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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.

Severe persistent mental illness (SPMI) is associated with worse outcomes in cancer patients. Less is known about the relationship between SPMI and surgical outcomes after mastectomy for breast cancer.We selected patients with breast cancer and SPMI from the National Inpatient Sample (2016-2018) and used propensity score matching. We then used multivariate analysis, Kruskal-Wallis tests, and conditional logistic regression to compare demographics and outcomes.The study sample consisted of 670 patients: 536 without SPMI and 134 with SPMI. SPMI was associated with bilateral mastectomy (bilateral: 53% vs. unilateral: 42.7%, p = 0.033) and decreased frequency of breast reconstruction (p 0.001). SPMI was associated with more extended hospitalization (4 days vs. 2 days, p 0.001) and increased risk of developing post-procedural infection and sepsis (OR 2.909).SPMI is associated with bilateral mastectomy, more extended hospitalization, and increased risk for post-procedural infection and sepsis - suggesting the need for increased use of standardized screening tools to identify SPMI in patients and inform perioperative management correctly.

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.

The efficacy of antidepressants for patients with major depressive disorder (MDD) varies from individual to individual, making the prediction of therapeutic outcomes difficult. Better methods for predicting antidepressant outcomes are needed. However, complex interactions between biological, psychological, and environmental factors affect outcomes, presenting immense computational challenges for prediction. Using machine learning (ML) techniques with pharmacogenomics data provides one pathway toward individualized prediction of therapeutic outcomes of antidepressants.This report systematically reviews the methods, results, and limitations of individual studies of ML and pharmacogenomics for predicting response and/or remission with antidepressants in patients with MDD. Future directions for research and pragmatic considerations for the clinical implementation of ML-based pharmacogenomic algorithms are also discussed.ML methods utilizing pharmacogenomic and clinical data demonstrate promising results for predicting short-term antidepressant response. However, predictions of antidepressant treatment outcomes depend on contextual factors that ML algorithms may not be able to capture. As such, ML-driven prediction is best viewed as a companion to clinical judgment, not its replacement. Successful implementation and adoption of methods predicting antidepressant response warrants provider education about ML and close collaborations between computing scientists, pharmacogenomic experts, health system engineers, laboratory medicine experts, and clinicians.

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 (

Background Lithium and quetiapine can cause weight gain, but their comparative longer term anthropometric effects are unknown, as are the potential moderating effects of baseline binge-eating (BE) behavior. Methods We assessed 6 month changes in body weight, body mass index (BMI) and waist circumference in 482 adults with DSM-IV bipolar disorders who participated in a comparative effectiveness study of lithium and quetiapine with evidence-based adjunctive treatment (Bipolar CHOICE). Anthropometric measurements were obtained at baseline, and at 2, 4, 6, 8, 12, 16, 20, and 24 weeks. BE behavior was defined as affirmative responses to MINI items M1 and M3 at baseline. Data were analyzed using a mixed model repeated measures approach, adjusted for baseline values of dependent measures. Results On average, body weight and BMI increased over 6 months with lithium and quetiapine. However, those treated with quetiapine experienced greater increases from baseline in body weight (peak change, + 3.6 lbs. vs. + 1.4 lbs.) and BMI (peak change, + 0.6 kg/m2 vs. + 0.3 kg/m2), starting at 2 weeks (group x time, F8,3052= 2.9, p = 0.003 for body weight, F8,3052= 3.0, p = 0.002 for BMI). Significant increases in waist circumference were observed only with quetiapine. The relationship between drug treatment and changes in body weight (group x time x binge eating status, F1,2770= 2.0, p = 0.002), BMI (F1,2767= 2.0, p = 0.002), and waist circumference (women only, F25,1621= 2.9, p Limitations Bipolar CHOICE was not designed to study anthropometric outcomes. Conclusions Greater changes in body weight, BMI, and waist circumference occurred with quetiapine- versus lithium-based treatment over 6 months of treatment. The effects of study drugs on these anthropometric measures were moderated by BE behavior at baseline.

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.

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.

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.

BackgroundKetamine, an N-methyl-d-aspartate receptor antagonist, has been “repurposed” as a rapid-acting antidepressant for treatment-resistant depression (TRD). The s-enantiomer of ketamine, “esketamine,” was FDA approved for TRD and depressive symptoms in adults with major depressive disorder with suicidal ideations/behaviors. Intravenous (IV) ketamine, although financially less expensive, is often not covered by insurance and intranasal (IN) esketamine, although covered by insurance can be expensive. There is a paucity of literature on efficacy data comparing subanesthetic IV ketamine and IN esketamine for TRD in a real-world scenario. Thus, we conducted this study comparing the efficacy and the number of treatments required to achieve remission/response with repeated use of subanesthetic IV ketamine/IN esketamine among TRD patients.MethodsThis was an observational study where we included adults (≥18 years) with TRD who provided consent and had received up to 6 IV ketamine infusions (0.5 mg/kg, infused over 40 minutes) or up to 8 intranasal (IN) esketamine (56/84 mg) treatments for TRD at the Mayo Clinic Depression Center. Depression symptoms were measured utilizing the self-report 16-Item Quick Inventory of Depressive Symptomatology (QIDS-SR) scale before and 24 hours after ketamine/esketamine treatment. Remission and response were defined as QIDS-SR 16 score ≤5 and ≥50% change in QIDS-SR 16, respectively. Continuous variables are reported as means ± SD and categorical variables as counts and percentages. The Wilcoxon rank-sum test was used to compare continuous variables. Chi-square and Fisher’s exact tests were used to compare categorical variables. The number of treatments to remission/response was calculated.ResultsSixty-three adults with TRD, middle-aged (47.0 ± 12.1 years), predominantly female (65%), of which 76% (n = 48) and 24% (n = 15) received IV ketamine and IN esketamine, respectively. Mean (SE) change in QIDS-SR 16 score was −8.7 ± 0.7 (P P > .05). The mean number of treatments received to achieve response (2.5 ± 1.6 vs 4.6 ± 2.1) and remission (2.4 ± 1.3 vs 6.3 ± 2.4) were significantly lower among patients who received IV ketamine compared to IN esketamine (P ConclusionIntravenous ketamine and intranasal esketamine showed similar response/remission in TRD patients but the number of treatments required to achieve response/remission was significantly lower with IV ketamine compared to IN esketamine. These findings need to be investigated in a randomized control trial comparing these two treatment interventions.FundingNo funding

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.

The discovery by Delay and Denicker in 1953 that chlorpromazine was highly effective in alleviating delusions, hallucinations, and disorganized thinking, was the seminal breakthrough in the treatment of schizophrenia, the first agent to produce sufficient relief of core psychotic symptoms to permit life outside of institutions for many patients with schizophrenia, and even a return to a semblance of function within normal limits. Chlorpromazine and the other related typical antipsychotic drugs which were introduced over the next 30 years have proven to be of immense benefit to vast numbers of people who experience psychotic symptoms as a component of a diverse group of neuropsychiatric and medical disorders, as well as drug-induced psychoses. These drugs have been invaluable in providing clues to the aetiology of schizophrenia and other forms of mental illness with psychotic features and as tools in understanding fundamental neural processes, especially those involving dopamine, a key neurotransmitter involved in psychosis. This class of drugs has now been supplanted by the so-called atypical antipsychotic drugs, of which clozapine is the prototype. This chapter will describe the various classes of antipsychotic agents, with emphasis on the atypical antipsychotic drugs, their benefits and adverse effects, recommendations for use in clinical practice, and mechanism of action. The drugs used to treat the extrapyramidal side-effects (EPS) produced mainly by the typical antipsychotic drugs are also considered.

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

Background Item 9 of the Patient Health Questionnaire (PHQ) evaluates passive thoughts of death or self-injury within the last two weeks, and is often used to screen depressed patients for suicide risk. We aimed to validate the PHQ-9 item 9 with a brief electronic version of the Columbia Suicide Severity Rating Scale (eC-SSRS). Methods We analyzed data from 841 patients enrolled in the National Network of Depression Centers Clinical Care Registry. We performed a validation analysis of PHQ-9 item 9 for suicide risk and ideation, using the eC-SSRS as a gold standard (defined as positive response to suicidal ideation with intent to act or recent suicidal behavior). Results Of the 841 patients, 13.4% and 41.1% were assessed as being positive for suicide risk by the eC-SSRS and PHQ-9 item 9, respectively. For the overall cohort, sensitivity was 87.6% (95%CI 80.2–92.5%), specificity was 66.1% (95%CI 62.6–69.4%), PPV was 28.6% (95%CI 24.1–33.6%), and NPV was 97.2% (95%CI 95.3–98.3%) for the PHQ-9 suicide item. These performance measures varied within subgroups defined by demographic and clinical characteristics. In addition, the validity of PHQ-9 item 9 (cutoff score of 1) with eC-SSRS-defined suicide ideation showed overall poor results. Limitations The gold standard used in our study was a surrogate measure of suicidality based on eC-SSRS scores. Conclusions The results of our study suggest that item 9 of the PHQ-9 is an insufficient assessment tool for suicide risk and suicide ideation, with limited utility in certain demographic and clinical subgroups that requires further investigation.

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

Objectives To compare the relative influence (RI) of individual predictors for lifetime attempted suicide between adults with bipolar I (BDBD-I) and bipolar II disorder (BDBD-II). Methods We conducted an analysis of data from 1465 enrollees in the Mayo Clinic Bipolar Disorder Biobank. Demographic and clinical variables and history of attempted suicide were ascertained using standardized questionnaires. Height and weight were assessed to determine body mass index (BMI); obesity was defined as BMI ≥30 kg/m 2 . The frequencies of these variables were compared between persons with and without self-reported lifetime suicide attempts both overall, and within BD-I and BD-II subgroups. Gradient boosting machine (GBM) models were used to quantify the RI of study variables on the risk of lifetime attempted suicide. Results Nearly one-third of patients reported having a lifetime suicide attempt. Attempted suicide rates were higher in patients with BD-I than BD-II, but absolute differences were small. Lifetime attempted suicide was associated with female sex, BD-I subtype, psychiatric and substance use comorbidities, binge eating behavior, lifetime history of rapid cycling, other indicators of adverse illness course, and early age of bipolar illness onset in the entire cohort. Differences in the rank-ordering of RI for predictors of attempted suicide between BD-I and BD-II patients were modest. Rapid cycling was a strong risk factor for attempted suicide, particularly in men with BD-I. Limitations Actively psychotic or suicidal patients needing psychiatric hospitalization were initially excluded, but were approached after these acute psychiatric problems resolved. Conclusions The prevalence of lifetime attempted suicide was significantly higher in BD-I than BD-II in this large, cross-sectional cohort. Predictors of attempted suicide were similar in BD-I and BD-II subgroups.

Background Bipolar patients experience sleep disturbances during and between mood episodes. Yet the impact of sleep on treatment with different medications has not been fully explored. The purpose of this paper is to explore the potential impact of poor sleep at baseline on outcomes in a randomized effectiveness trial of quetiapine and lithium. Methods The Bipolar CHOICE study was a 6-month, parallel group, multisite randomized controlled trial. Participants with bipolar disorder (N = 482; 59% female and age 18–70 years) received quetiapine or lithium. Patients were allowed to also receive adjunctive personalized treatments, which were guideline-informed, empirically-based medications added to treatment as needed. Medication changes were recorded as necessary clinical adjustments (NCA). Fisher's exact tests, mixed-regression models, and Mann-Whitney U tests were used to assess demographic and clinical characteristics as well as whether sleep disturbance would predict outcomes. Results 63% of patients had baseline sleep disturbance. Individuals with sleep disturbance had worse bipolar illness severity, greater severity of depression, mania, anxiety, irritability, and psychosis, were less likely to have sustained response (17% vs. 29%; adjusted RR: 0.55, 95% CI: 0.38–0.78, p = 0.0006) and had more NCAs (median 0.71 vs. 0.59, p = 0.03). Limitations Our findings were limited by how we defined sleep disturbance, and by how severity of sleep disturbance was assessed with one item with a non-sleep specific measure. Conclusions Baseline sleep disturbance was associated with more severe bipolar symptoms and worse 6-month outcomes. Further research is warranted on improving sleep in bipolar disorder, especially the role of psychosocial interventions.