Factors associated with early and late response to electroconvulsive therapy.

OBJECTIVE: Electroconvulsive therapy (ECT) is an effective treatment for severe depressive symptoms, yet more research is needed to examine predictors of treatment response, and factors associated with response in patients not initially improving with treatment. This study reports factors associated with time to response (early vs. late) to ECT in a real-world setting. METHODS: This was a retrospective, single-center cohort study of patients endorsing moderate to severe depressive symptoms using the Quick Inventory of Depressive Symptomatology (QIDS; QIDS>10). Response was defined as 50% or greater decrease in QIDS score from baseline. We used logistic regression to predict response at treatment #5 (early response) as well as after treatment #5 (late response) and followed patients through ECT discontinuation or through treatment #20. RESULTS: Of the 1699 patients included in this study, 555 patients (32.7%) responded to ECT treatment at treatment #5 and 397 (23.4%) responded after treatment #5. Among patients who did not respond by treatment #5, those who switched to brief pulse width ECT from ultrabrief pulse ECT had increased odds of response after treatment #5 compared with patients only receiving ultrabrief pulse (aOR = 1.55, 95% CI: 1.16-2.07). Additionally, patients with less improvement in QIDS from baseline to treatment #5 had decreased odds of response after treatment #5 (aOR = 0.97, 95% CI = 0.97-0.98). CONCLUSION: Among depressed patients treated with ECT, response occurred in 56.0% of patients by treatment #20. Patient receiving ultrabrief pulse ECT at baseline and who did not respond by treatment #5 had greater odds of subsequent response if switched to brief pulse ECT than if continued with ultrabrief pulse.

Trends in Female Authorship in High Impact Surgical Journals Between 2008 and 2018.

OBJECTIVE: This study evaluates the distribution of authorship by sex over the last 10 years among the top 25 surgical journals. SUMMARY OF BACKGROUND DATA: Despite an increase in women entering surgical residency, there remains a sex disparity in surgical leadership. Scholarly activity is the foundation for academic promotion. However, few studies have evaluated productivity by sex in surgical literature. METHODS: Original research in the 25 highest-impact general surgery/subspecialty journals were included (1/2008-5/2018). Journals with <70% identified author sex were excluded. Articles were categorized by sex of first, last, and overall authorship. We examined changes in proportions of female first, last, and overall authorship over time, and analyzed the correlation between these measurements and journal impact factor. RESULTS: There were 71,867 articles from 19 journals included. Sex was successfully predicted for 87.3% of authors (79.1%-92.5%). There were significant increases in the overall percentage of female authors (ß = 0.55, P < 0.001), female first authors (ß = 0.97, P < 0.001), and female last authors (ß = 0.53, P < 0.001) over the study period. Notably, all cardiothoracic subspecialty journals did not significantly increase the proportion of female last authors over the study period. There were no correlations between journal impact factor and percentage of overall female authors (rs = 0.39, P = 0.09), female first authors (rs = 0.29, P = 0.22), or female last author (rs = 0.35, P = 0.13). CONCLUSIONS: This study identifies continued but slow improvement in female authorship of high-impact surgical journals during the contemporary era. However, the improvement was more apparent in the first compared to senior author positions.

Association of Aripiprazole With Reduced Hippocampal Atrophy During Maintenance Treatment of First-Episode Schizophrenia.

PURPOSE/BACKGROUND: Hippocampal volume loss in early schizophrenia has been linked with markers of inflammation and oxidative stress, and with less response of negative symptoms. Aripiprazole has been reported to preserve hippocampal volume and to reduce inflammation. METHODS/PROCEDURES: Study 1 was a 12-month multicenter randomized placebo-controlled trial of citalopram added to clinician-determined second-generation antipsychotic medication in 95 patients with first-episode schizophrenia (FES), 19 of whom received aripiprazole. We compared participants taking aripiprazole with those on other antipsychotics to determine whether those on aripiprazole had less hippocampal volume loss. We also examined peripheral biomarker data from medication-naive patients with schizophrenia receiving 8 weeks of antipsychotic treatment (n = 24) to see whether markers of inflammation and oxidative stress that previously predicted hippocampal volume differed between aripiprazole (n = 9) and other antipsychotics (study 2). FINDINGS/RESULTS: Aripiprazole was associated with a mean increase in hippocampal volume of 0.35% (SD, 0.80%) compared with a 0.53% decrease (SD, 1.2%) with other antipsychotics during the first year of maintenance treatment in patients with FES. This difference was significant after adjusting for age, sex, citalopram treatment, and baseline Brief Psychiatric Rating Scale score (B = 0.0079, P = 0.03). Aripiprazole was also associated with reduced concentrations of the inflammatory cytokines interleukin-8 and tumor necrosis factor (P < 0.01) during the first 8 weeks of treatment in medication-naive patients with FES. IMPLICATIONS/CONCLUSIONS: These results suggest that aripiprazole may protect against hippocampal atrophy via an anti-inflammatory mechanism, but these results require replication in larger, randomized trials, and the clinical relevance of hippocampal volume loss is not established.

Efficient genome-wide association in biobanks using topic modeling identifies multiple novel disease loci.

Biobanks and national registries represent a powerful tool for genomic discovery, but rely on diagnostic codes that may be unreliable and fail to capture the relationship between related diagnoses. We developed an efficient means of conducting genome-wide association studies using combinations of diagnostic codes from electronic health records (EHR) for 10845 participants in a biobanking program at two large academic medical centers. Specifically, we applied latent Dirichilet allocation to fit 50 disease topics based on diagnostic codes, then conducted genome-wide common-variant association for each topic. In sensitivity analysis, these results were contrasted with those obtained from traditional single-diagnosis phenome-wide association analysis, as well as those in which only a subset of diagnostic codes are included per topic. In meta-analysis across three biobank cohorts, we identified 23 disease-associated loci with p<1e-15, including previously associated autoimmune disease loci. In all cases, observed significant associations were of greater magnitude than for single phenome-wide diagnostic codes, and incorporation of less strongly-loading diagnostic codes enhanced association. This strategy provides a more efficient means of phenome-wide association in biobanks with coded clinical data.

Enhancing Delirium Case Definitions in Electronic Health Records Using Clinical Free Text.

BACKGROUND: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically ill populations. Delirium is preventable and treatable when diagnosed but the diagnosis is often missed. This important and difficult diagnosis is an attractive candidate for computer-aided decision support if it can be reliably identified at scale. OBJECTIVE: Here, using an electronic health record-based case definition of delirium, we characterize incidence of this highly morbid condition in 2 large academic medical centers. METHODS: Using the electronic health record of 2 large New England academic medical centers, we calculated and compared the rate of the diagnosis of delirium using a range of administrative and discharge summary text-based case definitions over an 8-year period. RESULTS: Depending on case definitions, the overall delirium rate ranged from 2.0-5.4% of 809,512 admissions identified. The identified rate of delirium increased between 2005 and 2013, such that by the final year of the study, one of the two sites reported delirium in 7.0% of cases. The concordance between case definitions was low; only half of the cases identified by text analysis were captured by administrative data. CONCLUSION: Delirium may be better captured by composite outcomes, including both administrative claims data and elements drawn from unstructured data sources. That the rate of delirium observed in this study is far lower than the current literature estimates suggests that further work on case definitions, identification, and documented diagnosis is required.

Heterogeneity in Antidepressant Treatment and Major Depressive Disorder Outcomes Among Clinicians.

Importance: While abundant work has examined patient-level differences in antidepressant treatment outcomes, little is known about the extent of clinician-level differences. Understanding these differences may be important in the development of risk models, precision treatment strategies, and more efficient systems of care. Objective: To characterize differences between outpatient clinicians in treatment selection and outcomes for their patients diagnosed with major depressive disorder across academic medical centers, community hospitals, and affiliated clinics. Design, Setting, and Participants: This was a longitudinal cohort study using data derived from electronic health records at 2 large academic medical centers and 6 community hospitals, and their affiliated outpatient networks, in eastern Massachusetts. Participants were deidentified clinicians who billed at least 10 International Classification of Diseases, Ninth Revision (ICD-9) or Tenth Revision (ICD-10) diagnoses of major depressive disorder per year between 2008 and 2022. Data analysis occurred between September 2023 and January 2024. Main Outcomes and Measures: Heterogeneity of prescribing, defined as the number of distinct antidepressants accounting for 75% of prescriptions by a given clinician; proportion of patients who did not return for follow-up after an index prescription; and proportion of patients receiving stable, ongoing antidepressant treatment. Results: Among 11 934 clinicians treating major depressive disorder, unsupervised learning identified 10 distinct clusters on the basis of ICD codes, corresponding to outpatient psychiatry as well as oncology, obstetrics, and primary care. Between these clusters, substantial variability was identified in the proportion of selective serotonin reuptake inhibitors, selective norepinephrine reuptake inhibitors, and tricyclic antidepressants prescribed, as well as in the number of distinct antidepressants prescribed. Variability was also detected between clinician clusters in loss to follow-up and achievement of stable treatment, with the former ranging from 27% to 69% and the latter from 22% to 42%. Clinician clusters were significantly associated with treatment outcomes. Conclusions and Relevance: Groups of clinicians treating individuals diagnosed with major depressive disorder exhibit marked differences in prescribing patterns as well as longitudinal patient outcomes defined by electronic health records. Incorporating these group identifiers yielded similar prediction to more complex models incorporating individual codes, suggesting the importance of considering treatment context in efforts at risk stratification.

Residual symptoms following electroconvulsive therapy: A retrospective cohort study.

BACKGROUND: Residual depressive symptoms following treatment are a burden for patients and are associated with increased risk of relapse. While this phenomenon has been explored following pharmacotherapy, there is little research into residual symptoms following electroconvulsive therapy (ECT). This study quantifies the frequency and type of residual symptoms following ECT treatment. METHODS: This study used retrospective data from patients receiving ECT as part of routine clinical care. Depressive symptomatology was assessed using the Quick Inventory of Depressive Symptomatology - Self-Report 16 item scale (QIDS), which includes 9 symptom domains graded from 0 to 3. We quantified the frequency of mild or greater (QIDS≥1) and moderate or greater (QIDS ≥ 2) residual symptoms following treatment among patients responding to ECT (QIDS decrease ≥50 % from baseline) and non-responders (QIDS decrease <50 %). RESULTS: Among 1799 patients, 1015 (56.4 %) responded to ECT and 784 (43.6 %) did not. Among responders, 99.5 % had at least one residual symptom of mild severity or greater (median = 5, IQR = 3-6) and 83.3 % had at least one residual symptom of moderate severity or greater (median = 1, IQR = 1-2). Among non-responders, 100 % had residual symptoms of mild severity or greater (median = 8, IQR = 7-9), and 99.2 % had a residual symptom of moderate severity or greater (median = 4, IQR = 3-5). The most common residual symptoms among both responders and non-responders were sleep disturbances (93.1 % and 98.7 %, respectively) and sadness (68.9 % and 96.4 %, respectively). LIMITATIONS: Retrospective data from a single freestanding psychiatric hospital. CONCLUSION: Among patients with depression receiving ECT, there were high rates of residual symptoms even among patients responding to treatment.

Individual response to electroconvulsive therapy is not correlated between multiple treatment courses.

OBJECTIVES: Electroconvulsive therapy (ECT) effectively treats depressive disorders, but many patients will have subsequent relapses. While some guidelines suggest prior response to ECT is an indication for ECT in a subsequent mood episode, it is unknown whether response to ECT is correlated between treatment courses. This study explores whether response to ECT at a first treatment correlates with response to treatment in a second independent ECT course. METHODS: Single-center retrospective cohort of patients receiving two different ECT treatment courses between 2011 and 2020 and who self-reported depression symptoms using the Quick Inventory of Depressive Symptomatology (QIDS) at baseline and following treatment #5. RESULTS: 286 patients received two independent ECT series during the study period, of whom 153 received at least 5 treatments in both series. Patients had similar QIDS scores at the start of each treatment series (Pearson's correlation, r = 0.58, p <0.001), but the change in QIDS following 5 ECT treatments was not correlated between series for individual patients (Pearson's correlation, r = 0.083, p = 0.31). In multivariate analyses, change in QIDS was similar for both treatment series, but patients were less likely to receive 5 treatments in the second treatment series. LIMITATIONS: retrospective cohort cannot control for factors influencing access to repeat ECT treatment CONCLUSIONS: While on average final QIDS score was the same following two independent treatment courses, for individual patients the change in depression symptoms was not correlated between treatment series. Further research is needed to identify factors that may predict longitudinal ECT response.

Longitudinal validation of an electronic health record delirium prediction model applied at admission in COVID-19 patients.

OBJECTIVE: To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). METHOD: Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. RESULTS: Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73-0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. CONCLUSION: This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort.

BACKGROUND: Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied. METHODS: We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures. RESULTS: Among the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both. CONCLUSIONS: While passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.

Authorship inequality: a bibliometric study of the concentration of authorship among a diminishing number of individuals in high-impact medical journals, 2008-2019.

OBJECTIVE: Authorship and number of publications are important criteria used for making decisions about promotions and research funding awards. Given the increase in the number of author positions over the last few decades, this study sought to determine if there had been a shift in the distribution of authorship among those publishing in high-impact academic medical journals over the last 12 years. DESIGN: This study analysed the distribution of authorship across 312 222 original articles published in 134 medium-impact to high-impact academic medical journals between 1 January 2008 and 31 December 2019. Additionally, this study compared the trends in author distributions across nine medical specialties and a collection of cross-specialty high-impact journal articles. PRIMARY OUTCOME MEASURES: The distribution of authorship was assessed using the Gini coefficient (GC), a widely used measure of economic inequality. RESULTS: The overall GC for all articles sampled across the 12-year study period was 0.49, and the GCs for the first and last authorship positions were 0.30 and 0.44, respectively. Since 2008, there was a significant positive correlation between year and GC for the overall authorship position (r=0.99, p<0.001) the first author position (r=0.75, p=0.007) and the last author position (r=0.85, p<0.001) indicating increasingly uneven distribution in authorship over time. The cross-specialty high-impact journals exhibited the greatest rate of increase in GC over the study period for the first and last author position of any specialty analysed. CONCLUSION: Overall, these data suggest a growing inequality in authorship across authors publishing in high-impact academic medical journals, especially among the highest impact journals. These findings may have implications for processes such as promotions and allocation of research funding that use authorship metrics as key criteria for making decisions.

Mapping of Transdiagnostic Neuropsychiatric Phenotypes Across Patients in Two General Hospitals.

BACKGROUND: Multidimensional transdiagnostic phenotyping systems are increasingly important to neuropsychiatric phenotyping, particularly in translational research settings. The relationship the National Institute of Mental Health's Research Domain Criteria multidimensional approach to psychopathology and nonpsychiatric diagnoses has not been studied at scale but is relevant to those caring for neuropsychiatric illness in medical and surgical settings. METHODS: We applied the CQH Dimensional Phenotyper natural language processing tool to estimate National Institute of Mental Health's Research Domain Criteria domain-associated symptoms of individuals admitted to nonpsychiatric wards at each of 2 large academic general hospitals over an 8-year period. We compared patterns in individual domain symptom burden, as well as a new pooled unidimensional measure, by primary medical and surgical diagnosis. RESULTS: Analysis included 227,243 patients from hospital 1 of whom 68,793 (30.3%) had a prior psychiatric history and 220,213 patients from hospital 2 of whom 50,818 (23.1%) had a prior psychiatric history. The distribution of Research Domain Criteria symptom burdens over primary diagnosis was similar across hospital sites and differed significantly across primary medical or surgical diagnosis. The effect of primary medical or surgical diagnosis was larger than that of prior psychiatric history on Research Domain Criteria symptom burden. CONCLUSION: Research Domain Criteria-based neuropsychiatric symptom burden estimated from general hospital patients' clinical documentation is more strongly associated with the primary hospital medical or surgical diagnosis than it is with the presence of a previous psychiatric history. The bidirectional role of psychiatric and somatic illness warrants further study through the lens of transdiagnostic phenotyping.

Stratified delirium risk using prescription medication data in a state-wide cohort.

OBJECTIVE: Delirium is a common condition associated with increased morbidity and mortality. Medication side effects are a possible source of modifiable delirium risk and provide an opportunity to improve delirium predictive models. This study characterized the risk for delirium diagnosis by applying a previously validated algorithm for calculating central nervous system adverse effect burden arising from a full medication list. METHOD: Using a cohort of hospitalized adult (age 18-65) patients from the Massachusetts All-Payers Claims Database, we calculated medication burden following hospital discharge and characterized risk of new coded delirium diagnosis over the following 90 days. We applied the resulting model to a held-out test cohort. RESULTS: The cohort included 62,180 individuals of whom 1.6% (1019) went on to have a coded delirium diagnosis. In the training cohort (43,527 individuals), the medication burden feature was positively associated with delirium diagnosis (OR = 5.75, 95% CI 4.34-7.63) and this association persisted (aOR = 1.95; 1.31-2.92) after adjusting for demographics, clinical features, prescribed medications, and anticholinergic risk score. In the test cohort, the trained model produced an area under the curve of 0.80 (0.78-0.82). This performance was similar across subgroups of age and gender. CONCLUSION: Aggregating brain-related medication adverse effects facilitates identification of individuals at high risk of subsequent delirium diagnosis.

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

BACKGROUND: Agitation is a common feature of many neuropsychiatric disorders. OBJECTIVE: Understanding the prevalence, implications, and characteristics of agitation among hospitalized populations can facilitate more precise recognition of disability arising from neuropsychiatric diseases. METHODS: We developed two agitation phenotypes using an expansion of expert curated term lists. These phenotypes were used to characterize five years of psychiatric admissions. The relationship of agitation symptoms and length of stay was examined. RESULTS: Among 4548 psychiatric admissions, 1134 (24.9%) included documentation of agitation based on the primary agitation phenotype. These symptoms were greater among individuals with public insurance, and those with mania and psychosis compared to major depressive disorder. Greater symptoms were associated with longer hospital stay, with ~0.9 day increase in stay for every 10% increase in agitation phenotype. CONCLUSION: Agitation was common at hospital admission and associated with diagnosis and longer length of stay. Characterizing agitation-related symptoms through natural language processing may provide new tools for understanding agitated behaviors and their relationship to delirium.

Effect of citalopram on hippocampal volume in first-episode schizophrenia: Structural MRI results from the DECIFER trial.

Hippocampal volume loss is prominent in first episode schizophrenia (FES) and has been associated with poor clinical outcomes and with BDNF genotype; antidepressants are believed to reverse hippocampal volume loss via release of BDNF. In a 12-month, placebo-controlled add-on trial of the antidepressant, citalopram, during the maintenance phase of FES, negative symptoms were improved with citalopram. We now report results of structural brain imaging at baseline and 6 months in 63 FES patients (34 in citalopram group) from the trial to assess whether protection against hippocampal volume loss contributed to improved negative symptoms with citalopram. Hippocampal volumetric integrity (HVI) did not change significantly in the citalopram or placebo group and did not differ between treatment groups, whereas citalopram was associated with greater volume loss of the right CA1 subfield. Change in cortical thickness was associated with SANS change in 4 regions (left rostral anterior cingulate, right frontal pole, right cuneus, and right transverse temporal) but none differed between treatment groups. Our findings suggest that minimal hippocampal volume loss occurs after stabilization on antipsychotic treatment and that citalopram's potential benefit for negative symptoms is unlikely to result from protection against hippocampal volume loss or cortical thinning.

What do patients learn about psychotropic medications on the web? A natural language processing study.

BACKGROUND: Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment. METHODS: We applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug classes (antidepressants, antipsychotics, and mood stabilizers), as a means of seeing if articles referencing these medications were generally positive or generally negative in tone. We compared between medications of the same class, and across medication classes. RESULTS: Across 12,733 web search results, significant within-class differences in positive (antidepressants: F(24,2682) = 2.97, p < 0.001; antipsychotics: F(16,4029) = 3.25, p < 0.001; mood stabilizers: F(8,2371) = 6.88, p < 0.001) and negative sentiment (antidepressants: F(24,6282) = 11.17, p < 0.001; antipsychotics: F(16, 4029)  = 12.13, p < 0.001; mood stabilizers: F(8, 2371) = 13.28, p < 0.001) were identified. Among these were significantly greater negative sentiment for the antidepressants sertraline, duloxetine, venlafaxine, and paroxetine, and for the antipsychotics, quetiapine and risperidone. Conversely, lithium preparations and valproate exhibited less negative sentiment than other mood stabilizing medications. LIMITATIONS: While these results provide a novel means of comparing medications, the present analyses cannot be linked to individual patient consumption of this information, or to its influence on their future clinical interactions. CONCLUSIONS: Overall, a subset of psychotropic medications were associated with significantly more negative sentiment. Characterizing these differences may allow clinicians to anticipate patient willingness to initiate or continue medications.

The Variable Effects of NSAIDs on Osteotomy Healing and Opioid Consumption.

In an effort to fight the opioid epidemic, an NSAID pain protocol was created for osteotomy patients. The study asked if NSAIDs negatively affect bone healing or reduce the need for opioids. Methods: This was a retrospective review of 155 limbs that underwent osteotomy of a long bone with fixation. Patients received an NSAID-free protocol or an NSAID protocol. Time to union and bone healing index were recorded. Results: There was not a significant difference in the time to union (P = 0.89) or bone healing index (P = 0.07). In the deformity correction group, the total milligrams of morphine equivalents prescribed after discharge was significantly less in patients receiving NSAIDs (P < 0.001). Conclusions: The use of NSAIDs after osteotomy surgery did not negatively affect bone healing and resulted in a dramatic decrease in narcotic consumption for deformity correction patients. Level of Evidence: Level III retrospective cohort study.

D-cycloserine augmentation of cognitive behavioral therapy for delusions: A randomized clinical trial.

OBJECTIVE: D-cycloserine (DCS) promotes consolidation of extinction learning. This study extends earlier work by examining whether DCS can enhance cognitive behavioral therapy (CBT) for delusions. METHODS: Adults reporting moderate or greater delusions were randomly assigned to receive 50 mg of DCS or placebo prior to 10 weekly CBT sessions. The primary outcome was change in severity of delusions measured with the Psychotic Symptom Rating Scale delusion subscale (PSYRATS-D). Secondary outcomes included persistence of response at 3 and 6 month follow-up and the effects of DCS on memory consolidation and cognitive flexibility. Fifty-eight participants were randomized and 44 completed the trial. RESULTS: The DCS and placebo groups did not differ in change from baseline to end of CBT on PSYRATS-D, nor did DCS improve memory consolidation or cognitive flexibility compared to placebo. However, at the 3 month follow-up visit (week 24), 47% of participants who completed treatment with DCS reported a 20% or greater decrease on PSYRATS-D compared to 15% in the placebo group (p = .04). Change in distress across CBT sessions interacted with treatment group to predict change from baseline to week 24 in PSYRATS-D total score (p = .03) such that response at week 24 was greatest in DCS-treated participants who experienced a decrease in distress during CBT sessions. CONCLUSIONS: DCS augmentation of CBT did not improve delusions compared to placebo during treatment; however, DCS was associated with a higher response rate at 3-month follow-up. DCS may produce a delayed therapeutic effect, associated with successful CBT sessions, but this finding requires replication.

Gender Trends in Authorship in Psychiatry Journals From 2008 to 2018.

BACKGROUND: Women are currently underrepresented in academic psychiatry. As publication activity reflects both leadership and participation in academia, we examined temporal trends in women's authorship by conducting a large-scale bibliometric study of psychiatry journals. METHODS: We examined changes in proportions of women in the first, last, and overall authorship positions over time; relationship to journal impact factor and editorial board makeup; and rates of transition to senior author status using original research articles published in the 24 highest-impact psychiatry journals between January 2008 and May 2018. RESULTS: In 30,934 articles, women represented 40.0% of all authors in 2008 and 44.8% in 2018, with a significant increase in the percentage of women as first authors (2008: 43.5%, 2018: 49.5%; B = 0.64, p = .002) and last authors over time (2008: 30.0%, 2018: 35.7%; B = 0.64, p = 1 × 10-5). Articles with women as last authors were significantly more likely than those with men as last authors to have a woman as first author (χ21 = 126.1, p < 2.2 × 10-16). Women exhibited slower rates of transition to the last author position (log rank p = 2 × 10-16); time to 10% transition was 5 years for men and 9 years for women. CONCLUSIONS: These results indicate continued improvement in the representation of women authors in psychiatry journals, resulting in near parity in first authors. However, slower rates of transition to the senior author position and continued underrepresentation of women as senior authors suggest ongoing challenges in achieving gender parity in academic leadership. At the present rate of change for last authors (0.64% increase per year), women would achieve parity in senior authorship in ∼20 to 25 years.

Risk tolerance measured by probability discounting among individuals with primary mood and psychotic disorders.

OBJECTIVE: Change in risk tolerance is a feature of multiple psychiatric disorders and may contribute to adverse outcomes. We used a probability discounting (PD) task to measure risk-taking behavior among individuals with bipolar disorder (BPAD), major depressive disorder (MDD), schizoaffective disorder (SCAD), and schizophrenia (SCZ). METHOD: A PD task was administered to 117 patients and 88 healthy controls (HCs), along with a cognitive battery using the Cambridge Neuropsychological Test Automated Battery, and relevant symptomatology scales. We examined differences in PD rates between diagnostic groups, and compared with HCs, while controlling for potential confounding factors including measures of cognitive functioning. RESULTS: Individuals with a diagnosis of BPAD or SCAD/SCZ prefer smaller, more guaranteed rewards rather than larger, less likely rewards as compared with healthy controls (p = .002 and p = .034, respectively). There was no effect of performance on cognitive tasks, antipsychotic treatment, or symptomatology on the rate of probability discounting. CONCLUSION: This study supports the transdiagnostic measurement of risk-taking behaviors, even when such behaviors are not the primary area of psychopathology. Quantifying risk-taking may enable targeted therapeutic strategies across disorders. (PsycINFO Database Record (c) 2019 APA, all rights reserved).