Search

INTRODUCTION: The Clinical Global Impressions-Improvement (CGI-I) scale is widely used in clinical research to assess symptoms and functioning in the context of treatment. The correlates of the CGI-I with efficacy scales for adolescent major depressive disorder are poorly understood. This study focused on benchmarking CGI-I scores with changes in the Children's Depression Rating Scale-Revised (CDRS-R) and the Quick Inventory of Depressive Symptomatology-Adolescent (17-item) Self-Report (QIDS-A17-SR). METHODS: We examined three datasets with the clinician-rated CDRS-R to ascertain equivalent percent changes in total scores and CGI-I ratings. Exploratory analyses examined corresponding percentage changes in the QIDS-A17-SR and the CGI-I ratings. The CGI-I was the reference scale for nonparametric equipercentile linking with the Equate package in R. RESULTS: CGI-I scores of 1 mapped to ≥78%–95% change in CDRS-R scores at 4–6 weeks across three datasets. CGI-I scores of 2 mapped to 56%–94% change in CDRS-R scores at 4–6 weeks across three studies. CGI-I scores of 3 mapped to 30%–68% changes in CDRS-R scores at 4–6 weeks across three studies. CGI-I scores of 4 mapped to a range of 29%–44% at 4–6 weeks across three studies. There was no significant difference (p ≥ 0.6) between treatment groups in both the Treatment of Adolescents with Depression and Treatment of Resistant Depression in Adolescents studies, for each CGI-I score ( = 1, or = 2 or = 3, or ≥4), associated mapping of total depression severity score, or associated percent change from baseline for corresponding follow-up visits. There was no significant sex difference (p > 0.2) in CGI-I linkages to CDRS-R total or percentage changes. CONCLUSIONS: These findings establish clear relationships among CGI-I scores and the CDRS-R and the QIDS-A17-SR. These benchmarks have utility for clinical trial study design, inter-rater reliability training, and clinical implementation.

This article provides an updated review of the pharmacological profile and available efficacy and tolerability/safety data for vilazodone, one of the most recent antidepressant drugs to be approved in the USA for the treatment of major depressive disorder (MDD) in adults. The efficacy of vilazodone for MDD in adults is supported by four positive short-term (8-10 weeks), randomized, placebo-controlled trials. Beyond these pivotal trials, we review updated research findings pertaining to the clinical effects of vilazodone for MDD including the results of switch studies, small comparative efficacy trials, key pooled and secondary data analyses focused on important depressive subtypes (anxious depression) and predictors of treatment outcome, and safety studies including direct studies of sexual side-effects. Despite these additional research efforts and use for over a decade, important gaps in the clinical evidence base remain with vilazodone. Hypothesized differences in efficacy and adverse effects between other antidepressants and vilazodone based on its multimodal mechanism of action (combining serotonin reuptake inhibition with serotonin 5-HT1A partial agonist effects) have not been comprehensively demonstrated in clinical studies and its effectiveness as a continuation- or maintenance-phase therapeutic is not yet established. Questions remain regarding its reproductive and lactational safety profiles and its efficacy as a potential next-step therapeutic for patients with MDD who do not respond to first-line antidepressants such as selective serotonin reuptake inhibitors. Suggestions for clinical use of vilazodone and discussion of its place among the broad range of pharmacotherapies for adults with MDD are provided.

Objectives To compare the agreement between percentile ranks from 4 multi-morbidity scores. Design Population-based descriptive study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). Results We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. Conclusions The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.

Bipolar disorder often follows a set progression best described in stages where advanced stages are associated with poorer outcomes. Bipolar disorder is also often characterized by a predominance of episode polarity, where some individuals experience more depressive episodes (termed predominant depressive polarity) while others experience more hypo/manic episodes (termed predominant hypo/manic polarity). We examined the associations between staging and predominant polarity with measures of illness burden and treatment outcome utilizing data from a six-month comparative effectiveness trial of lithium and quetiapine in bipolar disorder (Bipolar CHOICE). We used number of self-reported lifetime mood (depressive and hypo/manic) episodes as a proxy for staging and ratio of depressive to manic episodes to define predominant polarity. Polarity and staging were correlated with several measures of burden of illness. Childhood abuse was correlated with more lifetime mood episodes, while more depressive episodes and depressive polarity were correlated with more anxiety disorder comorbidity. Depressive polarity was also correlated with more past trials of psychotropics, particularly antidepressants. However, neither staging nor predominant polarity moderated the randomized treatment effect of lithium vs. quetiapine. Number of depressive episodes in the past year was identified as a potential predictor of overall worse treatment outcome, regardless of medication condition. In conclusion, though staging and predominant episode polarity correlated with several measures of illness burden, they were not associated with differential treatment outcomes. This could be because many of our patients presented for treatment at advanced stages of illness and further highlights the need for early intervention in bipolar disorder.

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (

Purpose To examine the prevalence of, and factors associated with, atypical antipsychotic use among U.S. pregnant women, and potential associations between early pregnancy atypical antipsychotic use and risk for 14 birth defects. Methods We analyzed data from the National Birth Defects Prevention Study (1997–2011), a U.S. population-based case-control study examining risk factors for major structural birth defects. Results Atypical antipsychotic use during pregnancy was more common among women with pre-pregnancy obesity, and women who reported illicit drug use before and during pregnancy, smoking during pregnancy, alcohol use during pregnancy, or use of other psychiatric medications during pregnancy. We observed elevated associations (defined as a crude odds ratio [cOR] ≥2.0) between early pregnancy atypical antipsychotic use and conotruncal heart defects (6 exposed cases; cOR: 2.3, 95% confidence interval [CI]: 0.9–6.1), and more specifically Tetralogy of Fallot (3 exposed cases; cOR: 2.5, 95% CI: 0.7–8.8), cleft palate (4 exposed cases, cOR: 2.5, 95% CI: 0.8–7.6), anorectal atresia/stenosis (3 exposed cases, cOR: 2.8, 95% CI: 0.8–9.9), and gastroschisis (3 exposed cases, cOR: 2.1, 95% CI: 0.6–7.3). Conclusions Our findings support the close clinical monitoring of pregnant women using atypical antipsychotics. Women treated with atypical antipsychotics generally access healthcare services before pregnancy; efforts to reduce correlates of atypical antipsychotic use might improve maternal and infant health in this population.

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.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.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.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.

BACKGROUND: Due to invasive treatments and stressors related to heart health, adults with congenital heart defects (CHDs) may have an increased risk of post-traumatic stress disorder (PTSD), anxiety, and/or depressive disorders. Our objectives were to estimate the prevalence of these disorders among individuals with CHDs. METHODS: Using IBM(®) MarketScan(®) Databases, we identified adults age 18–49 years with ≥2 outpatient anxiety/depressive disorder claims on separate dates or ≥1 inpatient anxiety/depressive disorder claim in 2017. CHDs were defined as ≥2 outpatient CHD claims ≥30 days apart or ≥1 inpatient CHD claim documented in 2007–2017. We used log-binomial regression to estimate adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) for associations between CHDs and anxiety/depressive disorders. RESULTS: Of 13,807 adults with CHDs, 12.4% were diagnosed with an anxiety or depressive disorder. Adults with CHDs, compared to the 5,408,094 without CHDs, had higher prevalence of PTSD (0.8% vs. 0.5%; aPR: 1.5 [CI: 1.2–1.8]), anxiety disorders (9.9% vs. 7.5%; aPR: 1.3 [CI: 1.3–1.4]), and depressive disorders (6.3% vs. 4.9%; aPR: 1.3 [CI: 1.2–1.4]). Among individuals with CHDs, female sex (aPR range: 1.6–3.3) and inpatient admission (aPR range 1.1–1.9) were associated with anxiety/depressive disorders. CONCLUSION: Over 1 in 8 adults with CHDs had diagnosed PTSD and/or other anxiety/depressive disorders, 30–50% higher than adults without CHDs. PTSD was rare, but three times more common in women with CHDs than men. Screening and referral for services for these conditions in people with CHDs may be beneficial.

Objective: Relative age refers to a difference in age between peers in the same academic year. Although younger age of alcohol use is associated with a higher risk of lifetime problematic alcohol use, the potential effects of relative age are poorly understood. We hypothesized that a younger relative age would be associated with a younger chronological age of testing positive for alcohol in a medical setting. Methods: Problematic alcohol use was operationalized and identified as a positive alcohol test (PAT) in a medical setting. This was a retrospective population study of all 12 to 18-year-old residents (n = 4610) of Olmsted County, Minnesota (USA), who were tested for alcohol in a medical care setting from 1998 through 2016. Cox regression models examined the relationship between relative age and the age at testing positive for alcohol. Results: Relative age was not associated with age at first PAT. Results remained nonsignificant after stratifying by gender, and after adjusting for race, number of nonalcohol-related psychiatric comorbidities, and type of alcohol testing. Conclusions: The results did not support a relative age effect as a risk factor for alcohol use in adolescents in Olmsted County, Minnesota. These results contrast with findings from previous studies on this topic, which suggested older relative age increases risk of alcohol use in adolescence.

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS’ and CO-MED’s escitalopram/citalopram patients predicted response in CO-MED’s combination pharmacotherapy patients with accuracies of 76.6% (p p p p

The efficacy of subanesthetic intravenous ketamine for treatment resistant depression (TRD) has spurred a growth of clinics nationwide that provide this service. Ketamine is an FDA-approved drug as an anesthetic but remains unapproved for psychiatric indications, and this status raises a number of short- and long-term safety and efficacy concerns that need to be addressed when implementing and developing this type of clinic. Using a framework of systems, provider, and patient domains, we provide a review of the key challenges in providing ketamine infusions and suggest potential approaches. Under systems issues, we highlight broad stakeholder engagement involving cross-departmental and multidisciplinary considerations, business case development, and delineation of administrative standard operating procedures. In the provider domain, we highlight specific roles for different treatment team members as well as suggested training requirements. In the patient domain, we identify a variety of standard operating procedures involving initial patient assessment parameters, ketamine dosing and administration guidelines, and safety monitoring procedures. Together, this review provides key considerations for developing a ketamine clinic for depression, in an effort to meet the pressing demand for this novel treatment option while helping to ensure its safe implementation.

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1,ERICH3,AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

Objective To assess the validity of the US Department of Health and Human Services (DHHS) definition of multimorbidity using International Classification of Diseases, ninth edition (ICD-9) codes from administrative data. Design Cross-sectional comparison of two ICD-9 billing code algorithms to data abstracted from medical records. Setting Olmsted County, Minnesota, USA. Participants An age-stratified and sex-stratified random sample of 1509 persons ages 40–84 years old residing in Olmsted County on 31 December 2010. Study measures Seventeen chronic conditions identified by the US DHHS as important in studies of multimorbidity were identified through medical record review of each participant between 2006 and 2010. ICD-9 administrative billing codes corresponding to the 17 conditions were extracted using the Rochester Epidemiology Project records-linkage system. Persons were classified as having each condition using two algorithms: at least one code or at least two codes separated by more than 30 days. We compared the ICD-9 code algorithms with the diagnoses obtained through medical record review to identify persons with multimorbidity (defined as ≥2, ≥3 or ≥4 chronic conditions). Results Use of a single code to define each of the 17 chronic conditions resulted in sensitivity and positive predictive values (PPV) ≥70%, and in specificity and negative predictive values (NPV) ≥70% for identifying multimorbidity in the overall study population. PPV and sensitivity were highest in persons 65–84 years of age, whereas NPV and specificity were highest in persons 40–64 years. The results varied by condition, and by age and sex. The use of at least two codes reduced sensitivity, but increased specificity. Conclusions The use of a single code to identify each of the 17 chronic conditions may be a simple and valid method to identify persons who meet the DHHS definition of multimorbidity in populations with similar demographic, socioeconomic, and health care characteristics.

ObjectivesTo describe the percentile distribution of multimorbidity across age by sex, race and ethnicity, and to demonstrate the utility of multimorbidity percentiles to predict mortality.DesignPopulation-based descriptive study and cohort study.SettingOlmsted County, Minnesota (USA).ParticipantsWe used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years).MethodsFor each person, we obtained the count of chronic conditions (out of 20 conditions) present on each birthday by extracting all of the diagnostic codes received in the 5 years before the index birthday from the electronic indexes of the REP. To compare each person’s count to peers of same age, the counts were transformed into percentiles of the total population and displayed graphically across age by sex, race and ethnicity. In addition, quintiles 1, 2, 4 and 5 were compared with quintile 3 (reference) to predict the risk of death at 1 year, 5 years and through end of follow-up using time-to-event analyses. Follow-up was passive using the REP.ResultsWe identified 238 010 persons who experienced a total of 1 458 094 birthdays during the study period (median of 6 birthdays per person; IQR 3–10). The percentiles of multimorbidity across age did not vary noticeably by sex, race or ethnicity. In general, there was an increased risk of mortality at 1 and 5 years for quintiles 4 and 5 of multimorbidity. The risk of mortality for quintile 5 was greater for younger age groups and for women.ConclusionsThe assignment of multimorbidity percentiles to persons in a population may be a simple and intuitive tool to assess relative health status, and to predict short-term mortality, especially in younger persons and in women.

Objective: To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs). Methods: This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions. Results: 2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions. Conclusion: These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

Longitudinal associations between comorbid depression and anxiety with the accumulation of chronic illnesses are unclear, and questions remain about the contributions associated with each condition in the increasing prevalence of multimorbidity.To compare the risk and rate of accumulating chronic conditions in people with depression, anxiety, and comorbid depression and anxiety vs individuals with neither depression nor anxiety.This cohort study used the Rochester Epidemiology Project medical records-linkage system to identify residents of Olmsted County, Minnesota, from January 1, 2005, to December 31, 2014, with follow-up ending December 31, 2017. The sample was divided into cohorts anchored at birthday ages of 20, 40, and 60 years. Individuals were classified at anchoring birthday age as having depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group), using electronically extracted diagnosis codes from the International Classification of Diseases, Ninth Revision (ICD-9) in the 5 years before each anchoring birthday. Data were analyzed from August 2020 through November 2021.Depression alone, anxiety alone, comorbid depression and anxiety, or neither depression nor anxiety (reference group).The main outcome was sex-specific risk, calculated as hazard ratios (HRs) and rates of accumulation, calculated as mean annual incidence rates per 100 person-years, of 15 common chronic conditions within each birthday age cohort through the end of study.Among the 40 360 individuals included across all 3 age cohorts, 21 516 (53.3%) were women. After balancing cohorts on race, Hispanic ethnicity, education level, body mass index, smoking status, and calendar year at index birthday, the risk of accumulating chronic conditions was significantly increased among women with depression alone (cohort aged 20 years: HR, 1.20 [95% CI, 1.02-1.42]; cohort aged 40 years: HR, 1.20 [95% CI, 1.10-1.31]; cohort aged 60 years: HR, 1.09 [95% CI, 1.02-1.16]) and women with comorbid depression and anxiety (cohort aged 20 years: HR, 1.60 [95% CI, 1.28-1.99]; cohort aged 40 years: HR, 1.41 [95% CI, 1.21-1.65]; cohort aged 60 years: HR, 1.29 [95% CI, 1.15-1.44]) compared with referent women in the same birthday cohorts and in men with comorbid depression and anxiety compared with referent men in the cohort aged 20 years (HR, 1.77 [95% CI, 1.08-2.91]). For women, the rates of accumulation of conditions were significantly higher across birthday cohorts in the comorbid depression and anxiety group compared with the depression alone group (eg, cohort aged 20 years: difference, 1.2 [95% CI, 0.2-2.1] per 100 person-years) and reference group (eg, cohort aged 20 years: difference, 1.7 [95% CI, 0.9-2.6] per 100 person-years). For men, compared with the reference group, the rates of accumulation of conditions were significantly higher in men with comorbid depression and anxiety in the cohort aged 20 years (difference, 1.4 [95% CI, 0.1-2.6] per 100 person-years) and in men with depression in the cohort aged 40 years (difference, 2.0 [95% CI, 0.8-3.2] per 100 person-years).In this cohort study, the risk of accumulating chronic conditions was increased with depression and comorbid depression and anxiety in women across the age span and in younger men with comorbid depression and anxiety. Compared with women without depression or anxiety, there was a more rapid rate of accumulation of chronic conditions in women with depression and anxiety individually and an even higher rate when depression and anxiety cooccurred.

Numerous short-term randomized trials support the acute-phase efficacy of low-dose intravenous (IV) ketamine for patients with treatment-resistant unipolar or bipolar depression. Ketamine's antidepressive effects generally have limited duration, highlighting the need for maintenance treatment after an acute-phase response. It is increasingly likely that psychiatrists will be called upon to manage the care of patients with treatment-resistant unipolar or bipolar depression who have responded acutely to ketamine and to recommend or initiate next-step treatments. However, there is a paucity of controlled evidence to guide best practices for managing treatment of patients with treatment-resistant unipolar or bipolar depression who have had a positive initial response to ketamine. This article reviews the available evidence supporting specific strategies for extending and maintaining acute antidepressive responses to low-dose IV ketamine in patients with treatment-resistant unipolar or bipolar depression and provides some preliminary considerations for clinical practice.

Aims Decades of research show that people with schizophrenia have an increased risk of death from cancer; however, the relationship between schizophrenia and cancer incidence remains less clear. This population-based study investigates the incidence of seven common types of cancer among people with a hospital diagnosis of schizophrenia and accounting for the effects of age, sex and calendar time. Methods This population-based study used 1990–2013 data from three nationwide Swedish registries to calculate the incidence (in total, by age group and by sex) of any cancer and of lung, oesophageal, pancreatic, stomach, colon, (in men) prostate and (in women) breast cancer in 111 306 people with a hospital diagnosis of schizophrenia. The incidence in people with diagnosed schizophrenia was compared with the incidence in the general population. Risk estimates accounted for the effects of calendar time. Results In 1 424 829 person-years of follow-up, schizophrenia did not confer an overall higher cancer risk (IRR 1.02, 95% CI 0.91–1.13) but was associated with a higher risk for female breast (IRR 1.19, 95% CI 1.12–1.26), lung (IRR 1.42, 95% CI 1.28–1.58), oesophageal (IRR 1.25, 95% CI 1.07–1.46) and pancreatic (IRR 1.10, 95% CI 1.01–1.21) and a lower risk of prostate (IRR 0.66, 95% CI 0.55–0.79) cancer. Some age- and sex-specific differences in risk were observed. Conclusions People with schizophrenia do not have a higher overall incidence of cancer than people in the general population. However, there are significant differences in the risk of specific cancer types overall and by sex calling for efforts to develop disease-specific prevention programmes. In people with schizophrenia, higher risk generally occurs in those

Introduction: Our goal was to describe the pattern and identify risk factors of early-return ED visits orinpatient admissions following an index mental health and substance abuse (MHSA)-related ED visit in theUnited States. Methods: We performed a retrospective cohort study using Optum Labs Data Warehouse, a nationallyrepresentative database containing administrative claims data on privately insured and MedicareAdvantage enrollees. Authors identified patients presenting to an ED with a primary diagnosis of MHSAbetween 2005 and 2013 who were discharged home. Study inclusion required continuous insuranceenrollment for the 12 months preceding and the 31 days following the index ED visit. During the studyperiod we included only the first ED visit for each patient. Results: A total of 49,672 (14.2%) had a return visit to the ED or had a hospitalization within 30 daysfollowing discharge. Mean time to the next ED visit or inpatient admission was 11.7 days. An increasedage (age 65+ vs. age

AimsPeople who have schizophrenia die earlier from somatic diseases than do people in the general population, but information about cardiovascular deaths in people who have schizophrenia is limited. We analysed mortality in all age groups of people with schizophrenia by specific cardiovascular diseases (CVDs), focusing on five CVD diagnoses: coronary heart disease, acute myocardial infarction, cerebrovascular disease, heart failure and cardiac arrhythmias. We also compared hospital admissions for CVDs in people who had schizophrenia with hospital admissions for CVDs in the general population.MethodsThis national register study of 10 631 817 people in Sweden included 46 911 people who were admitted to the hospital for schizophrenia between 1 January 1987 and 31 December 2010. Information from national registers was used to identify people who had schizophrenia and obtain data about mortality, causes of death, medical diagnoses and hospitalisations.ResultsCVDs were the leading cause of death in people who had schizophrenia (5245 deaths), and CVDs caused more excess deaths than suicide. The mean age of CVD death was 10 years lower for people who had schizophrenia (70.5 years) than the general population (80.7 years). The mortality rate ratio (MRR) for CVDs in all people who had schizophrenia was 2.80 (95% confidence interval (CI) 2.73–2.88). In people aged 15–59 years who had schizophrenia, the MRR for CVDs was 6.16 (95% CI 5.79–6.54). In all people who had schizophrenia, the MRR for coronary heart disease was 2.83 (95% CI 2.73–2.94); acute myocardial infarction, 2.62 (95% CI 2.49–2.75); cerebrovascular disease, 2.4 (95% CI 2.25–2.55); heart failure, 3.25 (95% CI 2.94–3.6); and cardiac arrhythmias, 2.06 (95% CI 1.75–2.43). Hospital admissions for coronary heart disease were less frequent in people who had schizophrenia than in the general population (admission rate ratio, 0.88 (95% CI 0.83–0.94). In all age groups, survival after hospital admission for CVD was lower in people who had schizophrenia than in the general population.ConclusionsPeople who had schizophrenia died 10 years earlier from CVDs than did people in the general population. For all five CVD diagnoses, mortality risk was higher for those with schizophrenia than those in the general population. Survival after hospitalisation for CVDs in people who had schizophrenia was comparable with that of people in the general population who were several decades older.

ObjectivesSleep 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.MethodThis 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.ResultsThe 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 resultsA 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.

OBJECTIVE We studied eight mental health conditions diagnosed before bilateral oophorectomy performed for nonmalignant indications. METHODS We identified 1,653 premenopausal women who underwent bilateral oophorectomy for a nonmalignant indication in Olmsted County, Minnesota, during a 20-year period (1988-2007). Each woman was matched by age (±1 year) to one population-based control who had not undergone bilateral oophorectomy before the index date (age range: 21-49 years). Both cases and controls were identified using the records-linkage system of the Rochester Epidemiology Project (REP http://www.rochesterproject.org). For eight mental health conditions, we calculated odds ratios (ORs) and their 95% confidence intervals (95% CIs) adjusted for race, education, and income using conditional logistic regression. RESULTS Pre-existing mood disorders, anxiety disorders, and somatoform disorders were associated with increased risk of bilateral oophorectomy in overall analyses. These associations were also significant in women ≤45 years of age at index date. Personality disorders were associated with increased risk only in overall analyses and adjustment disorders only in women 46 to 49 years of age. Some of the associations were significantly different across strata by age at index date and by indication. There was also a linear trend of increasing adjusted ORs from 1.55 (95% CI 1.31-1.83) for one mental health condition to 2.19 (95% CI 1.40-3.41) for three or more conditions (trend P

Background Complex polypharmacy (CP) is common in bipolar disorder (BD). We assessed the associations between CP, adherence, and side effect burden, and patient traits associated with clinical improvement in relationship to CP. Methods We conducted a secondary analysis of 482 adult BD participants in the Bipolar CHOICE trial. We examined the associations between CP (use of ≥3 BD medications) and non-adherence (missing >30% of BD medication doses in the last 30 days) and side effect burden (Frequency, Intensity and Burden of Side Effects Rating scale) using multivariate models with patient random effects. We used logistic regression to assess the patient traits associated with remission among those with majority CP use (Clinical Global Impression-Severity for BD score ≤2 for 8+ weeks). Results 43% of patients had any CP and 25% had CP for the majority of the study. CP was associated with non-adherence (OR = 2.51, 95% CI [1.81, 3.50]), but not worse side effect burden. Among those with CP, 16% achieved remission; those with non-adherence, comorbid social or generalized anxiety disorder, or BD I vs. II were less likely to achieve remission among those with CP. Limitations There could be unmeasured confounding between use of CP and side effect burden or adherence. Adherence was measured by self-report, which could be subject to reporting error. Conclusions BD patients with CP were less likely to adhere to therapy, and those with worse adherence to CP were less likely to clinically respond. Clinicians should assess medication adherence prior to adding another agent to medication regimens.

BackgroundAcylcarnitines have important functions in mitochondrial energetics and β-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD)—(core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+))—following treatment with a selective serotonin reuptake inhibitor (SSRI).MethodsDepressed outpatients (n=240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and eight weeks post-treatment were profiled for multiple-, short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ®p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminate the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differ between groups.ResultsAt baseline, significantly lower concentrations of short- and long-chain acylcarnitines were found in CD+ and NVSM+ compared to ANX+, and the short-chain acylcarnitines remained lower after eight weeks. At eight weeks, the medium- and long-chain acylcarnitines were significantly lower in NVSM+ compared to ANX+. Regarding changes baseline to week eight, short-chain acylcarnitine levels significantly increased in CD+ and ANX+, and medium- and long-chain acylcarnitines significantly decreased in NVSM+ and CD+.ConclusionsIn depressed patients treated with SSRIs, β-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.

ObjectiveTo assess the validity of the US Department of Health and Human Services (DHHS) definition of multimorbidity using International Classification of Diseases, ninth edition (ICD-9) codes from administrative data.DesignCross-sectional comparison of two ICD-9 billing code algorithms to data abstracted from medical records.SettingOlmsted County, Minnesota, USA.ParticipantsAn age-stratified and sex-stratified random sample of 1509 persons ages 40–84 years old residing in Olmsted County on 31 December 2010.Study measuresSeventeen chronic conditions identified by the US DHHS as important in studies of multimorbidity were identified through medical record review of each participant between 2006 and 2010. ICD-9 administrative billing codes corresponding to the 17 conditions were extracted using the Rochester Epidemiology Project records-linkage system. Persons were classified as having each condition using two algorithms: at least one code or at least two codes separated by more than 30 days. We compared the ICD-9 code algorithms with the diagnoses obtained through medical record review to identify persons with multimorbidity (defined as ≥2, ≥3 or ≥4 chronic conditions).ResultsUse of a single code to define each of the 17 chronic conditions resulted in sensitivity and positive predictive values (PPV) ≥70%, and in specificity and negative predictive values (NPV) ≥70% for identifying multimorbidity in the overall study population. PPV and sensitivity were highest in persons 65–84 years of age, whereas NPV and specificity were highest in persons 40–64 years. The results varied by condition, and by age and sex. The use of at least two codes reduced sensitivity, but increased specificity.ConclusionsThe use of a single code to identify each of the 17 chronic conditions may be a simple and valid method to identify persons who meet the DHHS definition of multimorbidity in populations with similar demographic, socioeconomic, and health care characteristics.

Objectives To estimate the risk of fatal and non-fatal myocardial infarction (MI) and stroke in patients with bipolar I disorder compared to people without bipolar I disorder. Method Utilizing a records-linkage system spanning 30 years (1966–1996), a population-based cohort of 334 subjects with bipolar I disorder and 334 age and sex-matched referents from Olmsted County, Minnesota, U.S. was identified. Longitudinal follow-up continued until incident MI or stroke (confirmed by board-certified cardiologist/neurologist), death, or study end date (December 31, 2013). Cox proportional hazards models assessed the hazard ratio (HR) for MI or stroke, adjusting for potential confounders. Results There was an increased risk of fatal or non-fatal MI or stroke (as a composite outcome) in patients with bipolar I disorder [HR 1.54, 95% confidence interval (CI) 1.02, 2.33; p=0.04]. However, after adjusting for baseline cardiovascular risk factors (alcoholism, hypertension, diabetes, and smoking), the risk was no longer significantly increased (HR 1.19, 95% CI 0.76, 1.86; p =0.46). Limitations Small sample size for the study design. Findings were not retained after adjustment for cardiovascular disease risk factors. Psychotropic medication use during the follow-up was not ascertained and was not included in the analyses. Conclusion This study in a geographically defined region in the U.S. demonstrated a significant increased risk of MI or stroke in bipolar I disorder, which was no longer significant after adjustment for cardiovascular risk factors.

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.

Background The coexistence of chronic mental health conditions with somatic conditions (somatic-mental multimorbidity, or SMM) is common and has been associated with greater symptom burden and functional impairment, higher costs, and excess mortality. However, most existing literature focused on the co-occurrence of an index mental health condition with specific additional conditions. By contrast, we studied the prevalence and patterns of SMM more broadly considering 19 selected conditions, and we focused on differences by age, sex, and race/ethnicity. Methods The Rochester Epidemiology Project (REP) records-linkage system was used to identify all residents of Olmsted County, MN, on April 1, 2010. We identified individuals with each of 19 common chronic conditions, including 5 mental health conditions, using the International Classification of Diseases, ninth revision (ICD-9) codes received from any health care provider between April 1, 2005 and March 31, 2010. Results Among the 138,858 residents of the county, 52.4% were women, and 7.9% had SMM. SMM increased steeply with older age, was 1.7 times more common in women, and was lower in Asians compared with whites. Of the 10,903 persons with SMM, 7,739 (71.0%) were younger than 65 years. Depressive and anxiety disorders were the most common conditions involved in SMM. The dyads that were observed more frequently or less frequently than expected by chance varied in composition by age and sex. Conclusions SMM that reaches medical attention is highly prevalent across all age groups, is more frequent in women, is less frequent in Asians, and encompasses a wide range of conditions.

Background Few brief, self-report measures exist that can reliably predict adverse suicidality outcomes in patients with BD. This study utilized the Concise Health Risk Tracking Self-Report (CHRT) to assess suicidality in patients with BD and examined its psychometric performance, clinical correlates, and prospective value in predicting adverse events related to suicidality. Methods The CHRT was administered at baseline and follow-up to 482 adult patients in Bipolar CHOICE, a 6-month randomized comparative effectiveness trial. The Columbia Suicide Severity Rating Scale (CSSRS) was used at baseline to assess lifetime history of suicide attempts and related behaviors. Clinician-rated measures of mood (Bipolar Inventory of Symptoms Scale) and bipolar symptoms (Clinical Global Impressions-Bipolar Version) were conducted at baseline and follow-up. Results The CHRT showed excellent internal consistency and construct validity and was highly correlated with clinician ratings of depression, anxiety, and overall functioning at baseline and throughout the study. Baseline CHRT scores significantly predicted risk of subsequent suicidality-related Serious Adverse Events (sSAEs), after controlling for mood and comorbidity. Specifically, the hazard of a sSAE increased by 76% for every 10-point increase in baseline CHRT score. Past history of suicide attempts and related behaviors, as assessed by the CSSRS, did not predict subsequent sSAEs. Limitations The CSSRS was used to assess static risk factors in terms of past suicidal behaviors and may have been a more powerful predictor over longer-term follow-up. Conclusions The CHRT offers a quick and robust self-report tool for assessing suicidal risk and has important implications for future research and clinical practice.

Millions of patients suffer from major depressive disorder (MDD), but many do not respond to selective serotonin reuptake inhibitor (SSRI) therapy. We used a pharmacometabolomics-informed pharmacogenomics research strategy to identify genes associated with metabolites that were related to SSRI response. Specifically, 306 MDD patients were treated with citalopram or escitalopram and blood was drawn at baseline, 4 and 8 weeks for blood drug levels, genome-wide single nucleotide polymorphism (SNP) genotyping and metabolomic analyses. SSRI treatment decreased plasma serotonin concentrations (P

INTRODUCTION: Depressive episodes are often prevalent among patients with bipolar disorder, but little is known regarding the differential patterns of development over time. We aimed to determine and characterize trajectories of depressive symptoms among adults with bipolar disorder during 6 months of systematic treatment.METHODS: The pragmatic clinical trial, Bipolar Clinical Health Outcomes Initiative in Comparative Effectiveness (CHOICE), randomized 482 outpatients with bipolar disorder to lithium or quetiapine. Depressive symptoms were rated at up to 9 visits using the Montgomery-Asberg Depression Rating Scale (MADRS). Growth mixture modeling was utilized to identify trajectories and multinomial regression analysis estimated associations with potential predictors.RESULTS: Four distinct trajectories of depressive symptoms were identified. The responding class (60.3%) with a rapid reduction and subsequent low level; the partial-responding class (18.4%) with an initial reduction followed by an increase during the remaining weeks; the fluctuating class (11.6%) with a fluctuation in depressive symptoms; and the non-responding class (9.7%) with sustained moderate-severe depressive symptoms. Bipolar type I predicted membership of the non-responding class and randomization to quetiapine predicted membership of either the responding or the non-responding class.CONCLUSION: Approximately 30% experienced a partial or fluctuating course, and almost 10% had a chronic course with moderate-severe depression during 6 months. Patients diagnosed with bipolar type 1 had higher risk of being categorized into a class with a worse outcome. While no differences in average overall outcomes occurred between the lithium and quetiapine groups, trajectory analysis revealed that the lithium group had more variable courses.

There is a new appreciation of the perimenopause-defined as the early and late menopause transition stages as well as the early postmenopause-as a window of vulnerability for the development of both depressive symptoms and major depressive episodes. However, clinical recommendations on how to identify, characterize and treat clinical depression are lacking. To address this gap, an expert panel was convened to systematically review the published literature and develop guidelines on the evaluation and management of perimenopausal depression. The areas addressed included: (1) epidemiology; (2) clinical presentation; (3) therapeutic effects of antidepressants; (4) effects of hormone therapy; and (5) efficacy of other therapies (e.g., psychotherapy, exercise, and natural health products). Overall, evidence generally suggests that most midlife women who experience a major depressive episode during the perimenopause have experienced a prior episode of depression. Midlife depression presents with classic depressive symptoms commonly in combination with menopause symptoms (i.e., vasomotor symptoms, sleep disturbance), and psychosocial challenges. Menopause symptoms complicate, co-occur, and overlap with the presentation of depression. Diagnosis involves identification of menopausal stage, assessment of co-occurring psychiatric and menopause symptoms, appreciation of the psychosocial factors common in midlife, differential diagnoses, and the use of validated screening instruments. Proven therapeutic options for depression (i.e., antidepressants, psychotherapy) are the front-line treatments for perimenopausal depression. Although estrogen therapy is not approved to treat perimenopausal depression, there is evidence that it has antidepressant effects in perimenopausal women, particularly those with concomitant vasomotor symptoms. Data on estrogen plus progestin are sparse and inconclusive.