Sociodemographic predictors of response to antidepressant treatment

Citalopram amplifies the influence of living conditions on mood in depressed patients enrolled in the STAR*D study

Selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed antidepressant drugs, have a variable and incomplete efficacy. In order to better understand SSRI action, we explored the hypothesis that SSRIs do not affect mood per se but amplify the influence of the living conditions on mood. To this aim, we exploited the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) data set, selected a subpopulation of 591 patients with an overlapping clinical history and analyzed treatment outcome according to dosage -20 or 40 mg per day of citalopram. We found that sociodemographic characteristics affected treatment response in the same direction in the two dose groups, but these effects reached statistical significance only in the 40 mg per day dose group. In the latter, higher improvement rate was associated with having a working employment status (P=0.0219), longer education (P=0.0053), high income (P=0.01) or a private insurance (P=0.0031), and the higher remission rate was associated with having a working employment status (P=0.0326) or longer education (P=0.0484). Moreover, the magnitude of the effect of the sociodemographic characteristics on mood, measured as the percent of patients showing a positive outcome when exposed to favorable living conditions, was much greater-up to 37-fold-in the 40 compared to the 20 mg per day dose group. Overall, our results indicate that citalopram amplifies the influence of the living conditions on mood in a dose-dependent manner. These findings provide a potential explanation for the variable efficacy of SSRIs and might lead to the development of personalized strategies aimed at enhancing their efficacy.

Specificity profile of venlafaxine and sertraline in major depression: metaregression of double-blind, randomized clinical trials

Despite the well-known efficacy of selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs) in the treatment of major depressive disorder, there is a lack of indications for each drug in different groups of patients. The aim of this study is to investigate the possible role of clinical sociodemographic factors as moderators of clinical response to venlafaxine (SNRI) and sertraline (SSRI). Research was performed on Medline and EMBASE for randomized control trials in English focused on sertraline and venlafaxine in the treatment of major depressive disorder and 59 studies were included. Clinical efficacy of each treatment was assessed on the basis of Hamilton Depressive Rating Scale and Montgomery-Asberg Depression Rating Scale. A metaregression analysis was performed to evaluate the role of clinical and sociodemographic factors as moderators of outcome, calculating the effect of each variable with the random-effects method. Gender, ethnicity and duration of depressive episode could have a role in prediction of clinical response to both antidepressants. Venlafaxine seems to have better effects in females and in Caucasian patients. Sertraline seems to be more efficacious in the treatment of females. Both drugs were more efficacious in patients who suffered a shorter episode of illness. Our results could represent an interesting point of view in the perspective of choosing the most suitable therapy based on clinical and social features for each patient. Metaregression is a retrospective analysis, based on the cumulative results of previous studies, so the lack of original data could represent the main limitation in this report and in the interpretation of the results obtained.

Socioeconomic differences in antidepressant use in the PATH Through Life Study: evidence of health inequalities, prescribing bias, or an effective social safety net?

BACKGROUND

Depression is more common amongst those who are economically disadvantaged. However there is inconsistent evidence concerning the relationship between socioeconomic position and antidepressant use. Moreover, evidence of greater antidepressant use amongst those of lower socioeconomic position may reflect their greater psychiatric morbidity, a prescribing bias towards pharmacological treatments, or provide evidence of an effective social safety net. This study investigates these issues whilst addressing methodological limitations of earlier studies.

METHOD

Data were from a large, random community survey of Australian adults (N=4493) with linked administrative data for primary-care service use. Depression was measured using the Patient Health Questionnaire, with other measures of current mental health and history of depression included in analysis. Multiple personal indicators and a combined measure of social disadvantage were considered. A series of analyses systematically examined competing explanations for socioeconomic differences in depression and antidepressant treatment.

RESULTS

Markers of socioeconomic disadvantage were associated with a greater likelihood of antidepressant use. This finding was not attributable to the higher rates of depression amongst the disadvantaged. A similar pattern of results was evident for non-pharmaceutical treatments (primary care consultations). Socioeconomic position was not associated with use of complementary medications for depression, not covered by Australia's social safety net.

LIMITATIONS

Analysis did not consider specialist mental health services.

CONCLUSIONS

Socially disadvantaged respondents reported greater antidepressant use and service use after controlling for current depression symptoms. This pattern of findings suggests Australia's universal health-care system and social safety net may help address potential inequalities in health care.

Treating depression in disabled, low-income elderly: a conceptual model and recommendations for care

BACKGROUND

The treatment of depression in low-income older adults who live in poverty is complicated by several factors. Poor access to resources, disability, and mild cognitive impairment are the main factors that moderate treatment effects in this population. Interventions that not only address the depressive syndrome but also manage social adversity are sorely needed to help this patient population recover from depression.

METHODS

This paper is a literature review of correlates of depression in late life. In the review we propose a treatment model that combines case management (CM) to address social adversity with problem solving treatment (PST) to address the depressive syndrome.

RESULTS

We present the case of Mr Z, an older gentleman living in poverty who is also depressed and physically disabled. In this case we illustrate how the combination of CM and PST can work together to ameliorate depression.

CONCLUSIONS

The combination of age, disability, and social adversity complicates the management and treatment of depression. CM and PST are interventions that work synergistically to overcome depression and manage social problems.

Income and attrition in the treatment of depression: a STAR*D report

BACKGROUND

Attrition, or dropping out of treatment, remains a major issue in the care of depressed outpatients. Whether different factors are associated with attrition for different socioeconomic groups is not known. This report assessed whether attrition rates and predictors of attrition differed among depressed outpatients with different income levels.

METHODS

Outpatients with nonpsychotic major depressive disorder treated for up to 14 weeks with citalopram in the first step of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study were divided by household incomes of <$20,000, $20,000-<$40,000, and >or=$40,000. Attrition rates and sociodemographic and clinical correlates of attrition were identified for each group.

RESULTS

Regardless of income level, remission rates were lower for participants who dropped out of treatment. Attrition rates increased as income decreased. For all income levels, younger age was independently associated with attrition. For the lowest income level, less education, better mental health functioning, being on public insurance, and having more concurrent Axis I conditions were associated with a greater likelihood of attrition. For the middle income group, less education, better mental health functioning, being Black or of another non-White race, and treatment in a psychiatric versus primary-care setting predicted greater attrition. For the highest income group, being Hispanic, having a family history of drug abuse, and melancholic features predicted attrition. Atypical symptom features (middle income group) and recurrent depression (highest income group) were associated with retention.

CONCLUSIONS

Efforts to retain patients in antidepressant treatment should focus especially on less educated patients with lower household incomes and younger patients.

Socioeconomic status and anxiety as predictors of antidepressant treatment response and suicidal ideation in older adults

BACKGROUND

Separate reports from the maintenance treatment for late-life depression (MTLD) trials have shown that low socioeconomic status (SES) and anxiety symptoms at the time of treatment initiation predict lower levels of response to antidepressant treatment and higher levels of suicidal ideation in older adults.

AIM

To determine whether SES and anxiety independently contribute to worse treatment outcomes, as indicated by persistence of depressive symptoms during treatment and the persistence of suicidal ideation. Consistent with prior evidence that sociodemographic factors and clinical history are both prognostic of depression treatment efficacy, we hypothesized that SES and pre-existing anxiety symptoms will both predict lower levels of response to treatment and higher levels of suicidal ideation.

METHOD

Secondary analyses of data from the MTLD trials.

RESULTS

Regression analyses which controlled for comorbid anxiety indicated that residents of middle- and high-income census tracts were more likely to respond to treatment (HR, 1.63; 95%CI, 1.08-2.46) and less likely to report suicidal ideation during treatment (OR, 0.51; 95%CI, 0.28-0.90) than residents of low income census tracts. The same regression models indicated that pre-existing anxiety symptoms were independently related to lower treatment response (HR, 0.73; 95%CI, 0.60-0.89) and higher risk of suicidal ideation (OR, 1.45; 95%CI, 0.98-2.14).

CONCLUSION

These findings demonstrate the importance of treating anxiety symptoms during the course of treatment for late-life depression and, at the same time, addressing barriers to treatment response related to low SES.

Common genetic, clinical, demographic and psychosocial predictors of response to pharmacotherapy in mood and anxiety disorders

The aim of this study is to summarize available knowledge about common genetic, clinical, demographic and psychosocial predictors of response to pharmacotherapy in mood and anxiety disorders. A literature search was carried out by using MEDLINE and references of selected articles. The search included articles published up to March 2008. The main genetic finding concerns the serotonin transporter gene promoter polymorphisms, the long variant of which seems to be related to a positive response to therapy in mood disorders and could also have a role in the treatment of anxiety disorders. Among other predictors, the main factors common to both classes of disorder are comorbid axis II disorders and early onset of illness, which are related to a worse response to therapy and concomitant good physical conditions, absence of earlier treatments, early administration and response to therapies, and higher self- directedness, which is related to a better outcome. Many common predictors have been identified and these seem to be related to features covering the totality of patients that go beyond specific characteristics of single disorders. Possible limitations and suggestions for future research based on a more integrated vision of human complexity are discussed.

Pharmacogenetics in psychiatry: are we ready for widespread clinical use?

There are high expectations about the capabilities of pharmacogenetics to tailor psychotropic treatment and "personalize" treatment. While a large number of associations, with generally small effect size, have been discovered, a "test" with widespread use and adoption is still missing. A more realistic picture, recognizing the important contribution of clinical and environmental factors toward overall clinical outcome has emerged. In this emerging view, genetic findings, if considered individually, may have limited clinical applications. Thus, in recent years, combinations of information in several genes have been used for the selection of appropriate therapeutic doses and for the prediction of agranulocytosis, hyperlipidemia, and response to antipsychotic and antidepressant medications. While these tests based on multiple genes show greater predictive ability than individual allele tests, their net impact on clinical consequence and costs is limited, thus leading to limited penetration into widespread clinical use. As one looks at other branches of medicine, there are successful examples of pharmacogenetic tests guiding treatment, and thus, it is reasonable to hope that with the incorporation of clinical and environmental information and the identification of new genes drawn from genome-wide analysis, will improve the predictive utility of these tests leading to their increased use by clinicians.

Predicting the outcome of antidepressants and psychotherapy for depression: a qualitative, systematic review

As treatment outcome in depression varies widely, it is important to understand better the predictive value of particular patient characteristics. However, qualitative systematic reviews of the association between easily identifiable patient characteristics and outcome for commonly used treatment options have been unavailable. This article provides an overview of the consistency of findings on the association between sociodemographic factors and depression characteristics, on the one hand, and outcomes of pharmacotherapy, cognitive-behavioral therapy, and interpersonal/psychodynamic psychotherapy for major depression, on the other. There were no findings indicating that gender was associated with treatment outcome in the case of tricyclic antidepressants. There are some indications that younger patients respond worse to tricyclics, whereas especially women appeared to have better outcomes with modern antidepressants (selective serotonin/norepinephrine reuptake inhibitors). Marital status may be related to better outcome in the case of antidepressants and cognitive-behavioral therapy. Longer duration of depression was identified as a negative predictor, most consistently in psychotherapy. In none of the treatment modalities was recurrence a negative predictor. The relation between severity of depression and outcome appeared to be complex, precluding any straightforward inferences.

Clinical prediction of antidepressant response in mood disorders: linear multivariate vs. neural network models

Predicting the outcome of antidepressant treatment by pre-treatment features would be of great usefulness for clinicians as up to 50% of major depressives may not have a satisfactory response in spite of adequate trials of antidepressant drugs. In the present article we compared a linear multivariate model of predictors with a few artificial neural network (ANN) models differing from one another by outcome definition and validation procedure. The sample consisted of a reanalysis of 116 inpatients with a major depressive episode included in a 6-week open-label trial with fluvoxamine. With the original outcome definition (responders/non-responders), ANN performed better than logistic regression (90% of correct classifications in the training sample vs. 77%). However only 62% of new patients were correctly predicted by ANN for their outcome class. Length of the index episode, psychotic features and suicidal behavior emerged as outcome predictors in both models, while demographic characteristics, personality disorders and concomitant somatic morbidity were pointed to only by ANN analysis. Increase of classes in the outcome field resulted in a more elevated error: 46.4% for three classes, 60.4% for four classes and 70.3% for five classes. Overall, our findings suggest that antidepressant outcome prediction based on clinical variables is poor. The ANN approach is as valid as traditional multivariate techniques for the analysis of psychopharmacology studies. The complex interactions modelled through ANN may eventually be applied at the clinical level for individualized therapy. However, the accuracy of prediction is still far from satisfactory from a clinical point of view.

A neural network model for combining clinical predictors of antidepressant response in mood disorders

Artificial neural networks (ANN) represent a promising tool for combining multiple predictors in complex diseases. Antidepressant response in mood disorders is a typical complex phenomenon were a number of predictors influence outcome under non-linear interactions. In the present study we tested a neural network strategy for antidepressant outcome in subjects affected by major depression. One hundred and forty-five never reported depressed inpatients were included in this study (major depressives/bipolars: 111/34). A multi layer perceptron network composed of 1 hidden layer with 13 nodes was chosen. The network was performed on the sample of 145 cases divided as follows: train 73+verify 36+test 36. Correlation of predicted versus observed response was 0.46 in the test (independent) sample that corresponds to 21% of variance explained. Number of episodes, side effects, delusional features, baseline HAM-D, length of current episode, lithium augmentation, current medical condition and personality disorders were the main factors identified by the model. Sex, age at onset, polarity, plasma level and baseline VAS score were part of the model but with a lower rank. Overall, our findings suggest that neural networks could be a valid technique for the analysis of psychopharmacology studies. The complex interactions modelled through ANN may be eventually applied at the clinical level for the individualized therapy.

Dissecting the determinants of depressive disorders outcome: an in depth analysis of two clinical cases

Clinicians face everyday the complexity of depression. Available pharmacotherapies and psychotherapies improve patients suffering in a large part of subjects, however up to half of patients do not respond to treatment. Clinicians may forecast to a good extent if a given patient will respond or not, based on a number of data and sensations that emerge from face to face assessment. Conversely, clinical predictors of non response emerging from literature are largely unsatisfactory. Here we try to fill this gap, suggesting a comprehensive assessment of patients that may overcome the limitation of standardized assessments and detecting the factors that plausibly contribute to so marked differences in depressive disorders outcome. For this aim we present and discuss two clinical cases. Mr. A was an industrial manager who came to psychiatric evaluation with a severe depressive episode. His employment was demanding and the depressive episode undermined his capacity to manage it. Based on standardized assessment, Mr. A condition appeared severe and potentially dramatic. Mrs. B was a housewife who came to psychiatric evaluation with a moderate depressive episode. Literature predictors would suggest Mrs. B state as associated with a more favourable outcome. However the clinician impression was not converging with the standardized assessment and in fact the outcome will reverse the prediction based on the initial formal standard evaluation. Although the present report is based on two clinical cases and no generalizability is possible, a more detailed analysis of personality, temperament, defense mechanisms, self esteem, intelligence and social adjustment may allow to formalize the clinical impressions used by clinicians for biologic and pharmacologic studies.

Patients with extreme obesity: change in mental symptoms three years after gastric banding

OBJECTIVE

Extreme obesity causes grave psychosocial and psychopathological problems in addition to somatic morbidity. One possible treatment is gastric banding, a surgical reduction of stomach volume. The aim of this study was to investigate whether gastric banding leads to lasting change in: 1) the Body Mass Index (BMI); 2) social factors such as work and partnerships, eating behavior, anxiety and depression symptoms; and 3) health related quality of life.

METHOD

We surveyed a sample of 50 adipose women (BMI > 40 kg/m2). Primary outcome measures were self-reported changes on the scales of the Three-Factor Eating Questionnaire (TFEQ), the Hospital Anxiety and Depression Scale (HADS-D), and the Health Survey (SF-36).

RESULTS

In comparison with the control group, we observed significant changes in BMI (p < 0.01) and the existence of a partnership (p < 0.01), on all three scales of the TFEQ (p < 0.01), on both scales of the HADS-D (anxiety: p < 0.05; depression:p < 0.01), and on all scales of the SF-36 Health Survey (p between < 0.05 and < 0.01 in every case). The most marked changes in all the qualities investigated occurred within the first 12 months of surgery.

CONCLUSIONS

Three years after gastric banding, positive changes in BMI reduction, partnership, eating behavior, anxiety, depressive symptomatology, and health related quality of life could be observed. There was also a significant correlation between BMI reduction and reduction firstly on the depression scale (HADS-D) and secondly on the SF-36 scales for physical functioning (PHFU), role physical (ROPH), mental health (PSYC), and vitality (VITA).

Clinical and sociodemographic predictors of response to augmentation, or dose increase among depressed outpatients resistant to fluoxetine 20 mg/day

OBJECTIVE

Patients with major depressive disorder often show only partial or no response to antidepressants, necessitating next-step interventions such as dose increase or augmentation. Factors moderating response to these next-step interventions are not well-studied.

METHOD

In this randomized, double-blind investigation of next-step treatments in 101 outpatients who failed to respond to fluoxetine 20 mg for 8 weeks, the impact of depressive course and sociodemographic factors on likelihood of treatment response following dose increase or lithium or desipramine augmentation was examined.

RESULTS

After controlling for depression severity at baseline, current marriage and earlier onset of depression were associated with greater likelihood of response in a logistic regression. Intervention strategy was not predictive of response.

CONCLUSION

Marital status and earlier onset of depression may be clinically useful in predicting outcome following any next-step intervention for treatment resistance, rather than with particular strategies.