Rising? trends in Anxiety and Depression among Individuals with Rheumatoid Arthritis: A Population-Based Study.

OBJECTIVE: To investigate trends in depression and anxiety over three decades among individuals with rheumatoid arthritis (RA). METHODS: Patients with incident RA (age >18 years, meeting 1987 ACR criteria in 1985-2014) were identified using the Rochester Epidemiology Project. Individuals with RA were matched 1:1 with non-RA comparators on age, sex, and calendar year of RA incidence. Patients were followed until death, migration or 12/31/2020. Depression and anxiety were defined using established ICD9/10 code sets. Cox models were used to compare trends in the occurrence of depression and anxiety diagnoses and cooccurring anxiety and depression by decade and RA status, adjusted for potential confounders. RESULTS: The study included 1,012 individuals with RA and 1,012 matched controls (mean age 55.9 years; 68.38% female). Hazard ratios (HR) demonstrated a temporal increase in anxiety and co-occurring anxiety and depression from 2005-2014 compared to 1985-1994 for individuals both with and without RA. Persons with RA exhibited a rising occurrence of anxiety (HR: 1.27; 95% confidence interval (CI): 0.86-1.88). and concomitant anxiety and depression (HR: 1.49; 95% CI: 0.96-2.33) compared to controls. Trends were most pronounced in seropositive RA patients (HR for anxiety: 4.01; 95% CI: 2.21-7.30). CONCLUSION: Anxiety and concomitant anxiety and depression diagnoses are elevated in individuals with RA. The increasing occurrence of anxiety and co-occurring anxiety and depression suggests rising awareness and diagnosis of these disorders. Adding to stable but high rates of depression diagnoses, individuals with RA now have evidence of a widening gap in mental health diagnoses that clinicians should address.

Predicting remission after acute phase pharmacotherapy in patients with bipolar I depression: A machine learning approach with cross-trial and cross-drug replication.

OBJECTIVES: Interpatient variability in bipolar I depression (BP-D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]). METHODS: Supervised machine learning models were trained on data from BP-D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission. RESULTS: An AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine-treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week-4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week-4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45-5.76; p < 9.3E-11). CONCLUSION: Machine learning strategies achieved cross-trial and cross-drug replication in predicting remission after 8 weeks of pharmacotherapy for BP-D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.

Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.

BACKGROUND: The occupational burnout epidemic is a growing issue, and in the United States, up to 60% of medical students, residents, physicians, and registered nurses experience symptoms. Wearable technologies may provide an opportunity to predict the onset of burnout and other forms of distress using physiological markers. OBJECTIVE: This study aims to identify physiological biomarkers of burnout, and establish what gaps are currently present in the use of wearable technologies for burnout prediction among health care professionals (HCPs). METHODS: A comprehensive search of several databases was performed on June 7, 2022. No date limits were set for the search. The databases were Ovid: MEDLINE(R), Embase, Healthstar, APA PsycInfo, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Science Core Collection via Clarivate Analytics, Scopus via Elsevier, EBSCOhost: Academic Search Premier, CINAHL with Full Text, and Business Source Premier. Studies observing anxiety, burnout, stress, and depression using a wearable device worn by an HCP were included, with HCP defined as medical students, residents, physicians, and nurses. Bias was assessed using the Newcastle Ottawa Quality Assessment Form for Cohort Studies. RESULTS: The initial search yielded 505 papers, from which 10 (1.95%) studies were included in this review. The majority (n=9) used wrist-worn biosensors and described observational cohort studies (n=8), with a low risk of bias. While no physiological measures were reliably associated with burnout or anxiety, step count and time in bed were associated with depressive symptoms, and heart rate and heart rate variability were associated with acute stress. Studies were limited with long-term observations (eg, ≥12 months) and large sample sizes, with limited integration of wearable data with system-level information (eg, acuity) to predict burnout. Reporting standards were also insufficient, particularly in device adherence and sampling frequency used for physiological measurements. CONCLUSIONS: With wearables offering promise for digital health assessments of human functioning, it is possible to see wearables as a frontier for predicting burnout. Future digital health studies exploring the utility of wearable technologies for burnout prediction should address the limitations of data standardization and strategies to improve adherence and inclusivity in study participation.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses.

BACKGROUND: When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. METHODS: A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. DISCUSSION: The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. TRIAL REGISTRATION: NCT05481138.

Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.

BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N = 271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N = 255) or placebo (N = 265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression.

Effects of a multidisciplinary quality of life intervention on sleep quality in patients with advanced cancer receiving radiation therapy.

OBJECTIVES: Sleep disturbances are prevalent in cancer patients, especially those with advanced disease. There are few published intervention studies that address sleep issues in advanced cancer patients during the course of treatment. This study assesses the impact of a multidisciplinary quality of life (QOL) intervention on subjective sleep difficulties in patients with advanced cancer. METHOD: This randomized trial investigated the comparative effects of a multidisciplinary QOL intervention (n = 54) vs. standard care (n = 63) on sleep quality in patients with advanced cancer receiving radiation therapy as a secondary endpoint. The intervention group attended six intervention sessions, while the standard care group received informational material only. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), administered at baseline and weeks 4 (post-intervention), 27, and 52. RESULTS: The intervention group had a statistically significant improvement in the PSQI total score and two components of sleep quality and daytime dysfunction than the control group at week 4. At week 27, although both groups showed improvements in sleep measures from baseline, there were no statistically significant differences between groups in any of the PSQI total and component scores, or ESS. At week 52, the intervention group used less sleep medication than control patients compared to baseline (p = 0.04) and had a lower ESS score (7.6 vs. 9.3, p = 0.03). SIGNIFICANCE OF RESULTS: A multidisciplinary intervention to improve QOL can also improve sleep quality of advanced cancer patients undergoing radiation therapy. Those patients who completed the intervention also reported the use of less sleep medication.

The association of obesity and coronary artery disease genes with response to SSRIs treatment in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Treatment outcomes of acute bipolar depressive episode with psychosis.

BACKGROUND: The impact of psychosis on the treatment of bipolar depression is remarkably understudied. The primary aim of this study was to compare treatment outcomes of bipolar depressed individuals with and without psychosis. The secondary aim was to compare the effect of lithium and quetiapine, each with adjunctive personalized treatments (APTs), in the psychotic subgroup. METHODS: We assessed participants with DSM-IV bipolar depression included in a comparative effectiveness study of lithium and quetiapine with APTs (the Bipolar CHOICE study). Severity was assessed by the Bipolar Inventory of Symptoms Scale (BISS) and by the Clinical Global Impression Scale-Severity-Bipolar Version (CGI-S-BP). Mixed models were used to assess the course of symptom change, and Cox regression survival analysis was used to assess the time to remission. RESULTS: Psychotic features were present in 10.6% (n = 32) of the depressed participants (n = 303). Those with psychotic features had higher scores on the BISS before (75.2 ± 17.6 vs. 54.9 ± 16.3; P < .001) and after (37.2 ± 19.7 vs. 26.3 ± 18.0; P = .003) 6-month treatment. The CGI-S-BP yielded similar results. Participants with and without psychosis had similar course of symptom improvement and similar time to remission. There was no significant difference in the treatment outcomes of lithium (n = 11) and quetiapine (n = 21) among the psychotic subgroup. CONCLUSION: Bipolar depressive episodes with psychotic features are more severe, and compared to nonpsychotic depressions, present a similar course of improvement. Given the small number of participants presenting psychosis, the lack of statistically significant difference between lithium- and quetiapine-based treatment of psychotic bipolar depressive episodes needs replication in a larger sample.

The association of exercise during pregnancy with trimester-specific and postpartum quality of life and depressive symptoms in a cohort of healthy pregnant women.

Few published studies have examined the relationship between exercise during pregnancy, quality of life (QOL), and postpartum depressive symptoms in healthy pregnant women. A prospective cohort of 578 healthy pregnant women were followed during their pregnancy through 6 months postpartum. Levels of self-reported exercise and QOL before, during, and following pregnancy were assessed using standardized questionnaires during each trimester of pregnancy and 6 months postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS) at 28 weeks gestation and 6 weeks postpartum. Participants were classified as having "sufficient exercise" if they achieved at least 150 min of exercise per week. Sufficient exercisers reported significantly higher ratings on most domains of QOL during each trimester of pregnancy and in the postpartum follow-up, compared with insufficient exercisers. There were no significant between-group differences in depressive symptoms. In examining the impact of exercise during each trimester, active women who became sedentary during their third trimester demonstrated a decline in their QOL. Achieving recommended levels of exercise during pregnancy was associated with higher QOL during pregnancy and the postpartum in healthy pregnant women. Decreasing the amount of exercise during pregnancy was associated with reduced QOL. These results suggest that it may be important for health care professionals to counsel healthy pregnant women about both the benefits of being physically active during pregnancy, and to provide guidance on how to remain physically active during a healthy pregnancy.

Decreased activation and subsyndromal manic symptoms predict lower remission rates in bipolar depression.

OBJECTIVE: Activation encompasses energy and activity and is a central feature of bipolar disorder. However, the impact of activation on treatment response of bipolar depression requires further exploration. The aims of this study were to assess the association of decreased activation and sustained remission in bipolar depression and test for factors that could affect this association. METHODS: We assessed participants with Diagnostic and Statistical Manual of Mental Disorders (4th ed) bipolar depression ( n = 303) included in a comparative effectiveness study of lithium- and quetiapine-based treatments (the Bipolar CHOICE study). Activation was evaluated using items from the Bipolar Inventory of Symptoms Scale. The selection of these items was based on a dimension of energy and interest symptoms associated with poorer treatment response in major depression. RESULTS: Decreased activation was associated with lower remission rates in the raw analyses and in a logistic regression model adjusted for baseline severity and subsyndromal manic symptoms (odds ratio = 0.899; p = 0.015). The manic features also predicted lower remission (odds ratio = 0.934; p < 0.001). Remission rates were similar in the two treatment groups. CONCLUSION: Decreased activation and subsyndromal manic symptoms predict lower remission rates in bipolar depression. Patients with these features may require specific treatment approaches, but new studies are necessary to identify treatments that could improve outcomes in this population.

KETAMINE FOR TREATMENT-RESISTANT UNIPOLAR AND BIPOLAR MAJOR DEPRESSION: CRITICAL REVIEW AND IMPLICATIONS FOR CLINICAL PRACTICE.

There is an urgent need for more rapidly effective pharmacotherapies for major depressive disorder and bipolar disorder (BP) that are efficacious and tolerable for depressed patients who respond poorly to conventional treatments. Multiple controlled trials have now demonstrated a rapid, nonsustained antidepressive response to a single intravenous infusion of ketamine. Early controlled studies of intranasal or serial infusion therapy appear promising. The effective dose for depression is lower than the typical anesthetic doses, and side-effects are generally mild and transient. The data investigating the adjunctive use of concurrent ketamine in the course of electroconvulsive therapy (ECT) for depression do not suggest efficacy or tolerability. The therapeutic potential of ketamine has stimulated considerable excitement among clinicians, patients, and industry, and has led to the increasing use of ketamine as an off-label substitute for ECT and other antidepressive treatments. This clinical review of ketamine will assess the evidence-based use of ketamine and initial clinical implications of further development of a potentially novel treatment for rapid reduction of symptoms in depressed patients.

Quantifying the impact of chronic conditions on a diagnosis of major depressive disorder in adults: a cohort study using linked electronic medical records.

BACKGROUND: Major depressive disorder (MDD) is often comorbid with other chronic mental and physical health conditions. Although the literature widely acknowledges the association of many chronic conditions with the risk of MDD, the relative importance of these conditions on MDD risk in the presence of other conditions is not well investigated. In this study, we aimed to quantify the relative contribution of selected chronic conditions to identify the conditions most influential to MDD risk in adults and identify differences by age. METHODS: This study used electronic health record (EHR) data on patients empanelled with primary care at Mayo Clinic in June 2013. A validated EHR-based algorithm was applied to identify newly diagnosed MDD patients between 2000 and 2013. Non-MDD controls were matched 1:1 to MDD cases on birth year (±2 years), sex, and outpatient clinic visits in the same year of MDD case diagnosis. Twenty-four chronic conditions defined by Chronic Conditions Data Warehouse were ascertained in both cases and controls using diagnosis codes within 5 years of index dates (diagnosis dates for cases, and the first clinic visit dates for matched controls). For each age group (45 years or younger, between 46 and 60, and over 60 years), conditional logistic regression models were used to test the association between each condition and subsequent MDD risk, adjusting for educational attainment and obesity. The relative influence of these conditions on the risk of MDD was quantified using gradient boosting machine models. RESULTS: A total of 11,375 incident MDD cases were identified between 2000 and 2013. Most chronic conditions (except for eye conditions) were associated with risk of MDD, with different association patterns observed depending on age. Among 24 chronic conditions, the greatest relative contribution was observed for diabetes mellitus for subjects aged ≤ 60 years and rheumatoid arthritis/osteoarthritis for those over 60 years. CONCLUSIONS: Our results suggest that specific chronic conditions such as diabetes mellitus and rheumatoid arthritis/osteoarthritis may have greater influence than others on the risk of MDD.

Validation of the 17-item Hamilton Depression Rating Scale definition of response for adults with major depressive disorder using equipercentile linking to Clinical Global Impression scale ratings: analysis of Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) data.

OBJECTIVE: The study aimed to define thresholds of clinically significant change in 17-item Hamilton Depression Rating Scale (HDRS-17) scores using the Clinical Global Impression-Improvement (CGI-I) Scale as a gold standard. METHODS: We conducted a secondary analysis of individual patient data from the Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study, an 8-week, single-arm clinical trial of citalopram or escitalopram treatment of adults with major depression. We used equipercentile linking to identify levels of absolute and percent change in HDRS-17 scores that equated with scores on the CGI-I at 4 and 8 weeks. Additional analyses equated changes in the HDRS-7 and Bech-6 scale scores with CGI-I scores. RESULTS: A CGI-I score of 2 (much improved) corresponded to an absolute decrease (improvement) in HDRS-17 total score of 11 points and a percent decrease of 50-57%, from baseline values. Similar results were observed for percent change in HDRS-7 and Bech-6 scores. Larger absolute (but not percent) decreases in HDRS-17 scores equated with CGI-I scores of 2 in persons with higher baseline depression severity. CONCLUSIONS: Our results support the consensus definition of response based on HDRS-17 scores (>50% decrease from baseline). A similar definition of response may apply to the HDRS-7 and Bech-6. Copyright © 2016 John Wiley & Sons, Ltd.

Complexity of illness and adjunctive benzodiazepine use in outpatients with bipolar I or II disorder: results from the Bipolar CHOICE study.

Benzodiazepines are widely prescribed for patients with bipolar disorders in clinical practice, but very little is known about the subtypes of patients with bipolar disorder or aspects of bipolar illness that contribute most to benzodiazepine use. We examined the prevalence of and factors associated with benzodiazepine use among 482 patients with bipolar I or II disorder enrolled in the Bipolar CHOICE study. Eighty-one subjects were prescribed benzodiazepines at study entry and were considered benzodiazepine users. Stepwise logistic regression was used to model baseline benzodiazepine use versus nonuse, using entry and exit criteria of P < 0.1. In bivariate analyses, benzodiazepine users were prescribed a significantly higher number of other psychotropic medications and were more likely to be prescribed lamotrigine or antidepressants as compared with benzodiazepine nonusers. Benzodiazepine users were more likely to have a diagnosis of bipolar I disorder and comorbid anxiety disorder, but not comorbid alcohol or substance use disorders. Benzodiazepine users also had experienced more anxiety and depressive symptoms and suicidality, but not irritability or manic symptoms, than did benzodiazepine nonusers. In the multivariate model, anxiety symptom level (regardless of diagnosis), lamotrigine use, number of concomitant psychotropic medications, college education, and high household income predicted benzodiazepine use. Benzodiazepine use in patients with bipolar disorders is associated with greater illness complexity as indicated by a higher number of concomitant psychotropic medications and higher anxiety symptom burden, regardless of a comorbid anxiety disorder diagnosis. Demographic factors were also important determinants of benzodiazepine use, which may be related to access to care and insurance coverage for benzodiazepines.

The brain-derived neurotrophic factor (BDNF) Val66Met polymorphism is associated with increased body mass index and insulin resistance measures in bipolar disorder and schizophrenia.

OBJECTIVES: We tested the hypothesis that a common functional variant in brain-derived neurotrophic factor (BDNF), Val66Met, which has been shown to be associated with increased body mass index (BMI) in schizophrenia (SCZ) and schizoaffective disorder (SAD), is also associated with antipsychotic-induced weight gain in bipolar disorder (BPD). Association of Val66Met with other metabolic measures, including high- and low-density cholesterol, triglycerides, total cholesterol, fasting blood glucose, and hemoglobin A1c, was also tested. METHODS: This was a 12-month, prospective, randomized trial of two atypical antipsychotic drugs (APDs) with moderate (risperidone) or high (olanzapine) risk to cause weight gain. Subjects were diagnosed as having BPD (n = 90) and SCZ or SAD (n = 76). RESULTS: BMI was significantly greater in all diagnoses for Met66 allele carriers at six months (p = 0.01). Met66 carriers with BPD showed a greater increase in the triglycerides/high-density (HDL) cholesterol ratio (p = 0.01), a key marker for metabolic syndrome related to insulin resistance, and log-triglycerides (p = 0.04), after three or six months of treatment. Met66 carriers had the greatest increase in log-triglycerides (p = 0.03) and triglycerides/HDL cholesterol ratio after three months of treatment with risperidone (p = 0.003), and the highest BMI at six months (p = 0.01). CONCLUSIONS: The positive association of BNDF Val66Met with high BMI values replicates previous findings in patients with SCZ and indicates the BDNF Val66Met genotype as a potential risk factor for obesity and insulin resistance measures in patients with BPD receiving antipsychotics as well.

Sex differences in the risk of rapid cycling and other indicators of adverse illness course in patients with bipolar I and II disorder.

OBJECTIVES: To examine the independent effects of sex on the risk of rapid cycling and other indicators of adverse illness course in patients with bipolar I disorder (BP-I) or bipolar II disorder (BP-II). METHODS: We analyzed data from the first 1,225 patients enrolled in the Mayo Clinic Individualized Medicine Biobank for Bipolar Disorder. Demographic and clinical variables were ascertained using standardized questionnaires; height and weight were assessed to determine body mass index (BMI). Rates of rapid cycling, cycle acceleration, and increased severity of mood episodes over time were compared between women and men overall and within subgroups defined by bipolar disorder subtype (BP-I or BP-II). Multiple logistic regression analysis was used to assess the independent effect of sex on the risk of these indicators of adverse illness course. RESULTS: Women had significantly higher rates of rapid cycling than men. Overall rates of rapid cycling were higher in patients with BP-II than BP-I; and sex differences in the rate of rapid cycling were more pronounced in patients with BP-II than BP-I, although the power to detect statistically significant differences was reduced due to the lower sample size of subjects with BP-II. Female sex was a significant predictor of rapid cycling, cycle acceleration, and increased severity of mood episodes over time after adjusting for age, bipolar disorder subtype, BMI, having any comorbid psychiatric disorder, and current antidepressant use. CONCLUSIONS: Female sex was associated with significantly higher risk of rapid cycling, cycle acceleration, and increased severity of mood episodes over time in a sample of 1,225 patients with bipolar disorders.

Association between history of psychosis and cardiovascular disease in bipolar disorder.

OBJECTIVES: To determine whether clinical features of bipolar disorder, such as history of psychosis, and cardiovascular disease (CVD) risk factors contribute to a higher risk of CVD among patients with bipolar disorder. METHODS: This cross-sectional study included a sample of 988 patients with bipolar I or bipolar II disorder or schizoaffective bipolar type confirmed by the Structured Clinical Interview for DSM-IV-TR disorders (SCID). Medical comorbidity burden was quantified utilizing the Cumulative Illness Severity Rating Scale (CIRS). This 13-item organ-based scale includes cardiac disease severity quantification. Confirmed by medical record review, patients who scored 1 (current mild or past significant problem) or higher in the cardiac item were compared by logistic regression to patients who scored 0 (no impairment), adjusting for CVD risk factors that were selected using a backwards stepwise approach or were obtained from the literature. RESULTS: In a multivariate model, age [odds ratio (OR) = 3.03, 95% confidence interval (CI): 1.66-5.54, p < 0.0001], hypertension (OR = 2.43, 95% CI: 1.69-3.55, p < 0.0001), and history of psychosis (OR = 1.48, 95% CI: 1.03-2.13, p = 0.03) were associated with CVD. When CVD risk factors from the literature were added to the analysis, age (OR = 3.19, 95% CI: 1.67-6.10, p = 0.0005) and hypertension (OR = 2.46, 95% CI: 1.61-3.76, p < 0.01) remained significant, with psychosis being at the trend level (OR = 1.43, 95% CI: 0.96-2.13, p = 0.08). CONCLUSIONS: The phenotype of psychotic bipolar disorder may reflect higher illness severity with associated cardiac comorbidity. Further studies are encouraged to clarify the effect of the disease burden (i.e., depression), lifestyle, and treatment interventions (i.e., atypical antipsychotics) on this risk association.

Medical burden in bipolar disorder: findings from the Clinical and Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study (Bipolar CHOICE).

OBJECTIVES: Individuals with bipolar disorder have high rates of other medical comorbidity, which is associated with higher mortality rates and worse course of illness. The present study examined common predictors of medical comorbidity. METHODS: The Clinical and Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study (Bipolar CHOICE) enrolled 482 participants with bipolar I or bipolar II disorder in a six-month, randomized comparative effectiveness trial. Baseline assessments included current and lifetime DSM-IV-TR diagnoses, demographic information, psychiatric and medical history, severity of psychiatric symptoms, level of functioning, and a fasting blood draw. Medical comorbidities were categorized into two groups: cardiometabolic (e.g., diabetes, hyperlipidemia, and metabolic syndrome) and non-cardiovascular (e.g., seizures, asthma, and cancer). Additionally, we looked at comorbid substance use (e.g., smoking and drug dependence). RESULTS: We found that 96.3% of participants had at least one other medical comorbidity. Older age predicted a greater likelihood of having a cardiometabolic condition. Early age of onset of bipolar symptoms was associated with a lower chance of having a cardiometabolic condition, but a greater chance of having other types of medical comorbidity. Additional predictors of other medical comorbidities in bipolar disorder included more time spent depressed, less time spent manic/hypomanic, and longer duration of illness. Medications associated with weight gain were associated with low high-density lipoprotein and abnormal triglycerides. CONCLUSIONS: There appears to be a substantial medical burden associated with bipolar disorder, highlighting the need for collaborative care among psychiatric and general medical providers to address both psychiatric and other medical needs concomitantly in this group of patients.

Psychotherapy use in bipolar disorder: Association with functioning and illness severity.

OBJECTIVE: This study examines characteristics of individuals with bipolar disorder who sought psychotherapy versus those who did not. METHODS: Bipolar CHOICE was an 11-site comparative effectiveness study of lithium versus quetiapine in symptomatic outpatients (N = 482) with bipolar disorder. At baseline, participants' psychotherapy use within the past 3 months, mood, functioning, and overall health were assessed. Logistic regressions were used to test whether psychotherapy users and non-users differed on various demographic and clinical variables at baseline. Mixed-effects regression was used to determine whether psychotherapy groups differed on response to treatment over the 6-month study. Kaplan-Meier plots and log-rank tests were employed to test whether there were any differences in time to recovery (CGI-BP ≤ 2 for at least 8 weeks) between the groups. RESULTS: Thirty one percent of participants reported using psychotherapy services. Psychotherapy users reported greater medication side effect burden than non-users and were more likely to have moderate to high suicide risk and at least one anxiety disorder. Participants not utilizing medications or psychotherapy had greater mania symptom severity, were younger, and less educated than medication only users. Medication only users were more likely to be married than the other participants. CONCLUSIONS: These data suggest that a minority of individuals with bipolar disorder attend psychotherapy services, and those that do have greater illness burden.