONS

We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

Translating RDoC to Real-World Impact in Developmental Psychopathology: A Neurodevelopmental Framework for Application of Mental Health Risk Calculators

The National Institute of Mental Health Research Domain Criteria's (RDoC) has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally-based tools for clinical decision-making has limited RDoC's real-world impact. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally-informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between RDoC's multiple units of analysis and developmental science.

Neurocognitive Functioning in Individuals at Clinical High Risk for Psychosis: A Systematic Review and Meta-analysis

IMPORTANCE

Neurocognitive functioning is a potential biomarker to advance detection, prognosis, and preventive care for individuals at clinical high risk for psychosis (CHR-P). The current consistency and magnitude of neurocognitive functioning in individuals at CHR-P are undetermined.

OBJECTIVE

To provide an updated synthesis of evidence on the consistency and magnitude of neurocognitive functioning in individuals at CHR-P.

DATA SOURCES

Web of Science database, Cochrane Central Register of Reviews, and Ovid/PsycINFO and trial registries up to July 1, 2020.

STUDY SELECTION

Multistep literature search compliant with Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology performed by independent researchers to identify original studies reporting on neurocognitive functioning in individuals at CHR-P.

DATA EXTRACTION AND SYNTHESIS

Independent researchers extracted the data, clustering the neurocognitive tasks according to 7 Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) domains and 8 CHR-P domains. Random-effect model meta-analyses, assessment of publication biases and study quality, and meta-regressions were conducted.

MAIN OUTCOMES AND MEASURES

The primary effect size measure was Hedges g of neurocognitive functioning in individuals at CHR-P (1) compared with healthy control (HC) individuals or (2) compared with individuals with first-episode psychosis (FEP) or (3) stratified for the longitudinal transition to psychosis.

RESULTS

A total of 78 independent studies were included, consisting of 5162 individuals at CHR-P (mean [SD; range] age, 20.2 [3.3; 12.0-29.0] years; 2529 [49.0%] were female), 2865 HC individuals (mean [SD; range] age, 21.1 [3.6; 12.6-29.2] years; 1490 [52.0%] were female), and 486 individuals with FEP (mean [SD; range] age, 23.0 [2.0; 19.1-26.4] years; 267 [55.9%] were female). Compared with HC individuals, individuals at CHR-P showed medium to large deficits on the Stroop color word reading task (g = -1.17; 95% CI, -1.86 to -0.48), Hopkins Verbal Learning Test-Revised (g = -0.86; 95% CI, -1.43 to -0.28), digit symbol coding test (g = -0.74; 95% CI, -1.19 to -0.29), Brief Assessment of Cognition Scale Symbol Coding (g = -0.67; 95% CI, -0.95 to -0.39), University of Pennsylvania Smell Identification Test (g = -0.55; 95% CI, -0.97 to -0.12), Hinting Task (g = -0.53; 95% CI, -0.77 to -0.28), Rey Auditory Verbal Learning Test (g = -0.50; 95% CI, -0.78 to -0.21), California Verbal Learning Test (CVLT) (g = -0.50; 95% CI, -0.64 to -0.36), and National Adult Reading Test (g = -0.52; 95% CI, -1.01 to -0.03). Individuals at CHR-P were less impaired than individuals with FEP. Longitudinal transition to psychosis from a CHR-P state was associated with medium to large deficits in the CVLT task (g = -0.58; 95% CI, -1.12 to -0.05). Meta-regressions found significant effects for age and education on processing speed.

CONCLUSIONS AND RELEVANCE

Findings from this meta-analysis support neurocognitive dysfunction as a potential detection and prognostic biomarker in individuals at CHR-P. These findings may advance clinical research and inform preventive approaches.

The Moderating Effect of Attentional Control on the Relationship Between COVID Stress and Generalized Anxiety Symptoms

The recent global pandemic (i.e., COVID-19) has had a serious impact on psychological health, as the stress associated with the pandemic increases the likelihood of developing clinically significant anxiety. Evidence suggests that attentional control may protect those individuals with outcome-specific vulnerabilities from developing maladaptive psychological outcomes. In the present study, attentional control was examined as a moderator of the relation between COVID-19 stress and generalized anxiety symptoms in a community sample (N = 359 adults). As predicted, the relationship between COVID-19 stress and anxiety was moderated by attentional control. Specifically, as attentional control decreased, the strength of the association between COVID-19 stress and anxiety increased. The results suggest that, among those with higher levels of COVID-19 stress, attentional control may act as a protective factor against developing anxiety. It may be beneficial as a matter of standard public health guidance to recommend that the general public engages in activities that are known to improve attentional control and alleviate emotional distress (e.g., mindfulness-based techniques) at the outset of a pandemic or other global catastrophe to reduce the likelihood that prolonged event-related stress will lead to impairing anxiety.

[Cross-sectoral therapeutic concepts and innovative technologies: new opportunities for the treatment of patients with mental disorders]

Mental disorders are widespread and a major public health problem. The risk of developing a mental disorder at some point in life is around 40%. Therefore, mental disorders are among the most common diseases. Despite the introduction of newer psychotropic drugs, disorder-specific psychotherapy and stimulation techniques, many of those affected still show insufficient symptom remission and a chronic course of the disorder. Conceptual and technological progress in recent years has enabled a new, more flexible and personalized form of mental health care. Both the traditional therapeutic concepts and newer decentralized, modularly structured, track units, together with innovative digital technologies, will offer individualized therapeutic options in order to alleviate symptoms and improve quality of life of patients with mental illnesses. The primary goal of closely combining inpatient care concepts with innovative technologies is to provide comprehensive therapy and aftercare concepts for all individual needs of patients with mental disorders. Last but not least, this also ensures that specialist psychiatric treatment is available regardless of location. In twenty-first century psychiatry, modern care structures must be effectively linked to the current dynamics of digital transformation. This narrative review is dedicated to the theoretical and practical aspects of a cross-sectoral treatment system combined with innovative digital technologies in the psychiatric-psychotherapeutic field. The authors aim to illuminate these therapy modalities using the example of the Central Institute of Mental Health in Mannheim.