Comorbidity between mental and physical disorder conditions is the rule rather than the exception. It is estimated that 25% of adult population have mental health condition and 68% of them suffer ...from comorbid medical condition. Readmission rates in psychiatric patients are high and we still lack understanding potential predictors of recidivism. Physical comorbidity could be one of important risk factors for psychiatric readmission. The aim of the present study was to review the impact of physical comorbidity variables on readmission after discharge from psychiatric or general inpatient care among patients with co-occurring psychiatric and medical conditions.
A comprehensive database search from January 1990 to June 2014 was performed in the following bibliographic databases: Ovid Medline, PsycINFO, ProQuest Health Management, OpenGrey and Google Scholar. An integrative research review was conducted on 23 observational studies.
Six studies documented physical comorbidity variables only at admission/discharge and 17 also at readmission. The main body of studies supported the hypothesis that patients with mental disorders are at increased risk of readmission if they had co-occurring medical condition. The impact of physical comorbidity variables on psychiatric readmission was most frequently studied in in patients with affective and substance use disorders (SUD). Most common physical comorbidity variables with higher probability for psychiatric readmission were associated with certain category of psychiatric diagnoses. Chronic lung conditions, hepatitis C virus infection, hypertension and number of medical diagnoses were associated with increased risk of readmission in SUD; Charlson Comorbidity Index, somatic complaints, physical health problems with serious mental illnesses (schizophrenia, schizoaffective disorder, personality disorders); not specified medical illness, somatic complaints, number of medical diagnoses, hyperthyroidism with affective disorders (depression, bipolar disorder). Co-occurring physical and mental disorders can worsen patient's course of illness leading to hospital readmission also due to non-psychiatric reasons.
The association between physical comorbidity and psychiatric readmission is still poorly understood phenomenon. Nevertheless, that physical comorbid conditions are more common among readmitted patients than single admission patients, their association with readmission can vary according to the nature of mental disorders, characteristics of study population, applied concept of comorbidity, and study protocol.
Background
It has been suggested that clinician‐rated scales and self‐report questionnaires may be interchangeable in the measurement of depression severity, but it has not been tested whether ...clinically significant information is lost when assessment is restricted to either clinician‐rated or self‐report instruments. The aim of this study is to test whether self‐report provides information relevant to short‐term treatment outcomes that is not captured by clinician‐rating and vice versa.
Methods
In genome‐based drugs for depression (GENDEP), 811 patients with major depressive disorder treated with escitalopram or nortriptyline were assessed with the clinician‐rated Montgomery–Åsberg Depression Rating Scale (MADRS), Hamilton Rating Scale for Depression (HRSD), and the self‐report Beck Depression Inventory (BDI). In sequenced treatment alternatives to relieve depression (STAR*D), 4,041 patients treated with citalopram were assessed with the clinician‐rated and self‐report versions of the Quick Inventory of Depressive Symptomatology (QIDS‐C and QIDS‐SR) in addition to HRSD.
Results
In GENDEP, baseline BDI significantly predicted outcome on MADRS/HRSD after adjusting for baseline MADRS/HRSD, explaining additional 3 to 4% of variation in the clinician‐rated outcomes (both P < .001). Likewise, each clinician‐rated scale significantly predicted outcome on BDI after adjusting for baseline BDI and explained additional 1% of variance in the self‐reported outcome (both P < .001). The results were confirmed in STAR*D, where self‐report and clinician‐rated versions of the same instrument each uniquely contributed to the prediction of treatment outcome.
Conclusions
Complete assessment of depression should include both clinician‐rated scales and self‐reported measures.
Abstract The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and ...error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5–10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R2 ) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.
Abstract Aim Patients with diabetes differ in compliance to diabetes self-management which influences their long-term health. Psychological factors, namely depression and cognitive abilities, are ...associated with diabetes self-management behavior. The aim of the study was to identify independent association of particular cognitive functions with diabetes self-management. Methods In a cross sectional study 98 adults with type 2 diabetes attending Diabetes Outpatient Clinic were examined using the measures of diabetes self-management (Summary of Diabetes Self-Care Activities (SDSCA) measure), depression (Hamilton Depression Inventory (HDI)), diabetes distress (Problem Areas In Diabetes scale (PAID)), and the neuropsychological battery of tests for assessment of cognitive functions. Sociodemographic and diabetes-related data were collected. Univariate and multivariate regression analyses were used to identify and evaluate the predictors of diabetes self-management. Results Specific cognitive functions, namely immediate memory, visuospatial/constructional abilities, attention, and specific executive functions (planning and problem solving) were significantly associated with diabetes self-management. Among cognitive factors, planning and problem solving abilities were strongest predictors; furthermore, in a multivariate regression their association was independent from depression. Conclusions Specific cognitive abilities, particularly planning and problem solving, play an independent role in diabetes self-management behaviors. Assessing patients’ cognitive abilities may be of value for adjusting self-management education and treatment regimen.
Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict ...who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.
Aims:
Antidepressant response varies between patients, possibly due to differences in the rate cytochrome P450 enzymes metabolise antidepressants into inactive compounds. Drug metabolism rates are ...influenced by common variants in the genes encoding these enzymes. However, it remains unclear whether treatment outcomes can be predicted by either CYP450 genotype or antidepressant serum concentration.
Methods:
In GENDEP (a pharmacogenetic study of depressed individuals treated with either escitalopram or nortriptyline), serum concentrations of antidepressants and their primary metabolite were measured after eight weeks treatment and variants in CYP2D6 and CYP2C19 were genotyped.
Results:
Amongst patients taking escitalopram (n=223), the genotype CYP2C19 was significantly associated with escitalopram serum concentrations and desmethylescitalopram:escitalopram ratio. For those taking nortriptyline (n=161), the CYP2D6 genotype was significantly associated with nortriptyline and 10-hydroxynortriptyline serum concentrations and 10-hydroxynortriptyline:nortrip-tyline ratio. CYP450 genotypes conferring greater enzyme activity were linked to lower drug serum concentrations and higher metabolite:drug ratios. Nonetheless, no significant association was found between either CYP450 genotype or antidepressant serum concentration and treatment response.
Conclusions:
While there is a significant relationship between the CYP450 genotype and serum concentrations of escitalopram and nortriptyline, the genotypes are not predictive of differences in treatment response for either drug. Furthermore, differences in antidepressant serum concentrations are not associated with variability in treatment response.
The objective of the Genome-based Therapeutic Drugs for Depression study is to investigate the function of variations in genes encoding key proteins in serotonin, norepinephrine, neurotrophic and ...glucocorticoid signaling in determining the response to serotonin-reuptake-inhibiting and norepinephrine-reuptake-inhibiting antidepressants. A total of 116 single nucleotide polymorphisms in 10 candidate genes were genotyped in 760 adult patients with moderate-to-severe depression, treated with escitalopram (a serotonin reuptake inhibitor) or nortriptyline (a norepinephrine reuptake inhibitor) for 12 weeks in an open-label part-randomized multicenter study. The effect of genetic variants on change in depressive symptoms was evaluated using mixed linear models. Several variants in a serotonin receptor gene (HTR2A) predicted response to escitalopram with one marker (rs9316233) explaining 1.1% of variance (P=0.0016). Variants in the norepinephrine transporter gene (SLC6A2) predicted response to nortriptyline, and variants in the glucocorticoid receptor gene (NR3C1) predicted response to both antidepressants. Two HTR2A markers remained significant after hypothesis-wide correction for multiple testing. A false discovery rate of 0.106 for the three strongest associations indicated that the multiple findings are unlikely to be false positives. The pattern of associations indicated a degree of specificity with variants in genes encoding proteins in serotonin signaling influencing response to the serotonin-reuptake-inhibiting escitalopram, genes encoding proteins in norepinephrine signaling influencing response to the norepinephrine-reuptake-inhibiting nortriptyline and a common pathway gene influencing response to both antidepressants. The single marker associations explained only a small proportion of variance in response to antidepressants, indicating a need for a multivariate approach to prediction.
Abstract The mechanisms by which antidepressants have their effects are not clear and the reasons for variability in treatment outcomes are also unknown. However, there is evidence from candidate ...gene research that indicates gene expression changes may be involved in antidepressant action. In this study, we examined antidepressant-induced alterations in gene expression on a transcriptome-wide scale, exploring associations with treatment response. Blood samples were taken from a subset of depressed patients from the GENDEP study ( n =136) before and after eight weeks of treatment with either escitalopram or nortriptyline. Transcriptomic data were obtained from these samples using Illumina HumanHT-12 v4 Expression BeadChip microarrays. When analysing individual genes, we observed that changes in the expression of two genes ( MMP28 and KXD1 ) were associated with better response to nortriptyline. Considering connectivity between genes, we identified modules of genes that were highly coexpressed. In the whole sample, changes in one of the ten identified coexpression modules showed significant correlation with treatment response (cor=0.27, p =0.0029). Using transcriptomic approaches, we have identified gene expression correlates of the therapeutic effects of antidepressants, highlighting possible molecular pathways involved in efficacious antidepressant treatment.
Rationale
Cytochrome P450 enzymes are important in the metabolism of antidepressants. The highly polymorphic nature of these enzymes has been linked to variability in antidepressant metabolism rates, ...leading to hope regarding the use of P450 genotyping to guide treatment. However, evidence that P450 genotypic differences underlie the variation in treatment outcomes is inconclusive.
Objectives
We explored the links between both P450 genotype and serum concentrations of antidepressant with antidepressant side effects, using data from the Genome-Based Therapeutic Drugs for Depression Project (GENDEP), which is a large (
n
= 868), pharmacogenetic study of depressed individuals treated with escitalopram or nortriptyline.
Methods
Patients were genotyped for the enzymes CYP2C19 and CYP2D6, and serum concentrations of both antidepressant and primary metabolite were measured after 8 weeks of treatment. Side effects were assessed weekly. We investigated associations between P450 genotypes, serum concentrations of antidepressants and side effects, as well as the relationship between P450 genotype and study discontinuation.
Results
P450 genotype did not predict total side effect burden (nortriptyline:
n
= 251,
p
= 0.5638,
β
= −0.133, standard error (SE) = 0.229; escitalopram:
n
= 340,
p
= 0.9627,
β
= −0.004, SE = 0.085), study discontinuation (nortriptyline
n
= 284, hazard ratio (HR) = 1.300,
p
= 0.174; escitalopram
n
= 376, HR = 0.870,
p
= 0.118) or specific side effects. Serum concentrations of antidepressant were only related to a minority of the specific side effects measured: dry mouth, dizziness and diarrhoea.
Conclusions
In this sample where antidepressant dosage is titrated using clinical judgement, P450 genotypes do not explain differences between patients in side effects with antidepressants. Serum drug concentrations appear to only explain variability in the occurrence of a minority of specific side effects.
When we talk about personality disorders, people usually have a very emotional reaction, with pain and unpleasant emotions being the most common. Those who have a relationship with a person who has a ...personality disorder, usually do not know what they are facing, but they do experience feelings of unease, despair, sadness, anger and depression, while doubting their own experience and perception of the world. Family and friends often feel like they are “caught in a relationship web”, which keeps getting more and more tangled, instead of untangling. Personality disorders cause everyone a lot of suffering, anger and disappointment, manifesting in every dimension of the human experience, but the behavior of a person with a personality disorder and the responses from the environment are actually a lot more predictable than you might imagine. In order to present this demanding and little-known topic to the widest range of readers, the handbook uses stories of everyday people to illustrate how their experiences of personality disorders intertwine with stress, mood disorders and problems with addiction. It also presents the process of identifying individual personality disorders and various options of self-help and recovery. The handbook is intended for anyone who is interested in the field of personality disorders, is personally facing mental health problems, or has a relative who is dealing with these issues.
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