Structural brain plasticity and inflammation are independently related to changes in depressive symptoms six months after an index ECT course.

BACKGROUND: Electroconvulsive therapy (ECT) is effective for treatment-resistant depression and leads to short-term structural brain changes and decreases in the inflammatory response. However, little is known about how brain structure and inflammation relate to the heterogeneity of treatment response in the months following an index ECT course. METHODS: A naturalistic six-month study following an index ECT course included 20 subjects with treatment-resistant depression. Upon conclusion of the index ECT course and again after six months, structural magnetic resonance imaging scans and peripheral inflammation measures [interleukin-6 (IL-6), IL-8, tumor necrosis factor (TNF-α), and C-reactive protein] were obtained. Voxel-based morphometry processed with the CAT-12 Toolbox was used to estimate changes in gray matter volume. RESULTS: Between the end of the index ECT course and the end of follow-up, we found four clusters of significant decreases in gray matter volume (p < 0.01, FWE) and no regions of increased volume. Decreased HAM-D scores were significantly related only to reduced IL-8 level. Decreased volume in one cluster, which included the right insula and Brodmann's Area 22, was related to increased HAM-D scores over six months. IL-8 levels did not mediate or moderate the relationship between volumetric change and depression. CONCLUSIONS: Six months after an index ECT course, multiple regions of decreased gray matter volume were observed in a naturalistic setting. The independent relations between brain volume and inflammation to depressive symptoms suggest novel explanations of the heterogeneity of longer-term ECT treatment response.

Risk of suicide attempts and self-harm after 1.4 million general medical hospitalizations of men with mental illness.

BACKGROUND: The short-term risk of suicide after medical hospital discharge is four times higher among men compared with women. As previous work has identified female-specific antecedents of suicide-related behavior after medical hospitalization of women with serious mental illness, we examined predictors among a similar population of men with multimorbidity. METHODS: Classification and regression tree (CART) models were developed and validated using electronic health records (EHRs) from 1,423,161 medical (non-psychiatric) hospitalizations of men ≥ 18-years-old with an existing diagnosis of a depressive disorder, bipolar disorder, or chronic psychosis. Hospitalizations occurred between 2009 and 2017. Risk groups were evaluated using an independent testing set. The primary outcome was readmission within one year associated with ICD-9 or -10 code for self-harm or attempt. RESULTS: The 1-year readmission rate for intentional self-harm and suicide attempt was 3.9% (55,337/1,423,161 hospitalizations). The classification model discriminated risk with area under the curve (AUC) 0.73 (Confidence Interval [95%CI] 0.68-0.74), accuracy 0.82 (95%CI 0.71-0.83), sensitivity 82.6% (95%CI 81.2-84), and specificity 83.1% (95%CI 81.7-84.5). Strongest predictors were medical comorbidity, prior self-harm, age, and prior hospitalization. Men with greater medical comorbidity burden and prior self-harm were at highest risk (Odds Ratio [OR] 3.10, 95%CI 3.02-3.18), as were men < 62-years-old with few medical comorbidities (OR 1.11 95%CI 1.08-1.13). LIMITATIONS: The study focused on medical hospitalizations for suicide attempt and thus captured only severe attempts resulting in hospitalization. CONCLUSIONS: After medical hospitalization, men with serious mental illness experienced a high risk of self-harm (1:25 hospitalizations). Risk was particularly elevated among younger patients without prior medical conditions and older patients with medical comorbidity and prior self-harm.

Interleukin-8 and lower severity of depression in females, but not males, with treatment-resistant depression.

INTRODUCTION: In cross-sectional studies of depressed patients, relationships between depression and levels of IL-8 are inconsistent, and have not been examined in relation to sex. Given identified sex differences in longitudinal data, it is important to evaluate sex-specific cross-sectional relationships between IL-8 and depressive symptoms, which may explain some inconsistency in the extant literature. It is further unknown whether IL-8 levels may relate to specific symptom profiles among depressed patients, with or without regard to sex. METHODS: Among 108 patients with treatment resistant depression (50 females), we evaluated cross-sectional relationships between IL-8 and depression severity, as measured by the Hamilton Depression Rating Scale [HAM-D] Score, and examined sex-specific relationships, as well as relationships with depressive symptom profiles. Other inflammatory markers (IL-6, IL-10, TNF-α, CRP) were also explored in relation to HAM-D. RESULTS: Higher IL-8 was associated with lower total HAM-D score (standardized ß = -0.19, p = 0.049). Sex-specific effects were identified (IL-8 x sex interaction: p = 0.03), in which higher IL-8 related to lower HAM-D score in females (standardized ß = -0.41, p = 0.004, effect size (sr2) = 0.17), but not males (standardized ß = 0.02, p = 0.91). Among a subset of 94 patients (41 females) who had individual HAM-D items available, we evaluated relationships between IL-8 and HAM-D factor subscores. Across sexes, higher IL-8 was associated with lower anxiety/hypochondriasis subscores (standardized ß = -0.31, p = 0.002; sex interaction: p = 0.99). Sex differences were identified for relationships between IL-8 and two other HAM-D factor subscores. CONCLUSIONS: IL-8 may be related to anxiety symptoms across sexes, but may have a sex-specific relationship with other depressive symptoms. Further evaluation of sex-specific relationships between IL-8, depression symptom profiles, treatment response, and potential neurobiological correlates, may inform mechanisms of depression pathophysiology and aid in development of precision medicine strategies.

Depression treatment response to ketamine: sex-specific role of interleukin-8, but not other inflammatory markers.

Inflammation plays a role in depression pathophysiology and treatment response, with effects varying by sex and therapeutic modality. Lower levels of interleukin(IL)-8 predict depression response to antidepressant medication and to electroconvulsive therapy (ECT), although ECT effects are specific to females. Whether IL-8 predicts depression response to ketamine and in a sex-specific manner is not known. Here, depressed patients (n = 46; female, n = 17) received open label infusion of ketamine (0.5 mg/kg over 40 min; NCT02165449). Plasma levels of IL-8 were evaluated at baseline and post-treatment. Baseline levels of IL-8 had a trending association with response to ketamine, depending upon sex (responder status × sex interaction: p = 0.096), in which lower baseline levels of IL-8 in females (p = 0.095) but not males (p = 0.96) trended with treatment response. Change in levels of IL-8 from baseline to post-treatment differed significantly by responder status (defined as ≥50% reduction in Hamilton Depression Rating Scale [HAM-D] Score), depending upon sex (responder status × sex × time interaction: F(1,42)=6.68, p = 0.01). In addition, change in IL-8 interacted with sex to predict change in HAM-D score (ß = -0.63, p = 0.003); increasing IL-8 was associated with decreasing HAM-D score in females (p = 0.08) whereas the inverse was found in males (p = 0.02). Other inflammatory markers (IL-6, IL-10, tumor necrosis factor-α, C-reactive protein) were explored with no significant relationships identified. Given these preliminary findings, further evaluation of sex differences in the relationship between IL-8 and treatment response is warranted to elucidate mechanisms of response and aid in the development of personalized approaches to depression treatment.

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing. OBJECTIVE: To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis. METHODS: We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network. RESULTS: The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted). CONCLUSIONS: Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.

Non-Convulsive Status Epilepticus in the Presence of Catatonia: A Clinically Focused Review.

INTRODUCTION: Catatonia is classically associated with psychiatric conditions but may occur in medical and neurologic disorders. Status epilepticus (SE) is a seizure lasting more than five minutes or two or more seizures within a five-minute period without interictal recovery of consciousness. Non-convulsive status epilepticus (NCSE) is SE without prominent motor activity that may present with catatonic symptoms. The relevance of NCSE as a potential etiology for catatonia is not clear in the literature. METHODS: A systematic review was completed to evaluate the literature on NCSE presenting with catatonia. PubMed and PsycInfo databases were searched and articles were reviewed for the presence of catatonia and NCSE. RESULTS: 15 articles describing 27 cases meeting inclusion criteria were identified. The authors add 1 case to the literature. The most common catatonic symptoms identified in NCSE were mutism and stupor. Clinical features frequent in NCSE presenting with catatonia included new catatonic symptoms, age over 50 years, comorbid neurological conditions, or a change in medications that affect seizure threshold. A documented psychiatric history was also common and may contribute to delayed diagnosis. DISCUSSION/CONCLUSION: It is important to consider NCSE in the differential diagnosis of new catatonic symptoms. A suggested approach to diagnostic evaluation is provided.

Predicting suicidal behavior and self-harm after general hospitalization of adults with serious mental illness.

Individuals with psychiatric disorders are vulnerable to adverse mental health outcomes following physical illness. This longitudinal cohort study defined risk profiles for readmission for suicidal behavior and self-harm after general hospitalization of adults with serious mental illness. Structured electronic health record data were analyzed from 15,644 general non-psychiatric index hospitalizations of individuals with depression, bipolar, and psychotic disorders admitted to an urban health system in the southwestern United States between 2006 and 2017. Using data from one-year prior to and including index hospitalization, supervised machine learning was implemented to predict risk of readmission for suicide attempt and self-harm in the following year. The Classification and Regression Tree algorithm produced a classification prediction with an area under the receiver operating curve (AUC) of 0.86 (95% confidence interval (CI) 0.74-0.97). Incidence of suicide-related behavior was highest after general non-psychiatric hospitalizations of individuals with prior suicide attempt or self-harm (18%; 69 cases/389 hospitalizations) and lowest after hospitalizations associated with very high medical morbidity burden (0 cases/3090 hospitalizations). Predictor combinations, rather than single risk factors, explained the majority of risk, including concomitant alcohol use disorder with moderate medical morbidity, and age ≤55-years-old with low medical morbidity. Findings suggest that applying an efficient and highly interpretable machine learning algorithm to electronic health record data may inform general hospital clinical decision support, resource allocation, and preventative interventions for medically ill adults with serious mental illness.

Neural Subtypes of Euthymic Bipolar I Disorder Characterized by Emotion Regulation Circuitry.

BACKGROUND: Current diagnostic strategy for bipolar disorders relies on symptomological classification. Yet, responses to both pharmacological and psychotherapeutic treatments vary widely, suggesting that underlying neuropathological differences are not well defined by current nosology. Classifying patients with bipolar disorder based on emotion regulation network (ERN) activation may account for some of the heterogeneity within the disorder. METHODS: Euthymic participants diagnosed with bipolar I disorder (n = 86) and healthy subjects (n = 80) underwent functional magnetic resonance imaging scans while engaged in emotional reappraisal of negative stimuli. After determining average regional activations in key network regions, we applied agglomerative hierarchical clustering to identify subtypes of bipolar disorder. Next, we examined relations among neural subtypes, demographic variables, and mood symptoms. RESULTS: Analyses revealed two primary neural subtypes of euthymic bipolar I disorder participants. The first subtype, ERN cluster 1, was characterized by increased amygdala activation and slightly increased ventrolateral prefrontal and subgenual cingulate activation, whereas ERN cluster 2 was defined by decreased amygdala activation with wider-spread prefrontal activation. Cluster 1 was associated with a higher number of hospitalizations for depression (odds ratio = 1.30, 95% confidence interval = 1.02-1.64) and later onset of manic episodes (odds ratio = 1.06, 95% confidence interval = 1.00-21.13) than cluster 2. ERN clusters of healthy subjects differed from bipolar disorder clusters and were defined by differential activation of the prefrontal cortex. ERN clusters of healthy subjects, which differed from bipolar disorder clusters, were defined by differential activation of the prefrontal cortex. CONCLUSIONS: Emotion regulation circuitry can distinguish neurobiological subtypes of bipolar disorder in the euthymic state. These subtypes, which are differentially associated with indices of illness severity and subsyndromal affective symptoms, may help to inform relapse risk and more personalized treatment approaches.

High-Risk Phenotypes of Early Psychiatric Readmission in Bipolar Disorder With Comorbid Medical Illness.

BACKGROUND: Individuals with co-existing serious mental illness and non-psychiatric medical illness are at high risk of acute care utilization. Mining of electronic health record data can help identify and categorize predictors of psychiatric hospital readmission in this population. OBJECTIVE: This study aimed to identify modifiable predictors of psychiatric readmission among individuals with comorbid bipolar disorder and medical illness. This goal was accomplished by applying objective variable selection via machine learning techniques. METHOD: This was a retrospective analysis of electronic health record data derived from 77,296 episodes of care from 2006 to 2016 within the University of California Health Care System. Data included 1,250 episodes of care involving patients with bipolar disorder and serious comorbid medical illnesses (defined by transfer between medicine and psychiatry services or concomitant primary medical and psychiatric admission diagnoses). Machine learning (classification trees) was used to identify potential predictors of 30-day psychiatric readmission across hospital encounters. Predictors included demographics, medical and psychiatric diagnoses, medication regimen, and disposition. The algorithm was internally validated using 10-fold cross-validation. RESULTS: The model predicted 30-day readmission with high accuracy (98% unbalanced model, 88% balanced model). Modifiable predictors of readmission were length of stay, transfers between medical and psychiatric services, discharge disposition to home, and all-cause acute health service utilization in the year before the index hospitalization. CONCLUSION: Among bipolar disorder patients with comorbid medical conditions, characteristics of the index hospitalization (e.g., duration, transfer, and disposition) emerged as more predictive than static properties of the patient (e.g., sociodemographic factors and psychiatric comorbidity burden). Findings identified phenotypes of patients at high risk for rehospitalization and suggest potential ways of modifying the risk of early readmission.

Diagnostic and clinical implications of functional neuroimaging in bipolar disorder.

Advances in functional neuroimaging have ushered in studies that have enhanced our understanding of the neuropathophysiology of bipolar disorder, but do not yet have clinical applications. We describe the major circuits (ventrolateral, dorsolateral, ventromedial, and anterior cingulate) thought to be involved in the corticolimbic dysregulation that may underlie mood states in patients with bipolar disorder. The potential clinical application of functional neuroimaging in bipolar disorder is considered in terms of prognostic, predictive, and treatment biomarkers. To date, most research has focused on prognostic biomarkers to differentiate patients with bipolar disorder from those with other affective or psychotic diagnoses, or healthy subjects. The search for treatment biomarkers, which suggest mechanisms of pharmacodynamic or treatment response, and predictive biomarkers has thus far involved only pediatric patients diagnosed with bipolar disorder. The results to date are encouraging and suggest that functional neuroimaging may be of eventual benefit in determining biomarkers of treatment response. Further refinement of biomarker identification, and perhaps even illness characterization are needed to find prognostic and predictive biomarkers of bipolar disorder.

Anti-N-methyl-D-aspartate receptor encephalitis: a targeted review of clinical presentation, diagnosis, and approaches to psychopharmacologic management.

BACKGROUND: Anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis was formally described in 2007 and includes a range of psychiatric and neurologic symptoms. Most patients with anti-NMDAR encephalitis initially present to psychiatrists for diagnosis and treatment. However, there is limited literature summarizing treatment strategies for psychiatric symptoms. In an effort to improve identification and treatment, this review article provides an overview of anti-NMDAR encephalitis, with a focus on psychopharmacologic treatment strategies. Two case reports provide a clinical context for the literature review. METHODS: The authors conducted a PubMed search. RESULTS: Prominent psychiatric symptoms of anti-NMDAR encephalitis include psychosis, agitation, insomnia, and catatonia. Neuroleptics may be helpful for managing psychosis and agitation, but may exacerbate movement abnormalities. Diphenhydramine and benzodiazepines are helpful for agitation and insomnia. In addition, the anticholinergic affinity of diphenhydramine can improve dystonia or rigidity attributable to anti-NMDAR encephalitis, while benzodiazepines and electroconvulsive therapy have been used for catatonia associated with this condition. CONCLUSIONS: Psychiatrists play an important role in the diagnosis and treatment of anti-NMDAR encephalitis. Recognizing the typical clinical progression and closely monitoring for accompanying neurologic symptoms will facilitate diagnosis and timely treatment. Careful selection of psychopharmacological interventions may reduce suffering.

Predictive socioeconomic and clinical profiles of antidepressant response and remission.

BACKGROUND: There are many prognostic factors for treatment outcome in major depressive disorder (MDD). The predictive power of any single factor, however, is limited. We aimed to develop profiles of antidepressant response and remission based upon hierarchical combinations of baseline clinical and demographic factors. METHODS: Using data from Level 1 of the Sequenced Treatment Alternatives to Relieve Depression trial (STAR*D), in which 2,876 participants with MDD were treated with citalopram, a signal-detection analysis was performed to identify hierarchical predictive profiles for patients with different treatment outcome. An automated algorithm was used to determine the optimal predictive variables by evaluating sensitivity, specificity, positive and negative predictive value, and test efficiency. RESULTS: Hierarchical combinations of baseline clinical and demographic factors yielded profiles that significantly predicted treatment outcome. In contrast to an overall 47% response rate in STAR*D Level 1, response rates of profiled patient subgroups ranged from 31 to 63%. In contrast to an overall remission rate of 28%, identified subsets of patients had a 12 to 55% probability of remission. The predictors of antidepressant treatment outcome most commonly incorporated into profiles were related to socioeconomic status (e.g., income, education), whereas indicators of depressive symptom type and severity, as well as comorbid clinical conditions, were useful but less powerful predictors. CONCLUSIONS: Hierarchical profiles of demographic and clinical baseline variables categorized patients according to the likelihood they would benefit from a single antidepressant trial. Socioeconomic factors had greater predictive power than symptoms or other clinical factors, and profiles combining multiple factors were stronger predictors than individual factors alone.

Benzodiazepine loading versus symptom-triggered treatment of alcohol withdrawal: a prospective, randomized clinical trial.

OBJECTIVES: The objectives were to compare the efficacy of a benzodiazepine loading versus a symptom-triggered protocol in the management of alcohol withdrawal. METHODS: We conducted a prospective, randomized, controlled trial including 47 consecutive patients admitted to one of two tertiary care medical centers who developed alcohol withdrawal syndrome. Patients were randomly assigned to either a benzodiazepine loading protocol or a symptom-triggered treatment protocol. The Clinical Institute Withdrawal Assessment for Alcohol-Revised scale (CIWA-Ar) was recorded throughout the length of stay, along with measures of autonomic system functioning. RESULTS: The average rate of change of CIWA-Ar scores was -1.5 ± 1.3 for the symptom-triggered group and -2.3 ± 2.5 for the loading group. Average rate of change for systolic blood pressure was -2.7 ± 5.3 for the symptom-triggered group and -2.3 ± 6.4 for the loading group. There was no significant difference between the rates of change for either group on either measure. Similarly, there was no significant difference in total benzodiazepine use between groups. Within 72 h of treatment, 69.6% of patients in the loading group were free of withdrawal symptoms versus 41.7% in the symptom-triggered group, a difference not reaching statistical significance. CONCLUSIONS: This study did not reveal clear evidence of a clinical advantage for choosing either treatment method.

Mood-state effects on amygdala volume in bipolar disorder.

BACKGROUND: Prior structural neuroimaging studies of the amygdala in patients with bipolar disorder have reported higher or lower volumes, or no difference relative to healthy controls. These inconsistent findings may have resulted from combining subjects in different mood states. The prefrontal cortex has recently been reported to have a lower volume in depressed versus euthymic bipolar patients. Here we examined whether similar mood state-dependent volumetric differences are detectable in the amygdala. METHODS: Forty subjects, including 28 with bipolar disorder type I (12 depressed and 16 euthymic), and 12 healthy comparison subjects were scanned on a 3T magnetic resonance image (MRI) scanner. Amygdala volumes were manually traced and compared across subject groups, adjusting for sex and total brain volume. RESULTS: Statistical analyses found a significant effect of mood state and hemisphere on amygdala volume. Subsequent comparisons revealed that amygdala volumes were significantly lower in the depressed bipolar group compared to both the euthymic bipolar (p=0.005) and healthy control (p=0.043) groups. LIMITATIONS: Our study was cross-sectional and some patients were medicated. CONCLUSIONS: Our results suggest that mood state influences amygdala volume in subjects with bipolar disorder. Future studies that replicate these findings in unmedicated patient samples scanned longitudinally are needed.

Effectiveness of quetiapine plus lamotrigine maintenance therapy in challenging bipolar disorder patients.

OBJECTIVE: Assess quetiapine plus lamotrigine (QTP+LTG) combination maintenance therapy effectiveness in challenging bipolar disorder (BD). METHOD: Outpatients assessed with the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD) Affective Disorders Evaluation and followed with the STEP-BD Clinical Monitoring Form were naturalistically prescribed QTP+LTG. RESULTS: Fifty-four outpatients with challenging BD, taking in addition to QTP+LTG, a mean±SD of 2.1±1.6 (in 63.0% at least 2) other psychotropic and 2.3±1.9 non-psychotropic prescription medications, had QTP+LTG maintenance trials. Median(mean±SD) QTP+LTG duration was 401(730±756) days. Final QTP and LTG doses were 87.5(188±211) and 300(287±108) mg/day, respectively. Half (27/54) of patients discontinued QTP (in 19), LTG (in 6), or QTP+LTG (in 2), after 294(415±414) days - due to side-effects in 10, inefficacy in seven, non-adherence in five, and other reasons in five. 42.6%(23/54) had additional pharmacotherapy intervention for emergent mood symptoms, after 175(261±237) days, with at least one psychotropic added (in 16/54) or substantively (by ≥50%) increased (in 7/54). 55.6%(30/54) had recurrent mood episodes, after 126(187±158) days, most often depressive (in 35.2%), although 64.8%(35/54) were euthymic at final visit taking QTP+LTG. Sedation increased significantly during treatment among those with side-effect discontinuations, and 19.2%(10/52, all having QTP added to LTG) had clinically significant (≥7%) weight gain. LIMITATIONS: No placebo comparison group. Small sample of predominantly female Caucasian insured outpatients taking complex concurrent medication regimens. CONCLUSION: Additional studies are warranted to confirm our preliminary observation that QTP+LTG maintenance may be effective in patients with challenging BD.

Individual risk profiles for postoperative delirium after joint replacement surgery.

BACKGROUND: Delirium occurs in nearly half of older patients after joint replacement surgery. However, risk profiles for developing delirium have not been established. OBJECTIVE: We sought to identify risk profiles for delirium in patients following joint replacement surgery. METHOD: Based on data from a randomized, double-blind, placebo-controlled trial of olanzapine (10 mg) as delirium prophylaxis in 400 patients (67-81 years old) undergoing hip or knee replacement surgery, we performed a signal detection analysis to develop risk profiles for postsurgical delirium (using baseline patient characteristics, iatrogenic factors, and physiologic response parameters). RESULTS: Olanzapine reduced the incidence of delirium by 63% relative to placebo. Among patients receiving placebo, those with an ASA class = 3 and age ≥ 74 years had a 64% risk of delirium. Those with ASA class < 3 still had a 67% risk of delirium if postoperative oxygen saturation was < 95%. Patients who received olanzapine had an 83% risk of developing delirium if they received ≥ 42.5 mg equivalents of intra-operative morphine, were ≥ 74 years old, and had a mean arterial pressure (MAP) < 90 mm Hg at the presurgical screening visit. Patients with the lowest risk (6%) of developing delirium received olanzapine had a hematocrit ≥ 28%, and a presurgical MAP ≥ 90. CONCLUSION: Although use of prophylactic olanzapine reduced the incidence of delirium, subsets of patients remained likely to develop delirium. The risk of developing delirium may be reduced through prophylactic dispensation of olanzapine, maintaining optimal perfusion and oxygenation, and limiting intra-operative opioids.

Preliminary evidence of within-subject changes in gray matter density associated with remission of bipolar depression.

A preliminary within-subjects MRI study of seven patients with a diagnosis of bipolar I disorder revealed that, compared to remission, depression was associated with gray matter density increases in subgenual prefrontal cortex, parahippocampal gyrus, and inferior temporal gyri. Decreases were observed in superior and inferior frontal gyri and anterior cingulate.

Safety and tolerability associated with second-generation antipsychotic polytherapy in bipolar disorder: findings from the Systematic Treatment Enhancement Program for Bipolar Disorder.

CONTEXT: Practitioners often combine 2 or more second-generation antipsychotics (SGAs) in patients with bipolar disorder, despite an absence of data to support their safety, tolerability, or efficacy. OBJECTIVE: This study sought to evaluate the safety and tolerability of SGA polytherapy compared to SGA monotherapy in bipolar disorder patients receiving open naturalistic treatment in the 22-site Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). METHOD: A longitudinal cohort of 1,958 patients who were prescribed at least 1 SGA was drawn from 4,035 bipolar patients in STEP-BD recruited between November 1999 and July 2005 and assessed at least quarterly for a mean duration of 21 months. Main outcome measures were the mean quarterly prevalence of adverse events, medical and psychiatric service usage, Global Assessment of Functioning ratings, and percentage of days spent well. RESULTS: Almost 10% of patients taking SGAs were prescribed SGA polytherapy. After controlling for illness onset, age, baseline illness severity, and medication load, patients prescribed SGA polytherapy, compared to monotherapy, exhibited more dry mouth (number needed to harm [NNH] = 4), tremor (NNH = 6), sedation (NNH = 8), sexual dysfunction (NNH = 8), and constipation (NNH = 11) and were almost 3 times as likely to incur more psychiatric and medical care; there was no association with greater global functioning scores or percentage of days spent well. CONCLUSIONS: Although SGA polytherapy was fairly common in bipolar disorder, it was associated with increased side effects and health service use but not with improved clinical status or function. Thus, SGA polytherapy in bipolar disorder may incur important disadvantages without clear benefit, warranting careful consideration before undertaking such interventions.