Katarzyna Popiolek, Tor Arnison, Susanne Bejerot, Katja Fall, Mikael Landén, Axel Nordenskjöld
Objective
The majority of patients hospitalized for treatment of a manic episode are readmitted within 2 years despite maintenance treatment. Electroconvulsive therapy (ECT) has been associated with lower rehospitalization rates in some psychiatric conditions, but its association with readmission after a manic episode has not been investigated. Therefore, the aim of this study was to determine whether the time to readmission in patients with mania treated with ECT was longer than in patients not treated with ECT and whether there were subgroups of patients that benefited more.
Methods
This was a nationwide register-based, observational study. All patients diagnosed with bipolar disorder, manic episode, admitted to any hospital in Sweden between 2012 and 2021 were included. Patients contributed data to the study for every admission. All admissions were followed up until psychiatric readmission, death, or the end of the study (December 31, 2021). Association between ECT and time to readmission was analyzed. A paired samples model was performed for 377 patients with at least two admissions for mania, treated with ECT at one admission and without ECT at the other admission. Times to readmission were analyzed.
Results
A total of 12,337 admissions were included; mean (SD) age 47.7 (17.2), 5443 (44.1%) men. Readmission rate within 1 year was 54.6%. ECT was administered in 902 (7.3%) admissions. Within 30 days after admission, 182 out of 894 (20.4%) patients treated with ECT versus 2105 out of 11,305 (18.6%) patients treated without ECT were readmitted. There was no association between ECT and time to readmission (aHR 1.00, 95% CI 0.86–1.16, p = 0.992) in the model with all admissions. The paired samples model included 754 admissions (377 patients), mean (SD) age during admission without ECT was 45.6 (16.5), and with ECT 46.6 (16.4), 147 (39.0%) were men. In that model, readmission rate within 30 days for treatment with ECT was 19.0%, and for treatments without ECT, 24.1% (aHR 0.75, 95% CI 0.55–1.02, p = 0.067).
Conclusion
Readmission rates after inpatient treatment of mania were high. ECT was not significantly associated with longer time to readmission, but there was a trend toward a protective effect of ECT when admissions with and without ECT were compared within the same patients.
{"title":"Association between electroconvulsive therapy and time to readmission after a manic episode","authors":"Katarzyna Popiolek, Tor Arnison, Susanne Bejerot, Katja Fall, Mikael Landén, Axel Nordenskjöld","doi":"10.1111/acps.13689","DOIUrl":"10.1111/acps.13689","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The majority of patients hospitalized for treatment of a manic episode are readmitted within 2 years despite maintenance treatment. Electroconvulsive therapy (ECT) has been associated with lower rehospitalization rates in some psychiatric conditions, but its association with readmission after a manic episode has not been investigated. Therefore, the aim of this study was to determine whether the time to readmission in patients with mania treated with ECT was longer than in patients not treated with ECT and whether there were subgroups of patients that benefited more.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This was a nationwide register-based, observational study. All patients diagnosed with bipolar disorder, manic episode, admitted to any hospital in Sweden between 2012 and 2021 were included. Patients contributed data to the study for every admission. All admissions were followed up until psychiatric readmission, death, or the end of the study (December 31, 2021). Association between ECT and time to readmission was analyzed. A paired samples model was performed for 377 patients with at least two admissions for mania, treated with ECT at one admission and without ECT at the other admission. Times to readmission were analyzed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 12,337 admissions were included; mean (SD) age 47.7 (17.2), 5443 (44.1%) men. Readmission rate within 1 year was 54.6%. ECT was administered in 902 (7.3%) admissions. Within 30 days after admission, 182 out of 894 (20.4%) patients treated with ECT versus 2105 out of 11,305 (18.6%) patients treated without ECT were readmitted. There was no association between ECT and time to readmission (aHR 1.00, 95% CI 0.86–1.16, <i>p</i> = 0.992) in the model with all admissions. The paired samples model included 754 admissions (377 patients), mean (SD) age during admission without ECT was 45.6 (16.5), and with ECT 46.6 (16.4), 147 (39.0%) were men. In that model, readmission rate within 30 days for treatment with ECT was 19.0%, and for treatments without ECT, 24.1% (aHR 0.75, 95% CI 0.55–1.02, <i>p</i> = 0.067).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Readmission rates after inpatient treatment of mania were high. ECT was not significantly associated with longer time to readmission, but there was a trend toward a protective effect of ECT when admissions with and without ECT were compared within the same patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"150 1","pages":"22-34"},"PeriodicalIF":6.7,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tyler C. Dalal, Liangbing Liang, Angelica M. Silva, Michael Mackinley, Alban Voppel, Lena Palaniyappan
Background and Hypothesis
Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. “Oddities” in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis.
Design
Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups.
Results
We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a ‘typical linguistic profile (TLP)’. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP – early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP – stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness).
Conclusion
The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of ‘formal thought disorder’.
{"title":"Speech based natural language profile before, during and after the onset of psychosis: A cluster analysis","authors":"Tyler C. Dalal, Liangbing Liang, Angelica M. Silva, Michael Mackinley, Alban Voppel, Lena Palaniyappan","doi":"10.1111/acps.13685","DOIUrl":"10.1111/acps.13685","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Hypothesis</h3>\u0000 \u0000 <p>Speech markers are digitally acquired, computationally derived, quantifiable set of measures that reflect the state of neurocognitive processes relevant for social functioning. “Oddities” in language and communication have historically been seen as a core feature of schizophrenia. The application of natural language processing (NLP) to speech samples can elucidate even the most subtle deviations in language. We aim to determine if NLP based profiles that are distinctive of schizophrenia can be observed across the various clinical phases of psychosis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>Our sample consisted of 147 participants and included 39 healthy controls (HC), 72 with first-episode psychosis (FEP), 18 in a clinical high-risk state (CHR), 18 with schizophrenia (SZ). A structured task elicited 3 minutes of speech, which was then transformed into quantitative measures on 12 linguistic variables (lexical, syntactic, and semantic). Cluster analysis that leveraged healthy variations was then applied to determine language-based subgroups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We observed a three-cluster solution. The largest cluster included most HC and the majority of patients, indicating a ‘typical linguistic profile (TLP)’. One of the atypical clusters had notably high semantic similarity in word choices with less perceptual words, lower cohesion and analytical structure; this cluster was almost entirely composed of patients in early stages of psychosis (EPP – early phase profile). The second atypical cluster had more patients with established schizophrenia (SPP – stable phase profile), with more perceptual but less cognitive/emotional word classes, simpler syntactic structure, and a lack of sufficient reference to prior information (reduced givenness).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The patterns of speech deviations in early and established stages of schizophrenia are distinguishable from each other and detectable when lexical, semantic and syntactic aspects are assessed in the pursuit of ‘formal thought disorder’.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"151 3","pages":"332-347"},"PeriodicalIF":5.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Martin Bernstorff, Lasse Hansen, Kenneth Enevoldsen, Jakob Damgaard, Frida Hæstrup, Erik Perfalk, Andreas Aalkjær Danielsen, Søren Dinesen Østergaard
Background
Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.
Methods
The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts.
Results
The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%).
Conclusion
A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.
{"title":"Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness","authors":"Martin Bernstorff, Lasse Hansen, Kenneth Enevoldsen, Jakob Damgaard, Frida Hæstrup, Erik Perfalk, Andreas Aalkjær Danielsen, Søren Dinesen Østergaard","doi":"10.1111/acps.13687","DOIUrl":"10.1111/acps.13687","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Type 2 diabetes (T2D) is approximately twice as common among individuals with mental illness compared with the background population, but may be prevented by early intervention on lifestyle, diet, or pharmacologically. Such prevention relies on identification of those at elevated risk (prediction). The aim of this study was to develop and validate a machine learning model for prediction of T2D among patients with mental illness.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study was based on routine clinical data from electronic health records from the psychiatric services of the Central Denmark Region. A total of 74,880 patients with 1.59 million psychiatric service contacts were included in the analyses. We created 1343 potential predictors from 51 source variables, covering patient-level information on demographics, diagnoses, pharmacological treatment, and laboratory results. T2D was operationalised as HbA1c ≥48 mmol/mol, fasting plasma glucose ≥7.0 mmol/mol, oral glucose tolerance test ≥11.1 mmol/mol or random plasma glucose ≥11.1 mmol/mol. Two machine learning models (XGBoost and regularised logistic regression) were trained to predict T2D based on 85% of the included contacts. The predictive performance of the best performing model was tested on the remaining 15% of the contacts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The XGBoost model detected patients at high risk 2.7 years before T2D, achieving an area under the receiver operating characteristic curve of 0.84. Of the 996 patients developing T2D in the test set, the model issued at least one positive prediction for 305 (31%).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A machine learning model can accurately predict development of T2D among patients with mental illness based on routine clinical data from electronic health records. A decision support system based on such a model may inform measures to prevent development of T2D in this high-risk population.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"151 3","pages":"245-258"},"PeriodicalIF":5.3,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Olivier Corbeil, Élizabeth Anderson, Laurent Béchard, Charles Desmeules, Maxime Huot-Lavoie, Lauryann Bachand, Sébastien Brodeur, Pierre-Hugues Carmichael, Christian Jacques, Marco Solmi, Isabelle Giroux, Michel Dorval, Marie-France Demers, Marc-André Roy
Introduction
Problem gambling (PBG) is more common in people with mental health disorders, including substance use, bipolar, and personality disorders, than in the general population. Although individuals with psychotic disorders might be expected to be more vulnerable to PBG, fewer studies have focused on this comorbidity. The aim of this review was to estimate the prevalence of PBG in people with psychotic disorders.
Methods
Medline (Ovid), EMBASE, PsycINFO (Ovid), CINAHL, CENTRAL, Web of science, and ProQuest were searched on November 1, 2023, without language restrictions. Observational and experimental studies including individuals with psychotic disorders and reporting the prevalence of PBG were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal for systematic reviews of prevalence data. The pooled prevalence of PBG was calculated using a fixed effects generalized linear mixed model and presented through forest plots.
Results
Of 1271 records screened, 12 studies (n = 3443) were included. The overall prevalence of PBG was 8.7% (95% CI = 7.8%–9.7%, I2 = 69%). A lower prevalence was found in studies with a low risk of bias (5.6%; 95% CI = 4.4%–7.0%) compared with studies with a moderate risk of bias (10.4%; 95% CI = 9.2%–11.7%). Different methods used to assess PBG also contributed to the heterogeneity found.
Conclusion
This meta-analysis found substantial heterogeneity, partly due to the risk of bias of the included studies and a lack of uniformity in PBG assessment. Although more research is needed to identify those at increased risk for PBG, its relatively high prevalence warrants routine screening for gambling in clinical practice.
{"title":"Problem gambling in psychotic disorders: A systematic review and meta-analysis of prevalence","authors":"Olivier Corbeil, Élizabeth Anderson, Laurent Béchard, Charles Desmeules, Maxime Huot-Lavoie, Lauryann Bachand, Sébastien Brodeur, Pierre-Hugues Carmichael, Christian Jacques, Marco Solmi, Isabelle Giroux, Michel Dorval, Marie-France Demers, Marc-André Roy","doi":"10.1111/acps.13686","DOIUrl":"10.1111/acps.13686","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Problem gambling (PBG) is more common in people with mental health disorders, including substance use, bipolar, and personality disorders, than in the general population. Although individuals with psychotic disorders might be expected to be more vulnerable to PBG, fewer studies have focused on this comorbidity. The aim of this review was to estimate the prevalence of PBG in people with psychotic disorders.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Medline (Ovid), EMBASE, PsycINFO (Ovid), CINAHL, CENTRAL, Web of science, and ProQuest were searched on November 1, 2023, without language restrictions. Observational and experimental studies including individuals with psychotic disorders and reporting the prevalence of PBG were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal for systematic reviews of prevalence data. The pooled prevalence of PBG was calculated using a fixed effects generalized linear mixed model and presented through forest plots.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 1271 records screened, 12 studies (<i>n</i> = 3443) were included. The overall prevalence of PBG was 8.7% (95% CI = 7.8%–9.7%, <i>I</i><sup>2</sup> = 69%). A lower prevalence was found in studies with a low risk of bias (5.6%; 95% CI = 4.4%–7.0%) compared with studies with a moderate risk of bias (10.4%; 95% CI = 9.2%–11.7%). Different methods used to assess PBG also contributed to the heterogeneity found.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This meta-analysis found substantial heterogeneity, partly due to the risk of bias of the included studies and a lack of uniformity in PBG assessment. Although more research is needed to identify those at increased risk for PBG, its relatively high prevalence warrants routine screening for gambling in clinical practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 6","pages":"445-457"},"PeriodicalIF":6.7,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13686","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia Eder, Catherine Glocker, Barbara Barton, Elif Sarisik, David Popovic, Jana Lämmermann, Alexandra Knaf, Anja Beqiri-Zagler, Katharina Engl, Leonie Rihs, Lisa Pfeiffer, Andrea Schmitt, Peter Falkai, Maria S. Simon, Richard Musil
Background
Weight gain is a common side effect in psychopharmacology; however, targeted therapeutic interventions and prevention strategies are currently absent in day-to-day clinical practice. To promote the development of such strategies, the identification of factors indicative of patients at risk is essential.
Methods
In this study, we developed a transdiagnostic model using and comparing decision tree classifiers, logistic regression, XGboost, and a support vector machine to predict weight gain of ≥5% of body weight during the first 4 weeks of treatment with psychotropic drugs associated with weight gain in 103 psychiatric inpatients. We included established variables from the literature as well as an extended set with additional clinical variables and questionnaires.
Results
Baseline BMI, premorbid BMI, and age are known risk factors and were confirmed by our models. Additionally, waist circumference has emerged as a new and significant risk factor. Eating behavior next to blood glucose were found as additional potential predictor that may underlie therapeutic interventions and could be used for preventive strategies in a cohort at risk for psychotropics induced weight gain (PIWG).
Conclusion
Our models validate existing findings and further uncover previously unknown modifiable factors, such as eating behavior and blood glucose, which can be used as targets for preventive strategies. These findings underscore the imperative for continued research in this domain to establish effective preventive measures for individuals undergoing psychotropic drug treatments.
{"title":"Who is at risk for weight gain after weight-gain associated treatment with antipsychotics, antidepressants, and mood stabilizers: A machine learning approach","authors":"Julia Eder, Catherine Glocker, Barbara Barton, Elif Sarisik, David Popovic, Jana Lämmermann, Alexandra Knaf, Anja Beqiri-Zagler, Katharina Engl, Leonie Rihs, Lisa Pfeiffer, Andrea Schmitt, Peter Falkai, Maria S. Simon, Richard Musil","doi":"10.1111/acps.13684","DOIUrl":"10.1111/acps.13684","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Weight gain is a common side effect in psychopharmacology; however, targeted therapeutic interventions and prevention strategies are currently absent in day-to-day clinical practice. To promote the development of such strategies, the identification of factors indicative of patients at risk is essential.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In this study, we developed a transdiagnostic model using and comparing decision tree classifiers, logistic regression, XGboost, and a support vector machine to predict weight gain of ≥5% of body weight during the first 4 weeks of treatment with psychotropic drugs associated with weight gain in 103 psychiatric inpatients. We included established variables from the literature as well as an extended set with additional clinical variables and questionnaires.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Baseline BMI, premorbid BMI, and age are known risk factors and were confirmed by our models. Additionally, waist circumference has emerged as a new and significant risk factor. Eating behavior next to blood glucose were found as additional potential predictor that may underlie therapeutic interventions and could be used for preventive strategies in a cohort at risk for psychotropics induced weight gain (PIWG).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our models validate existing findings and further uncover previously unknown modifiable factors, such as eating behavior and blood glucose, which can be used as targets for preventive strategies. These findings underscore the imperative for continued research in this domain to establish effective preventive measures for individuals undergoing psychotropic drug treatments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"151 3","pages":"231-244"},"PeriodicalIF":5.3,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alexis C. Edwards, Linda Abrahamsson, Casey Crump, Jan Sundquist, Kristina Sundquist, Kenneth S. Kendler
Introduction
Alcohol use disorder (AUD) is among the strongest correlates of suicide death, but it is unclear whether AUD status is differentially associated with risk of suicide by particular methods.
Methods
The authors used competing risks models to evaluate the association between AUD status and risk of suicide by poisoning, suffocation, drowning, firearm, instruments, jumping, or other means in a large Swedish cohort born 1932–1995 (total N = 6,581,827; 48.8% female). Data were derived from Swedish national registers, including the Cause of Death Register and a range of medical registers.
Results
After adjusting for sociodemographic factors and familial liability to suicidal behavior, AUD was positively associated with risk of suicide for each method evaluated (cumulative incidence differences: 0.006–1.040 for females, 0.046–0.680 for males), except the association with firearm suicide in females. AUD was most strongly associated with risk of suicide by poisoning. Sex differences in the effects of AUD and family liability were observed for some, but not all, methods. Furthermore, high familial liability for suicidal behavior exacerbated AUD's impact on risk for suicide by poisoning (both sexes) and suffocation and jumping (males only), while the inverse interaction was observed for firearm suicide (males only).
Conclusions
AUD increases risk of suicide by all methods examined and is particularly potent with respect to risk of suicide by poisoning. Differences in risk related to sex and familial liability to suicidal behavior underscore AUD's nuanced role in suicide risk. Future research should investigate targeted means restriction effectiveness among persons with AUD.
{"title":"Alcohol use disorder and risk of specific methods of suicide death in a national cohort","authors":"Alexis C. Edwards, Linda Abrahamsson, Casey Crump, Jan Sundquist, Kristina Sundquist, Kenneth S. Kendler","doi":"10.1111/acps.13683","DOIUrl":"10.1111/acps.13683","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Alcohol use disorder (AUD) is among the strongest correlates of suicide death, but it is unclear whether AUD status is differentially associated with risk of suicide by particular methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The authors used competing risks models to evaluate the association between AUD status and risk of suicide by poisoning, suffocation, drowning, firearm, instruments, jumping, or other means in a large Swedish cohort born 1932–1995 (total <i>N</i> = 6,581,827; 48.8% female). Data were derived from Swedish national registers, including the Cause of Death Register and a range of medical registers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After adjusting for sociodemographic factors and familial liability to suicidal behavior, AUD was positively associated with risk of suicide for each method evaluated (cumulative incidence differences: 0.006–1.040 for females, 0.046–0.680 for males), except the association with firearm suicide in females. AUD was most strongly associated with risk of suicide by poisoning. Sex differences in the effects of AUD and family liability were observed for some, but not all, methods. Furthermore, high familial liability for suicidal behavior exacerbated AUD's impact on risk for suicide by poisoning (both sexes) and suffocation and jumping (males only), while the inverse interaction was observed for firearm suicide (males only).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>AUD increases risk of suicide by all methods examined and is particularly potent with respect to risk of suicide by poisoning. Differences in risk related to sex and familial liability to suicidal behavior underscore AUD's nuanced role in suicide risk. Future research should investigate targeted means restriction effectiveness among persons with AUD.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 6","pages":"479-490"},"PeriodicalIF":6.7,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140331325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christoffer Beck, Carsten Bøcker Pedersen, Oleguer Plana-Ripoll, Søren Dalsgaard, Jean-Christophe Philippe Debost, Thomas Munk Laursen, Katherine Louise Musliner, Preben Bo Mortensen, Marianne Giørtz Pedersen, Liselotte Vogdrup Petersen, Zeynep Yilmaz, John McGrath, Esben Agerbo
<div> <section> <h3> Background</h3> <p>The age of onset (AOO), incidence and cumulative incidence of mental disorders are critical epidemiological measures, providing essential insights into the development and course of these disorders across the lifespan. This study aims to provide up-to-date estimates of the AOO, age-specific incidence, and cumulative incidence for a comprehensive range of mental disorders using data from Danish registers.</p> </section> <section> <h3> Methods</h3> <p>We conducted a follow-up study encompassing all Danish residents from January 1, 2004, to December 31, 2021, totaling 91,613,465 person-years. Data were sourced from the Danish Psychiatric Central Research Register, identifying individuals treated for various mental disorders in psychiatric hospitals, outpatient departments, and accident/emergency departments, that is, treated in secondary care settings. We investigated specific categories of mental disorders, including substance abuse disorders, schizophrenia, mood disorders, anxiety, eating disorders, borderline personality disorders, intellectual disabilities, pervasive developmental disorders, and behavioral and emotional disorders. Age-sex-specific incidence rates were estimated using Poisson generalized linear models, and cumulative incidence was calculated using Aalen–Johansen's competing risks model. The study provides estimates of AOO, incidence, and cumulative incidence for various mental disorders, including their age and sex distributions.</p> </section> <section> <h3> Results</h3> <p>The cumulative incidence by age 80 years for any mental disorder was 30.72% (95% confidence interval: 30.62%–30.83%) for males and 34.46% (34.35%–34.57%) for females. The most common types of mental disorders were anxiety-related disorders 16.27% (16.19%–16.36%) for males and 23.39% (23.29%–23.50%) for females, and followed by mood disorder 10.34% (10.27%–10.41%) for males and 16.67% (16.58%–16.77%) for females. For those who develop mental disorder, half will have developed their disorder by approximately age 22 years (median and interquartile range: males 21.37 (11.85–36.00); females 22.55 (16.31–36.08)).</p> </section> <section> <h3> Conclusions</h3> <p>Approximately one in three individuals will seek treatment for at least one mental disorder in a secondary care setting by age 80. Given that half of these individuals develop mental disorders before age 22, it is crucial to tailor service planning to meet the specific needs of young individuals. Web-based interactive data-visualization tools are provided for clinical u
{"title":"A comprehensive analysis of age of onset and cumulative incidence of mental disorders: A Danish register study","authors":"Christoffer Beck, Carsten Bøcker Pedersen, Oleguer Plana-Ripoll, Søren Dalsgaard, Jean-Christophe Philippe Debost, Thomas Munk Laursen, Katherine Louise Musliner, Preben Bo Mortensen, Marianne Giørtz Pedersen, Liselotte Vogdrup Petersen, Zeynep Yilmaz, John McGrath, Esben Agerbo","doi":"10.1111/acps.13682","DOIUrl":"10.1111/acps.13682","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The age of onset (AOO), incidence and cumulative incidence of mental disorders are critical epidemiological measures, providing essential insights into the development and course of these disorders across the lifespan. This study aims to provide up-to-date estimates of the AOO, age-specific incidence, and cumulative incidence for a comprehensive range of mental disorders using data from Danish registers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a follow-up study encompassing all Danish residents from January 1, 2004, to December 31, 2021, totaling 91,613,465 person-years. Data were sourced from the Danish Psychiatric Central Research Register, identifying individuals treated for various mental disorders in psychiatric hospitals, outpatient departments, and accident/emergency departments, that is, treated in secondary care settings. We investigated specific categories of mental disorders, including substance abuse disorders, schizophrenia, mood disorders, anxiety, eating disorders, borderline personality disorders, intellectual disabilities, pervasive developmental disorders, and behavioral and emotional disorders. Age-sex-specific incidence rates were estimated using Poisson generalized linear models, and cumulative incidence was calculated using Aalen–Johansen's competing risks model. The study provides estimates of AOO, incidence, and cumulative incidence for various mental disorders, including their age and sex distributions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The cumulative incidence by age 80 years for any mental disorder was 30.72% (95% confidence interval: 30.62%–30.83%) for males and 34.46% (34.35%–34.57%) for females. The most common types of mental disorders were anxiety-related disorders 16.27% (16.19%–16.36%) for males and 23.39% (23.29%–23.50%) for females, and followed by mood disorder 10.34% (10.27%–10.41%) for males and 16.67% (16.58%–16.77%) for females. For those who develop mental disorder, half will have developed their disorder by approximately age 22 years (median and interquartile range: males 21.37 (11.85–36.00); females 22.55 (16.31–36.08)).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Approximately one in three individuals will seek treatment for at least one mental disorder in a secondary care setting by age 80. Given that half of these individuals develop mental disorders before age 22, it is crucial to tailor service planning to meet the specific needs of young individuals. Web-based interactive data-visualization tools are provided for clinical u","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 6","pages":"467-478"},"PeriodicalIF":6.7,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140205897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>The paper in this issue by Fava and colleagues<span><sup>1</sup></span> highlights features of the Diagnostic Criteria for Psychosomatic Research (DCPR) and stresses how the DCPR was inspired by Engel's biopsychosocial model.<span><sup>2</sup></span></p><p>Engel wrote that his ‘proposed biopsychosocial model provides a blueprint for research, a framework for teaching, and a design for action in the real world of healthcare’.<span><sup>2</sup></span> Since then, the biopsychosocial model has been widely adopted, particularly by clinicians and clinical teachers. However, it has also attracted criticism. Some have argued that it says nothing about the subjective experience of the patient.<span><sup>3</sup></span> Others have gone further in their criticism, claiming that individualising treatment to each patient, as the model implies, gives rise to eclectic freedom which ‘borders on anarchy’ and merely replaces the dogmas which the model was intended to protect against (notably the reductionism of the biomedical model) with other dogmas.<span><sup>4</sup></span> Responding to criticisms that Engel's model lacks a sound theoretical basis, Bolton<span><sup>5</sup></span> noted that the original model needs to be understood as of its time and that despite its limitations, the model anticipated the crucial role in health and illness of ‘concepts such as regulation and dysregulation, information and communication and function and dysfunction’. It has been argued that the main problem with the model as Engel proposed it is that it is too general. One proposed solution to this is to elaborate specific, evidence-based, models for different diagnoses or conditions.<span><sup>6</sup></span> The DCPR represents a different but effective solution to the same problem. Instead of elaborating the details of the biopsychosocial model separately for different conditions (a monumental undertaking), the DCPR aims to describe particular transdiagnostic states (termed ‘syndromes’) which can occur as features of the experience of illness.</p><p>Fava et al.<span><sup>1</sup></span> illustrate the DCPR by characterising some of its syndromes. These are all patterns of responses to life situations involving illness, reflecting dysregulation and/or dysfunction. They are termed syndromes to distinguish them from disorders or diseases which form the basis of standard diagnostic classifications.<span><sup>7</sup></span> The syndromes were intended to be descriptive and without any pathogenic implications, although with progress in research and understanding, this assumption might now be challenged. The original syndromes were not intended to be exhaustive and indeed the original DCPR has been revised to include two additional syndromes.<span><sup>8</sup></span> An important feature of the syndromes is that they each include (or overlay) biological, psychological and social components. Allostatic overload is a prime example, manifestly showing biological, psychological and so
{"title":"Practical application of the biopsychosocial model to medical care—Are we nearly there yet?","authors":"Tom Sensky","doi":"10.1111/acps.13681","DOIUrl":"10.1111/acps.13681","url":null,"abstract":"<p>The paper in this issue by Fava and colleagues<span><sup>1</sup></span> highlights features of the Diagnostic Criteria for Psychosomatic Research (DCPR) and stresses how the DCPR was inspired by Engel's biopsychosocial model.<span><sup>2</sup></span></p><p>Engel wrote that his ‘proposed biopsychosocial model provides a blueprint for research, a framework for teaching, and a design for action in the real world of healthcare’.<span><sup>2</sup></span> Since then, the biopsychosocial model has been widely adopted, particularly by clinicians and clinical teachers. However, it has also attracted criticism. Some have argued that it says nothing about the subjective experience of the patient.<span><sup>3</sup></span> Others have gone further in their criticism, claiming that individualising treatment to each patient, as the model implies, gives rise to eclectic freedom which ‘borders on anarchy’ and merely replaces the dogmas which the model was intended to protect against (notably the reductionism of the biomedical model) with other dogmas.<span><sup>4</sup></span> Responding to criticisms that Engel's model lacks a sound theoretical basis, Bolton<span><sup>5</sup></span> noted that the original model needs to be understood as of its time and that despite its limitations, the model anticipated the crucial role in health and illness of ‘concepts such as regulation and dysregulation, information and communication and function and dysfunction’. It has been argued that the main problem with the model as Engel proposed it is that it is too general. One proposed solution to this is to elaborate specific, evidence-based, models for different diagnoses or conditions.<span><sup>6</sup></span> The DCPR represents a different but effective solution to the same problem. Instead of elaborating the details of the biopsychosocial model separately for different conditions (a monumental undertaking), the DCPR aims to describe particular transdiagnostic states (termed ‘syndromes’) which can occur as features of the experience of illness.</p><p>Fava et al.<span><sup>1</sup></span> illustrate the DCPR by characterising some of its syndromes. These are all patterns of responses to life situations involving illness, reflecting dysregulation and/or dysfunction. They are termed syndromes to distinguish them from disorders or diseases which form the basis of standard diagnostic classifications.<span><sup>7</sup></span> The syndromes were intended to be descriptive and without any pathogenic implications, although with progress in research and understanding, this assumption might now be challenged. The original syndromes were not intended to be exhaustive and indeed the original DCPR has been revised to include two additional syndromes.<span><sup>8</sup></span> An important feature of the syndromes is that they each include (or overlay) biological, psychological and social components. Allostatic overload is a prime example, manifestly showing biological, psychological and so","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 5","pages":"365-367"},"PeriodicalIF":6.7,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140173651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enya Redican, Philip Hyland, Mark Shevlin, Dmytro Martsenkovskyi, Thanos Karatzias, Menachem Ben-Ezra
Background
Although high rates of bereavement are evident in war-affected populations, no study has investigated the prevalence and correlates of probable ICD-11 prolonged grief disorder (PGD) under these circumstances.
Methods
Participants were 2050 adults who participated in a nationwide survey exploring the effects of the Ukraine-Russia war on the daily lives and mental health of Ukrainian people.
Results
Of the total sample, 87.7% (n = 1797) of people indicated a lifetime bereavement. In the full sample, 11.4% met the diagnostic requirements for probable ICD-11 PGD, and amongst those with a lifetime bereavement, the conditional rate of probable ICD-11 PGD was 13.0%. Significant risk factors of ICD-11 PGD included the recent loss of a loved one (6 months to a year ago), being most affected by a partner or spouse's death, loved one dying in the war, no recent contact with the deceased prior to their death, and meeting depression and anxiety diagnostic requirements.
Conclusion
The study reveals that a significant percentage of Ukrainian bereaved individuals have probable ICD-11 PGD, and identifying risk factors, particularly war-related losses, will aid in the development of intervention and prevention programs for bereaved adults.
{"title":"Prevalence and correlates of ICD-11 prolonged grief disorder among adults living in Ukraine during the war with Russia","authors":"Enya Redican, Philip Hyland, Mark Shevlin, Dmytro Martsenkovskyi, Thanos Karatzias, Menachem Ben-Ezra","doi":"10.1111/acps.13678","DOIUrl":"10.1111/acps.13678","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Although high rates of bereavement are evident in war-affected populations, no study has investigated the prevalence and correlates of probable ICD-11 prolonged grief disorder (PGD) under these circumstances.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Participants were 2050 adults who participated in a nationwide survey exploring the effects of the Ukraine-Russia war on the daily lives and mental health of Ukrainian people.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of the total sample, 87.7% (<i>n</i> = 1797) of people indicated a lifetime bereavement. In the full sample, 11.4% met the diagnostic requirements for probable ICD-11 PGD, and amongst those with a lifetime bereavement, the conditional rate of probable ICD-11 PGD was 13.0%. Significant risk factors of ICD-11 PGD included the recent loss of a loved one (6 months to a year ago), being most affected by a partner or spouse's death, loved one dying in the war, no recent contact with the deceased prior to their death, and meeting depression and anxiety diagnostic requirements.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The study reveals that a significant percentage of Ukrainian bereaved individuals have probable ICD-11 PGD, and identifying risk factors, particularly war-related losses, will aid in the development of intervention and prevention programs for bereaved adults.</p>\u0000 </section>\u0000 </div>","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 5","pages":"425-435"},"PeriodicalIF":6.7,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140139617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>In the context of artificial intelligence (AI), the term “hallucinations” is used to refer to false responses generated by conversational agents/chatbots or other generative AI tools (artificial intelligence/machine learning models capable of generating content such as text, speech, images and video).<span><sup>1, 2</sup></span> This metaphor is unfortunate as it is both imprecise and, due to its clear negative connotation, stigmatizing for the many individuals experiencing hallucinations—those with schizophrenia and other psychotic disorders in particular.<span><sup>3</sup></span> While the stigma associated with the misunderstood use of the term hallucinations in relation to generative AI is highly unfortunate, this editorial will propose that it is, however, possible that generative AI may also be helpful and reduce stigma for those experiencing hallucinations and/or delusions.</p><p>For individuals with schizophrenia and other psychotic disorders it can be difficult to communicate the nature and quality of their hallucinations and delusions to relatives, friends as well as to the healthcare professionals involved in their treatment—often due to the recipients not handling this communication well enough.<span><sup>4</sup></span> This may lead to lack of understanding of the suffering associated with these symptoms and could, in turn, contribute to detachment from loved ones, stigma and suboptimal treatment.<span><sup>5</sup></span> Therefore, tools to facilitate communication regarding hallucinations and delusions are sorely needed.</p><p>This editorial will propose that AI tools capable of generating images (e.g., DALL·E<span><sup>6</sup></span>) and video (e.g., Sora<span><sup>7</sup></span>) may be used to facilitate (highly affordable) illustration of- and, thereby, communication regarding hallucinations and delusions experienced by people with schizophrenia and other psychotic disorders. Interestingly, this approach has recently been described in an ophthalmological case report, where Woods and colleagues report on the diagnosing and treatment of a patient with monocular Charles Bonnet syndrome secondary to optic neuritis, where generative AI was used to successfully illustrate the patient's visual hallucination.<span><sup>8</sup></span></p><p>Figure 1 shows three hypothetical examples of hallucinations and delusions illustrated using the version of DALL·E<span><sup>6</sup></span> embedded within ChatGPT-4<span><sup>9</sup></span> at the time of writing—along with the exact prompts that were used to generate the images. Two images of each set of symptoms are shown (read from left to right)—highlighting the importance of the wording of the prompts and the possibility to revise images if they are not accurately portraying the symptoms in question.</p><p>While this is, by no means, a formal assessment of the quality of the generated illustrations or their usefulness, it does seem that this approach has the potential to aid communication r
{"title":"Can generative artificial intelligence facilitate illustration of- and communication regarding hallucinations and delusions?","authors":"Søren Dinesen Østergaard","doi":"10.1111/acps.13680","DOIUrl":"10.1111/acps.13680","url":null,"abstract":"<p>In the context of artificial intelligence (AI), the term “hallucinations” is used to refer to false responses generated by conversational agents/chatbots or other generative AI tools (artificial intelligence/machine learning models capable of generating content such as text, speech, images and video).<span><sup>1, 2</sup></span> This metaphor is unfortunate as it is both imprecise and, due to its clear negative connotation, stigmatizing for the many individuals experiencing hallucinations—those with schizophrenia and other psychotic disorders in particular.<span><sup>3</sup></span> While the stigma associated with the misunderstood use of the term hallucinations in relation to generative AI is highly unfortunate, this editorial will propose that it is, however, possible that generative AI may also be helpful and reduce stigma for those experiencing hallucinations and/or delusions.</p><p>For individuals with schizophrenia and other psychotic disorders it can be difficult to communicate the nature and quality of their hallucinations and delusions to relatives, friends as well as to the healthcare professionals involved in their treatment—often due to the recipients not handling this communication well enough.<span><sup>4</sup></span> This may lead to lack of understanding of the suffering associated with these symptoms and could, in turn, contribute to detachment from loved ones, stigma and suboptimal treatment.<span><sup>5</sup></span> Therefore, tools to facilitate communication regarding hallucinations and delusions are sorely needed.</p><p>This editorial will propose that AI tools capable of generating images (e.g., DALL·E<span><sup>6</sup></span>) and video (e.g., Sora<span><sup>7</sup></span>) may be used to facilitate (highly affordable) illustration of- and, thereby, communication regarding hallucinations and delusions experienced by people with schizophrenia and other psychotic disorders. Interestingly, this approach has recently been described in an ophthalmological case report, where Woods and colleagues report on the diagnosing and treatment of a patient with monocular Charles Bonnet syndrome secondary to optic neuritis, where generative AI was used to successfully illustrate the patient's visual hallucination.<span><sup>8</sup></span></p><p>Figure 1 shows three hypothetical examples of hallucinations and delusions illustrated using the version of DALL·E<span><sup>6</sup></span> embedded within ChatGPT-4<span><sup>9</sup></span> at the time of writing—along with the exact prompts that were used to generate the images. Two images of each set of symptoms are shown (read from left to right)—highlighting the importance of the wording of the prompts and the possibility to revise images if they are not accurately portraying the symptoms in question.</p><p>While this is, by no means, a formal assessment of the quality of the generated illustrations or their usefulness, it does seem that this approach has the potential to aid communication r","PeriodicalId":108,"journal":{"name":"Acta Psychiatrica Scandinavica","volume":"149 6","pages":"441-444"},"PeriodicalIF":6.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/acps.13680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140130222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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