JAMA Psychiatry最新文献

{"title":"Automating the Addiction Behaviors Checklist for Problematic Opioid Use Identification","authors":"Angus H. Chatham, Eli D. Bradley, Vanessa Troiani, Donielle L. Beiler, Parker Christy, Lori Schirle, Sandra Sanchez-Roige, David C. Samuels, Alvin D. Jeffery","doi":"10.1001/jamapsychiatry.2025.0424","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0424","url":null,"abstract":"ImportanceIndividuals whose chronic pain is managed with opioids are at high risk of developing an opioid use disorder. Electronic health records (EHR) allow large-scale studies to identify a continuum of problematic opioid use, including opioid use disorder. Traditionally, this is done through diagnostic codes, which are often unreliable and underused.ObjectiveTo determine whether regular expressions, an interpretable natural language processing technique, could automate a validated clinical tool (Addiction Behaviors Checklist) to identify problematic opioid use.Design, Setting, and ParticipantsThis cross-sectional study reports on a retrospective cohort with data analyzed from 2021 through 2023. The approach was evaluated against a blinded, manually reviewed holdout test set and validated against an independent test set at a separate institution. The study used data from Vanderbilt University Medical Center’s Synthetic Derivative, a deidentified version of the EHR for research purposes. This cohort comprised 8063 individuals with chronic pain, defined by diagnostic codes on at least 2 days. The study team collected free-text notes, demographics, and diagnostic codes and performed an external validation with 100 individuals with chronic pain from Geisinger, recruited from an interventional pain clinic cohort.Main Outcomes and MeasuresThe primary outcome was the evaluation of the automated method in identifying patients demonstrating problematic opioid use and its comparison with manual medical record review and opioid use disorder diagnostic codes. Methods with F1 scores were evaluated (a single value that combines sensitivity and positive predictive value at a single threshold) and areas under the curve (a single value that combines sensitivity and specificity across multiple thresholds).ResultsAmong the 8063 patients in the primary site (5081 female [63%] and 2982 male [37%]; mean [SD] age, 56 [16] years) and 100 patients in the validation site (57 female [57%] and 43 male [43%]; mean [SD] age, 54 [13] years), the automated approach outperformed diagnostic codes based on F1 scores (0.73; 95% CI, 0.62-0.83 vs 0.08; 95% CI, 0.00-0.19 at the primary site and 0.70; 95% CI, 0.50-0.85 vs 0.29; 95% CI, 0.07-0.50 at the validation site) and areas under the curve (0.82; 95% CI, 0.73-0.89 vs 0.52; 95% CI, 0.50-0.55 at the primary site and 0.86; 95% CI, 0.76-0.94 vs 0.59;95% CI, 0.50-0.67 at validation site).ConclusionsThis automated data extraction technique may facilitate earlier identification of people at risk for and who are experiencing problematic opioid use, and create new opportunities for studying long-term sequelae of opioid pain management.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"8 2 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinician Suicide Risk Assessment for Prediction of Suicide Attempt in a Large Health Care System","authors":"Kate H. Bentley, Chris J. Kennedy, Pratik N. Khadse, Jasmin R. Brooks Stephens, Emily M. Madsen, Matthew J. Flics, Hyunjoon Lee, Jordan W. Smoller, Taylor A. Burke","doi":"10.1001/jamapsychiatry.2025.0325","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0325","url":null,"abstract":"ImportanceClinical practice guidelines recommend suicide risk screening and assessment across behavioral health settings. The predictive accuracy of real-world clinician assessments for stratifying patients by risk of future suicidal behavior, however, remains understudied.ObjectiveTo evaluate routine clinical suicide risk assessment for prospectively predicting suicide attempt.Design, Setting, and ParticipantsThis electronic health record–based, prognostic study included 89 957 patients (≥5 years of age) with a structured suicide risk assessment (based on the Suicide Assessment Five-step Evaluation and Triage framework) that was documented by 2577 clinicians during outpatient, inpatient, and emergency department encounters at 12 hospitals in the Mass General Brigham health system between July 2019 and February 2023.Main Outcomes and MeasuresThe primary outcome was an emergency department visit with a suicide attempt code recorded in the electronic health record within 90 days or 180 days of the index suicide risk assessment. The predictive performance of suicide risk assessments was evaluated on a temporal test set first using stratified prevalence (clinicians’ overall risk estimates from a single suicide risk assessment item indicating minimal, low, moderate, or high risk) and then using machine learning models (incorporating all suicide risk assessment items).ResultsOf the 812 114 analyzed suicide risk assessments from the electronic health record, 58.81% were with female patients and 3.27% were with patients who were Asian, 5.26% were Black, 3.02% were Hispanic, 77.44% were White, and 11.00% were of Other or Unknown race. After suicide risk assessments were conducted during outpatient encounters, the suicide attempt rate was 0.12% within 90 days and 0.22% within 180 days; for inpatient encounters, the rate was 0.79% within 90 days and 1.29% within 180 days; and for emergency department encounters, the rate was 2.40% within 90 days and 3.70% within 180 days. Among patients evaluated during outpatient encounters, clinicians’ overall single-item risk estimates had an area under the curve (AUC) value of 0.77 (95% CI, 0.72-0.81) for 90-day suicide attempt prediction; among patients evaluated during inpatient encounters, the AUC was 0.64 (95% CI, 0.59-0.69); and among patients evaluated during emergency department encounters, the AUC was 0.60 (95% CI, 0.55-0.64). Incorporating all clinician-documented suicide risk assessment items (87 predictors) via machine learning significantly increased the AUC for 90-day risk prediction to 0.87 (95% CI, 0.83-0.90) among patients evaluated during outpatient encounters, 0.79 (95% CI, 0.74-0.84) among patients evaluated during inpatient encounters, and 0.76 (95% CI, 0.72-0.80) among patients evaluated during emergency department encounters. Performance was similar for 180-day suicide risk prediction. The positive predictive values for the best-performing machine learning models (with 95% specificity) ranged from ","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"96 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcranial Magnetic Stimulation in Ukraine.","authors":"Noah S Philip","doi":"10.1001/jamapsychiatry.2025.0209","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0209","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":22.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convergence of Cannabis and Psychosis on the Dopamine System","authors":"Jessica Ahrens, Sabrina D. Ford, Betsy Schaefer, David Reese, Ali R. Khan, Philip Tibbo, Rachel Rabin, Clifford M. Cassidy, Lena Palaniyappan","doi":"10.1001/jamapsychiatry.2025.0432","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0432","url":null,"abstract":"ImportanceDespite evidence that individuals with a cannabis use disorder (CUD) are at elevated risk of psychosis and that the neurotransmitter dopamine has a role in psychosis, the mechanism linking cannabis use and psychosis remains unclear.ObjectiveTo use neuromelanin-sensitive magnetic resonance imaging (MRI), referred to as the <jats:italic>neuromelanin-MRI signal</jats:italic>, a practical, proxy measure of dopamine function, to assess whether a common alteration in the dopamine system may be implicated in CUD and psychosis and whether this alteration can be observed in those with a CUD whether or not they have a diagnosis of first-episode schizophrenia (FES).Design, Setting, and ParticipantsThis longitudinal observational cohort study recruited individuals from 2019 to 2023 from an early psychosis service and the surrounding communities in London, Ontario. The sample included individuals with and without CUD, with some in each group also diagnosed with FES.ExposuresFES and CUD diagnoses from the Structured Clinical Interview for <jats:italic>DSM-5</jats:italic>.Main Outcomes and MeasuresNeuromelanin-MRI signals within the midbrain (substantia nigra [SN]/ventral tegmental area [VTA]) including a subregion previously linked to the severity of untreated psychosis (a priori region of interest). Linear mixed-effects analyses were performed relating neuromelanin-MRI signals to clinical measures.ResultsA total of 36 individuals without CUD (mean [SD] age, 22.3 [3.2] years; 29 male [81%]; 12 with FES) and 25 individuals with CUD (mean [SD] age, 24.3 [4.7] years; 22 male [88%]; 16 with FES) participated in the study. One-year follow-up was completed for 12 individuals with CUD and 25 without CUD. CUD was associated with elevated neuromelanin-MRI signal in a set of ventral SN/VTA voxels (387 of 2060 SN/VTA voxels, corrected <jats:italic>P</jats:italic> = .03, permutation test). CUD was also associated with elevated neuromelanin-MRI signal in the psychosis-related region of interest (<jats:italic>t</jats:italic><jats:sub>92</jats:sub> = 2.12, <jats:italic>P</jats:italic> = .04) with a significant dose-dependent association (higher burden of CUD symptoms associated with higher neuromelanin-MRI signal, <jats:italic>F</jats:italic><jats:sub>1, 96</jats:sub> = 4.89; <jats:italic>P</jats:italic> = .03). In contrast, participants with FES did not exhibit a significant elevation in neuromelanin-MRI signal (241 SN/VTA voxels had elevated signal, corrected <jats:italic>P</jats:italic> = .09). There was no association between time and neuromelanin-MRI signal.Conclusions and RelevanceElevated dopamine function in a critical SN/VTA subregion may be associated with psychosis risk in people with CUD. Cannabis was associated with the hypothesized final common pathway for the clinical expression of psychotic symptoms.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"39 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143806245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional vs Structural Cortical Deficit Pattern Biomarkers for Major Depressive Disorder.","authors":"Peter Kochunov, Bhim M Adhikari, David Keator, Daniel Amen, Si Gao, Nicole R Karcher, Demetrio Labate, Robert Azencott, Yewen Huang, Hussain Syed, Hongjie Ke, Paul M Thompson, Danny J J Wang, Braxton D Mitchell, Jessica A Turner, Theo G M van Erp, Neda Jahanshad, Yizhou Ma, Xiaoming Du, William Burroughs, Shuo Chen, Tianzhou Ma, Jair C Soares, L Elliot Hong","doi":"10.1001/jamapsychiatry.2025.0192","DOIUrl":"10.1001/jamapsychiatry.2025.0192","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;Major depressive disorder (MDD) is a severe mental illness characterized more by functional rather than structural brain abnormalities. The pattern of regional homogeneity (ReHo) deficits in MDD may relate to underlying regional hypoperfusion. Capturing this functional deficit pattern provides a brain pattern-based biomarker for MDD that is linked to the underlying pathophysiology.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To examine whether cortical ReHo patterns provide a replicable biomarker for MDD that is more sensitive than reduced cortical thickness and evaluate whether the ReHo MDD deficit pattern reflects regional cerebral blood flow (RCBF) deficit patterns in MDD and whether a regional vulnerability index (RVI) thus constructed may provide a concise brain pattern-based biomarker for MDD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, settings, and participants: &lt;/strong&gt;The UK Biobank (UKBB) participants had ReHo and structural measurements. Participants from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) Consortium were included for measuring the MDD structural cortical deficit pattern. The UKBB ReHo and ENIGMA cortical thickness effect sizes for MDD were used to test the deficit patterns in the Amish Connectome Project (ACP) with ReHo, structural, and RCBF data. Finally, the Ament Clinic Inc (ACI) sample had RCBF data measured using single-photon emission computed tomography. Data were analyzed from August 2021 to September 2024.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Exposures: &lt;/strong&gt;ReHo and structural measurements.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Included in this analysis were 4 datasets: (1) UKBB (N = 4810 participants; 2220 with recurrent MDD and 2590 controls; mean [SD] age, 63.0 [7.5] years; 1121 female [50%]), (2) ENIGMA (N = 10 115 participants; 2148 with MDD and 7957 healthy controls; mean [SD] age, 39.9 [10.0] years; 5927 female [59%]), (3) ACP (N = 204 participants; 68 with a lifetime diagnosis of MDD and 136 controls; mean [SD] age, 41.0 [14.5] years; 104 female [51%]), and (4) ACI (N = 372 participants; 296 with recurrent MDD and 76 controls; mean [SD] age, 45.3 [17.2] years; 189 female [51%]). MDD participants had lower cortical ReHo in the cingulum, superior temporal lobe, frontal lobe, and several other areas, with no significant differences in cortical thickness. The regional pattern of ReHo MDD effect sizes was significantly correlated with that of RCBF obtained from 2 independent datasets (Pearson r = 0.52 and Pearson r = 0.46; P &lt; 10-4). ReHo and RCBF functional RVIs showed numerically stronger effect sizes (Cohen d = 0.33-0.90) compared with structural RVIs (Cohen d = 0.09-0.20). Elevated ReHo-based RVI-MDD values in individuals with MDD were associated with higher depression symptom severity across cohorts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions and relevance: &lt;/strong&gt;Results of this case-control study suggest that the ReHo MDD deficit pattern reflected cortical hypoperfusion and was regionally specific in MDD. ReHo-based RVI ","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":22.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining Social Determinants of Suicide Risk Research.","authors":"Ping-I Lin, Nawar Nayeem, Erick Messias","doi":"10.1001/jamapsychiatry.2025.0333","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0333","url":null,"abstract":"","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":22.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Confounding in the Association Between Traumatic Brain Injury and Mental Disorder or Suicide","authors":"Søren Dinesen Østergaard, Jessica Mundy, Alisha Silvia Mercedes Hall, Katherine L. Musliner","doi":"10.1001/jamapsychiatry.2025.0318","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0318","url":null,"abstract":"ImportanceTraumatic brain injury is common and occurs across all ages. Observational studies have shown that traumatic brain injury is associated with a wide range of mental disorders and suicide. Whether these associations represent a causal effect is, however, difficult to establish, and confounding by genetic liability for mental disorder may play a substantial role.ObjectiveTo investigate whether observational associations between traumatic brain injury and mental disorder or suicide could be confounded by genetic liability for mental disorder.Design, Setting, and ParticipantsThis cohort study was conducted from October 2023 to January 2025. The study population consisted of the general population subcohort of the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) sample, which is a representative sample of the Danish population born between 1981 and 2008 that has been genotyped.ExposuresPolygenic risk scores (PRSs) for schizophrenia, bipolar disorder, depression, and attention-deficit/hyperactivity disorder (ADHD) calculated from the genotypes and genome-wide association summary statistics.Main Outcomes and MeasuresThe primary outcome was traumatic brain injury, operationalized via hospital diagnoses. The associations between PRSs for schizophrenia, bipolar disorder, depression, and ADHD, respectively, and traumatic brain injury were examined via Cox proportional hazards regression, yielding hazard rate ratios (HRRs) with 95% confidence intervals.ResultsThe final cohort consisted of a total of 40 274 individuals, of whom 19 802 (49.2%) were female. A total of 3341 (8.3%) of the cohort members (of whom 1464 [43.8%] were female and 1877 [56.2%] were male) experienced traumatic brain injury during follow-up. All 4 PRSs showed statistically significant positive associations with traumatic brain injury (PRS-schizophrenia: HRR, 1.06; 95% CI, 1.02-1.10; <jats:italic>P</jats:italic> = .002; PRS-bipolar disorder: HRR, 1.04; 95% CI, 1.00-1.08; <jats:italic>P</jats:italic> = .04; PRS-depression: HRR, 1.10; 95% CI, 1.06-1.14; <jats:italic>P</jats:italic> &amp;amp;lt; .001; and PRS-ADHD: HRR, 1.12; 95% CI, 1.08-1.16; <jats:italic>P</jats:italic> &amp;amp;lt; .001).Conclusions and RelevanceThe results of this cohort study suggest that confounding by genetic liability for mental disorder could explain some of the association between traumatic brain injury and mental disorder or suicide. Consequently, genetic liability for mental disorder should be factored into future studies of these associations to avoid overestimation of causality.","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"33 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Esketamine Combined With SSRI or SNRI for Treatment-Resistant Depression.","authors":"Antonio Del Casale, Sara Spirito, Jan Francesco Arena, Saskia Preissner, Marina Borro, Giovanna Gentile, Martina Nicole Modesti, Robert Preissner, Stefano Ferracuti, Maurizio Simmaco","doi":"10.1001/jamapsychiatry.2025.0200","DOIUrl":"10.1001/jamapsychiatry.2025.0200","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;Treatment-resistant depression (TRD) remains a critical challenge in psychiatry, with limited effective options. Esketamine, a rapid-acting antidepressant, is usually combined with a selective serotonin reuptake inhibitor (SSRI) or a serotonin-norepinephrine reuptake inhibitor (SNRI), but comparative evidence of these combinations' effectiveness in real-world settings is sparse.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To determine whether the combination of esketamine + SNRI shows differences in clinical outcomes compared to esketamine + SSRI in patients with TRD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, setting, and participants: &lt;/strong&gt;This retrospective cohort study was conducted in September 2024 using data from the TriNetX global health research network, with a 5-year time window from the first esketamine trial. TriNetX data are drawn from real-world clinical settings and use electronic medical records from more than 90 health care centers across 20 countries. Adults with TRD who were treated with esketamine combined with either an SSRI or an SNRI were eligible for inclusion.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Exposure: &lt;/strong&gt;Treatment with esketamine combined with an SSRI (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, or vilazodone) or an SNRI (desvenlafaxine, duloxetine, levomilnacipran, milnacipran, or venlafaxine).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcomes and measures: &lt;/strong&gt;The primary outcomes were all-cause mortality, hospitalization, depression relapse, and suicide attempts. Kaplan-Meier survival analysis was used to estimate survival probabilities, while risk ratios and odds ratios were calculated for all outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In a population-based sample of 61 882 adult participants with TRD who were treated with esketamine combined with either an SSRI or an SNRI, 55 480 participants were selected after applying propensity score matching for age and sex. These patients were divided into 2 matched cohorts: 27 740 patients treated with esketamine + SSRI (16 007 female participants [57.7%]; mean [SD] age, 46.0 [21.3] years) and 27 740 treated with esketamine + SNRI (16 242 female participants [58.6%]; mean [SD] age, 45.9 [21.9] years). In the entire study population, the incidence of mortality, hospitalizations, depressive relapses, and suicide attempts was low throughout the study period. Patients in the esketamine + SNRI group had significantly lower all-cause mortality (5.3% vs 9.1%; P &lt; .001), hospitalization rates (0.1% vs 0.2%; P &lt; .001), and depression relapses (14.8% vs 21.2%; P &lt; .001) compared to the esketamine + SSRI group, which instead showed a lower incidence of suicidal attempts (0.3% vs 0.5%; P = .04).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions and relevance: &lt;/strong&gt;In this retrospective comparative effectiveness study, among the study sample, incidence of mortality, hospitalizations, depressive relapses, and suicide attempts was low. The esketamine + SNRI group showed lower incidence of mortality, hospi","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":""},"PeriodicalIF":22.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143763973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should Inflammation Be a Specifier for Major Depression in the DSM-6?","authors":"Manish K. Jha, Marion Leboyer, Carmine M. Pariante, Andrew H. Miller","doi":"10.1001/jamapsychiatry.2025.0206","DOIUrl":"https://doi.org/10.1001/jamapsychiatry.2025.0206","url":null,"abstract":"This Viewpoint discusses the opportunity to include inflammatory biomarkers as specifiers for major depression in the upcoming Sixth Edition of the <jats:italic>Diagnostic and Statistical Manual of Mental Disorders</jats:italic> (<jats:italic>DSM-6</jats:italic>).","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":"1 1","pages":""},"PeriodicalIF":25.8,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychiatric, Neurological, and Somatic Comorbidities in Intermittent Explosive Disorder.","authors":"Yanli Zhang-James, John Paliakkara, Joshua Schaeffer, Joseph Strayhorn, Stephen V Faraone","doi":"10.1001/jamapsychiatry.2024.4465","DOIUrl":"10.1001/jamapsychiatry.2024.4465","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Importance: &lt;/strong&gt;Intermittent explosive disorder (IED) is an understudied psychiatric condition marked by impulsive aggression and poorly regulated emotional control, often resulting in interpersonal and societal consequences. Better understanding of comorbidities can improve screening, diagnosis, and treatment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To investigate the prevalence of IED and its associations with psychiatric, neurological, and somatic disorders.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Design, setting, and participants: &lt;/strong&gt;In this cohort study, matched groups of patients with and without IED were identified from the TriNetX Research Network (dated January 31, 2024). Electronic medical record data were analyzed. The mean (SD) time from the first to last known visits was 4.8 (5.4) years.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Exposure: &lt;/strong&gt;Lifetime diagnosis of IED.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Main outcomes and measures: &lt;/strong&gt;Main outcomes were International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnostic categories and root codes. Cox proportional hazard models were used to estimate and compare probabilities of acquiring other diagnoses. Key measures include the numbers and proportions of patients with these diagnoses and adjusted hazard ratios (HRs) for IED.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Overall, 30 357 individuals with IED and 30 357 demographically matched controls were included. In each group, 21 313 (70%) were male, with a mean (SD) age at the first visit 26 (17) years. Despite only 0.03% of the total patient population having an IED diagnosis, extensive comorbidities with psychiatric, neurological, and somatic conditions were found. A notable 95.7% of individuals with IED (29 054 individuals) had another psychiatric diagnosis. All psychiatric subcategories and 92% of the psychiatric diagnoses were significantly associated with IED, with HRs ranging from 2.1 (95% CI, 2.0-2.2) for substance use disorder to 76.6 (95% CI, 65.4-89.6) for disorders of adult personality and behavior (excluding IED). Among neurological conditions, neurodegenerative diseases (HR, 5.0; 95% CI, 4.1-6.1) and epilepsy (HR, 4.9; 95% CI, 4.3-5.6) had the highest HRs, followed by movement disorders (HR, 3.1; 95% CI, 2.8-3.5), cerebral palsy (HR, 2.6; 95% CI, 2.2-3.0), and sleep disorders (HR, 2.2; 95% CI, 2.1-2.3). Significant associations with IED were also observed for many somatic diseases, including obesity (HR, 1.6; 95% CI, 1.5-1.7), hyperlipidemia (HR, 1.5; 95% CI, 1.4-1.5), hypertension (HR, 1.6; 95% CI, 1.5-1.7), and gastroesophageal reflux disease (HR, 1.7; 95% CI, 1.7-1.9).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion and relevance: &lt;/strong&gt;These findings highlight the extensive comorbidities between IED and psychiatric, neurological, and somatic disorders, emphasizing the need for integrated diagnostic and treatment approaches addressing both psychological and physical health aspects of IED. Limitations related to reliance on medical records and low diagnostic rat","PeriodicalId":14800,"journal":{"name":"JAMA Psychiatry","volume":" ","pages":"358-367"},"PeriodicalIF":22.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}