Background Social determinants of health (SDOH) complicate medical care and affect clinical outcomes, but the lack of a reliable structured interview for medical patients has impeded clinical assessment and research on SDOH. We assessed the reliability and validity of a newly developed SDOH Patient Interview Form and investigated SDOH in patients with heart failure. Methods The SDOH Patient Interview Form was administered to recently hospitalized patients with heart failure between January 2021 and April 2024. The interviews were recorded, and 50 were randomly selected for the interrater reliability analysis. Results The sample included 367 participants (mean age, 61.2±12.1 years; 42.5% women; 57.7% minorities). The interrater reliability of SDOH Patient Interview Form categories (eg, Legal and Social Problems) was 100%. The κ reliability coefficients for individual items were ≥0.94. Higher lifetime SDOH counts were associated with lower age and income and higher New York Heart Association class and body mass index. Two distinct sets of problems were found to contribute to relatively high burdens of SDOH. The first originates with abuse in childhood and presents as severe socioeconomic deprivation in adulthood. The second includes losses of family members and occupational, financial, and caregiver stress along with difficulty affording medical care. Conclusions The SDOH Patient Interview Form is a reliable instrument for assessing patient‐reported SDOH in patients with heart failure. It is suitable for use in research and clinical contexts but requires further testing in other medical patient populations. This study revealed 2 distinct patterns of stressful problems that can contribute to a high overall burden of SDOH. Registration URL: https://clinicaltrials.gov; Unique identifier: NCT04637776.

Objective There have been numerous studies of specific psychiatric comorbidities such as major depression in patients with heart disease, but there have been relatively few studies of psychiatric multimorbidity in these patients. The purpose of this cross-sectional study was to investigate the prevalence and correlates of psychiatric multimorbidity in patients with heart failure (HF). Methods Patients who had been hospitalized with HF were enrolled in this cross-sectional study within 30 days of hospital discharge and interviewed within two weeks after enrollment. Participants completed the NetSCID-5 diagnostic interview, a social determinants of health (SDOH) interview, and perceived stress and health-related quality of life questionnaires. Results A total of 362 patients completed the interview. The maximum possible lifetime comorbidity count was 11 but the observed maximum was 8; the mean (SD) count was 1.48 (1.63). A total of 135 (37 %) patients had no history of any psychiatric disorder, 97 (27 %) had a lifetime history of a single disorder, and 130 (36 %) had ≥2 lifetime disorders. Higher numbers of psychiatric disorders were associated with younger age, more exposure to SDOH, higher perceived stress, and chronic obstructive pulmonary disease. Conclusion Psychiatric multimorbidity is prevalent in patients with HF and is associated with worse medical and social health status. New studies of the consequences or treatment of specific psychiatric comorbidities in patients with heart disease should take psychiatric multimorbidity into account, and further research on psychiatric multimorbidity per se is needed.

The development and evaluation of psychological outcome measures can involve the application of numerous sophisticated psychometric methods. Recent research noted that the use of different methods can lead to a plurality of modelling outcomes for commonly used measures. Furthermore, using these methods to optimise existing measures and derive weighted scores may not change the substantive results of studies when compared to original findings using total sum scores. In a series of semi-structured interviews, we presented these findings to 21 key stakeholders with psychology/psychiatry research backgrounds to elicit their perceptions regarding the use of advanced psychometric methods in applied research. Participants were purposively sampled and included psychometricians, clinicians, applied researchers, statisticians and academics. Using reflexive thematic analysis, we interpreted three themes; (1) Heterogeneity of latent traits, (2) A legacy of poor measurement, and (3) When psychometrics matter. Our findings highlight a mismatch between the generally held perception that latent traits are heterogeneous in nature and the way in which many measures were established as unitary constructs. The quality of commonly used outcome measures was critiqued, and a shortcoming in knowledge of how psychometrics could improve measurement accuracy was acknowledged. Improved training and increased collaboration with psychometricians could augment psychometric skills among applied researchers and enhance critical evaluation of routinely used outcome measures.

ObjectiveAs multiple sophisticated techniques are used to evaluate psychometric scales, in theory reducing error and enhancing measurement of patient reported outcomes, we aimed to determine whether applying different psychometric analyses would demonstrate important differences in treatment effects.Study Design and SettingWe conducted secondary analysis of individual participant data from 20 antidepressant treatment trials obtained from Vivli.org (n=6,843). Pooled item-level data from the HRSD-17 were analysed using confirmatory factory analysis (CFA), item response theory (IRT) and network analysis (NA). Multilevel models were used to analyse differences in trial effects at approximately 8 weeks (range 4-12 weeks) post-treatment commencement, with standardised mean differences calculated as Cohen’s d. Effect size outcomes for the original total depression scores were compared with psychometrically-informed outcomes based on abbreviated and weighted depression scores. ResultsSeveral items performing poorly during psychometric analyses and were eliminated, resulting in different models being obtained for each approach. Treatment effects were modified as follows per psychometric approach: 10.4%-14.9% increase for CFA, 0%-2.9% increase for IRT, 14.9%-16.4% reduction for NA. ConclusionPsychometric analyses differentially moderate effect size outcomes depending on the method used. In a 20-trial sample, factor analytic approaches increased treatment effect sizes relative to the original outcomes, NA decreased them, and IRT results reflected original trial outcomes.

Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument’s cutoff value for mild depression. A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (−0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.

In patients with type 2 diabetes (T2DM), depression increases the risk of poor glycemic control and decreases adherence to medications, exercise, and diet. Studies have shown an inverse relationship between plasma vitamin D (VD) level and depression risk. However, there are few interventional trials of African Americans (AAs), a demographic with higher prevalence of diabetes, depression, and VD deficiency. This randomized controlled trial evaluated the efficacy of vitamin D3 supplementation [4000 vs. 600 international units (IU)/day] for one year on mental and functional health outcomes in 75 adult AAs with T2DM with serum 25-hydroxy vitamin D (25(OH)D) level < 25 ng/mL. PHQ-9 and PROMIS questionnaires evaluated mental health outcomes, and 6-minute walk test estimated the ability to perform daily activities. At baseline, groups had similar levels of 25(OH)D, calcium, parathyroid hormone, hemoglobin A1c, and body mass index, and 25(OH)D levels correlated positively with a 6-minute walk distance. Surprisingly, both supplementation strategies increased 25(OH)D to > 30 ng/mL by 6 months with a plateau thereafter. Vitamin D3 4000 IU/day in AAs with T2DM did not produce significant difference in mental and functional health scores compared to 600 IU/day. Post-hoc analysis of those with baseline VD deficiency [25(OH)D < 20 ng/mL] demonstrated trends towards worsening pain interference and higher depression and fatigue scores throughout the study, plus consistently shorter 6-minute walk distances, most of which were independent of vitamin D supplementation group. These results suggest that VD deficient AAs with T2DM may be refractory to supplementation for improvement in mental and functional health outcomes.

The importance of psychological distress in patients with cardiovascular disease is increasingly recognized as both a contributing factor to the development and progression of cardiovascular disease and a consequence of the development of cardiovascular disease. Patients with acute myocardial infarction have increased risks for depression, anxiety, psychosocial stress, or posttraumatic stress disorder. Together, these negative psychological factors when occurring after myocardial infarction have been referred to as postmyocardial psychological distress. Up to half of patients after myocardial infarction may experience some form of psychological distress, and this postmyocardial psychological distress has been associated with an increased risk of future cardiac events. Biologically plausible mechanisms by which postmyocardial psychological distress may lead to increased future cardiac risk include lesser physical activity, smoking (and failure to stop smoking), excess alcohol consumption, poor diet, obesity, inadequate sleep, inadequate social support, decreased medication adherence, and poor attendance at cardiac rehabilitation. The data on whether treatment of postmyocardial psychological distress improves cardiac prognosis are mixed and of variable quality, and further studies, particularly in patients with anxiety, stress, and posttraumatic stress disorder, would be helpful. Regardless, multiple interventions can reduce psychological distress and thus lead to improved psychological health, a greater sense of emotional well-being, and a better quality of life. A goal of health care professionals should be to treat not only the disease but also the person as a whole in front of us.

Importance Collaborative care is a multicomponent intervention for patients with chronic disease in primary care. Previous meta-analyses have proven the effectiveness of collaborative care for depression; however, individual participant data (IPD) are needed to identify which components of the intervention are the principal drivers of this effect. Objective To assess which components of collaborative care are the biggest drivers of its effectiveness in reducing symptoms of depression in primary care. Data Sources Data were obtained from MEDLINE, Embase, Cochrane Library, PubMed, and PsycInfo as well as references of relevant systematic reviews. Searches were conducted in December 2023, and eligible data were collected until March 14, 2024. Study Selection Two reviewers assessed for eligibility. Randomized clinical trials comparing the effect of collaborative care and usual care among adult patients with depression in primary care were included. Data Extraction and Synthesis The study was conducted according to the IPD guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guideline. IPD were collected for demographic characteristics and depression outcomes measured at baseline and follow-ups from the authors of all eligible trials. Using IPD, linear mixed models with random nested effects were calculated. Main Outcomes and Measures Continuous measure of depression severity was assessed via validated self-report instruments at 4 to 6 months and was standardized using the instrument's cutoff value for mild depression. Results A total of 35 datasets with 38 comparisons were analyzed (N = 20 046 participants [57.3% of all eligible, with minimal differences in baseline characteristics compared with nonretrieved data]; 13 709 [68.4%] female; mean [SD] age, 50.8 [16.5] years). A significant interaction effect with the largest effect size was found between the depression outcome and the collaborative care component therapeutic treatment strategy (-0.07; P < .001). This indicates that this component, including its key elements manual-based psychotherapy and family involvement, was the most effective component of the intervention. Significant interactions were found for all other components, but with smaller effect sizes. Conclusions and Relevance Components of collaborative care most associated with improved effectiveness in reducing depressive symptoms were identified. To optimize treatment effectiveness and resource allocation, a therapeutic treatment strategy, such as manual-based psychotherapy or family integration, may be prioritized when implementing a collaborative care intervention.

BACKGROUND:Psychometric methods are used to remove underperforming items and reduce error in existing measures, albeit different approaches can produce different results. This study aimed to determine the implications of applying different psychometric methods for clinical trial outcomes. METHODS:Individual participant data from 15 antidepressant treatment trials from Vivli.org were analyzed. Baseline (pretreatment) and 8-week (range 4-12 weeks) outcome data from the Montgomery-Asberg Depression Rating Scale were subjected to best-practice factor analysis (FA), item response theory (IRT), and network analysis (NA) approaches. Trial outcomes for the original summative scores and psychometric-model scores were assessed using multilevel models. Percentage differences in Cohen's d effect sizes for the original summative and psychometrically modeled scores were the effects of interest. RESULTS:Each method produced unidimensional models, but the modified scales varied from 7 to 10 items. Treatment effects (d = 0.072) were unchanged for IRT (10 items), decreased by 1.3%-2.8% (eight-item abbreviated d = 0.070; weighted score d = 0.071) for NA, and increased by 11%-12.5% (seven-item abbreviated model d = 0.081; weighted score d = 0.080) for FA. DISCUSSION:IRT and NA yielded negligible differences in effect outcomes relative to original trials. FA increased effect sizes and may be the most effective method for identifying the items on which placebo and treatment group outcomes differ.

BACKGROUND:The 10-item Montgomery-Åsberg Depression Rating Scale (MADRS) is a commonly used measure of depression in antidepressant clinical trials. Numerous studies have adopted classical test theory perspectives to assess the psychometric properties of this scale, finding generally positive results. However, its network configural structure and stability is unexplored across different time-points and treatment groups. AIMS:To assess the network structure and stability of the MADRS in clinical settings pre- and post-treatment, and to determine a configurally invariant and stable model across time-points and treatment groups (placebo and intervention). METHOD:Individual participant data for 6440 participants from 14 clinical trials of major depressive disorder was obtained from the data repository Vivli.org. Exploratory Graphical Analysis (EGA) was used to identify empirical models pre-treatment (baseline) and post-treatment (8-week outcome). Bootstrapping techniques were applied to obtain optimised configurally invariant models. RESULTS:Empirical models presented with performance issues at baseline and for the placebo group at outcome. An abbreviated 8-item single-community model was found to be stable and configurally invariant across time-points and treatment groups. Symptoms such as low mood and lassitude showed most centrality across all models. LIMITATIONS:Metric invariance could not be explored due to research environment limitations. CONCLUSIONS:An 8-item one-community variant of the MADRS may provide optimal performance when conducting network analyses of antidepressant clinical trial outcomes. Findings suggest that interventions targeting low mood and lassitude might be most efficacious in treating depression among clinical trial participants. Further considerations of the potential impact on trial design and analysis should be explored.

Background The 17-item Hamilton Rating Scale for Depression (HRSD-17) is the most popular depression measure in antidepressant clinical trials. Prior evidence indicates poor replicability and inconsistent factorial structure. This has not been studied in pooled randomised trial data, nor has a psychometrically optimal model been developed. Aims To examine the psychometric properties of the HRSD-17 for pre-treatment and post-treatment clinical trial data in a large pooled database of antidepressant randomised controlled trial participants, and to determine an optimal abbreviated version. Method Data for 6843 participants were obtained from the data repository Vivli.org and randomly split into groups for exploratory (n = 3421) and confirmatory (n = 3422) factor analysis. Invariance methods were used to assess potential sex differences. Results The HRSD-17 was psychometrically sub-optimal and non-invariant for all models. High item variances and low variance explained suggested redundancy in each model. EFA failed at baseline and produced four item models for outcome groups (five for placebo-outcome), which were metric but not scalar invariant. Conclusions In antidepressant trial data, the HRSD-17 was psychometrically inadequate and scores were not sex invariant. Neither full nor abbreviated HRSD models are suitable for use in clinical trial settings and the HRSD's status as the gold standard should be reconsidered.

Major depressive disorder is a well-established risk factor for cardiac events in patients with coronary heart disease, but clinical trials have produced little evidence that treating depression reliably improves cardiac event-free survival in these patients. In this review, we offer evidence that certain symptoms that commonly remain after otherwise successful treatment of depression-insomnia, fatigue, and anhedonia-independently predict cardiac events. This may help to explain the failure of previous depression treatment trials to improve cardiac event-free survival even when other symptoms of depression improve. We thus propose that adverse cardiovascular effects that have long been attributed to syndromal depression may be instead caused by persistent fatigue, insomnia, and anhedonia, regardless of whether other symptoms of depression are present. We also identify interventions for these symptoms and call for more research to evaluate their effectiveness in depressed patients with coronary heart disease.

Background: Many patients with heart failure (HF) are repeatedly hospitalized. Heart failure self-care may reduce readmission rates. Hospitalizations may also affect self-care. Objective: The purpose of this secondary analysis was to test the hypotheses that better HF self-care is associated with a lower rate of all-cause readmissions and that readmissions motivate patients to improve their self-care. Methods: This was a prospective cohort study of patients with HF (N = 400) who were enrolled during a stay at an urban teaching hospital between 2014 and 2016. The Self-Care of Heart Failure Index v6.2 was administered during the hospital stay, along with other questionnaires, and repeated at 6-month intervals after discharge. All-cause readmissions and deaths were ascertained for 24 months. Results: A total of 333 (83.3%) were readmitted at least once, and 117 (29.3%) of the patients died during the follow-up period. A total of 1581 readmissions were ascertained. Higher Self-Care of Heart Failure Index Maintenance scores predicted more rather than fewer readmissions (adjusted hazard ratio, 1.09; 95% confidence interval, 1.01-1.17; P < .01). Conversely, more readmissions predicted higher Maintenance scores (b = 0.29; 95% confidence interval, 0.02-0.56; P < .05). Conclusions: These findings do not support the hypothesis that HF self-care maintenance or management helps to reduce the rate of all-cause readmissions, but they do suggest that the experience of multiple readmissions may help to motivate improvements in HF self-care.

Psychometric methods can be used to reduce redundancy and error in existing measures, albeit different approaches can produce different results. This study aimed to determine the implications of applying different psychometric methods for clinical trial outcomes. Individual participant data from 15 antidepressant treatment trials from Vivli.org were analysed. Baseline (pre-treatment) and 8-week (range 4-12 weeks) outcome data from the Montgomery-Asberg Depression Rating Scale (MADRS) were subjected to best-practice factor analysis (FA), item response theory (IRT) and network analysis (NA) approaches. Trial outcomes for the original summative scores and psychometric-model scores were assessed using multilevel models. Percentage differences in Cohen’s d effect sizes for the original summative and psychometrically-modelled scores were the effects of interest. Each method produced unidimensional models but the modified scales varied from 7-10 items. Treatment effects were unchanged for IRT (10 items), decreased by 1.3%-2.8% for NA (8 items), and increased by 11%-12.5% for FA (7 items). IRT and NA yielded negligible differences in effect outcomes relative to original trials. FA increased effect sizes and may be the most effective method for identifying the items on which placebo and treatment group outcomes differ.

BackgroundNetwork analysis (NA) is a modern statistical method for exploring relationships and patterns in complex data. NA techniques can also be used to eliminate unstable or nonperforming items, potentially reducing measurement error. However, the use of NA to improve measures used in randomised controlled trials has been limited, and it is unknown whether applying such techniques could impact trial effect size outcomes.AimWe aim to determine whether network analysis can impact clinical trial effects by reducing measurement error in depression models and subsequently modifying trial effect size outcomes. MethodWe will analyse individual participant data (IPD) from multiple depression trials that used the Montgomery Åsberg Depression Rating Scale (MADRS) as a depression measure. Data will be accessed from Vivli.org. A sequence of network modelling, followed by bootstrapping and stability analysis, will be performed to revise models of the MADRS at baseline and outcome. Net scores will be derived from abbreviated models and the differences in original trial outcomes versus the abbreviated outcomes utilising net scores, will be the effect of interest. Effect size outcomes will be modelled using multilevel linear regression. DiscussionThis study will determine whether network modelling can improve precision and inform better estimates of effect sizes in antidepressant treatment trials. The outcome of the proposed study could inform a shift in the way in which clinical trial data, and indeed data from other types of studies, may be analysed.

ABSTRACT It has been 35 years since we published a study in Psychosomatic Medicine showing that patients with coronary heart disease (CHD) and major depression were at twice the risk of having a cardiac event as were nondepressed patients (Carney et al. Psychosom Med. 1988;50:627–33). This small study was followed a few years later by a larger, more convincing report from Frasure-Smith et al. (JAMA. 1993;270:1819–25) showing that depression increased the rate of mortality in patients with a recent acute myocardial infarction. Since the 1990s, there have been many more studies of depression as a risk factor for cardiac events and cardiac-related mortality from all over the world, and many clinical trials designed to determine whether treating depression improves medical outcomes in these patients. Unfortunately, the effects of depression treatment in patients with CHD remain unclear. This article considers why it has been difficult to determine whether treatment of depression improves survival in these patients. It also proposes several lines of research to address this question, with the goal of definitively establishing whether treating depression can extend cardiac event–free survival and enhance quality of life in patients with CHD.

BACKGROUND:Depression is a recognized barrier to heart failure self-care, but there has been little research on interventions to improve heart failure self-care in depressed patients. OBJECTIVES:To investigate the outcomes of an individually tailored self-care intervention for patients with heart failure and major depression, and to determine whether the adequacy of self-care at baseline, the severity of depression or anxiety, or other factors affect the outcomes of this intervention. DESIGN:Secondary analysis of data from a pre-registered randomized controlled trial (NCT02997865). METHODS:Outpatients with heart failure and comorbid major depression (n = 139) were randomly assigned to cognitive behavior therapy or usual care for depression. In addition, an experienced cardiac nurse provided the tailored self-care intervention to all patients in both arms of the trial starting eight weeks after randomization. Weekly self-care intervention sessions were held between Weeks 8 and 16; the frequency was tapered to biweekly or monthly between Weeks 17 and 32. The Self-Care of Heart Failure Index (v6.2) was used to assess self-care outcomes, with scores ≥70 on each of its three scales (Maintenance, Management, and Confidence) being consistent with adequate self-care. The Week 16 Maintenance scale score was the primary outcome for this analysis. RESULTS:At baseline, 107 (77%) of the patients scored in the inadequate self-care range on the Maintenance scale. Between Weeks 8 and 16, Maintenance scores improved more in patients with initially inadequate than initially adequate self-care (11.9 vs. 3.2 points, p = .003). Sixty-six (48%) of the patients with initially inadequate Maintenance scores achieved scores in the adequate range by Week 32 (p < .0001). Covariate-adjusted predictors of better Maintenance outcomes included adequate Maintenance at baseline (p < .0001), higher anxiety at baseline (p < .05), and higher dosages of the self-care intervention (p < .0001). Neither treatment with cognitive behavior therapy nor less severe major depression predicted better self-care outcomes. CONCLUSIONS:Depressed patients with inadequate heart failure self-care are able to achieve clinically significant improvements in self-care with the help of an individually tailored self-care intervention. Further refinement and testing are needed to increase the intervention's potential for clinical implementation.

Objective: Both depression and inadequate self-care are common in patients with heart failure. This secondary analysis examines the one-year outcomes of a randomized controlled trial of a sequential approach to treating these problems. Methods: Patients with heart failure and major depression were randomly assigned to usual care (n = 70) or to cognitive behavior therapy (n = 69). All patients received a heart failure self-care intervention starting 8 weeks after randomization. Patient-reported outcomes were assessed at Weeks 8, 16, 32, and 52. Data on hospital admissions and deaths were also obtained. Results: One year after randomization, Beck Depression Inventory (BDI-II) scores were - 4.9 (95% C.I., -8.9 to -0.9; p < .05) points lower in the cognitive therapy than the usual care arm, and Kansas City Cardiomyopathy scores were 8.3 (95% C.I., 1.9 to 14.7; p < .05) points higher. There were no differences on the Self-Care of Heart Failure Index or in hospitalizations or deaths. Conclusions: The superiority of cognitive behavior therapy relative to usual care for major depression in patients with heart failure persisted for at least one year. Cognitive behavior therapy did not increase patients' ability to benefit from a heart failure self-care intervention, but it did improve HF-related quality of life during the followup period. Trial Registration:ClinicalTrials.gov Identifier NCT02997865

OBJECTIVE:Symptoms which commonly remain after treatment for major depression increase the risk of relapse and recurrence in medically well patients. The same symptoms predict major adverse cardiac events in observational studies of patients with coronary heart disease (CHD). The purpose of this study was to determine the prevalence and predictors of residual depression symptoms in depressed patients with CHD-.METHODS:Beck Depression Inventory-II data from two randomized clinical trials and an uncontrolled treatment study of depression in patients with CHD were combined to determine the prevalence and predictors of residual symptoms.RESULTS:Loss of energy, loss of pleasure, loss of interest, fatigue, and difficulty concentrating were the five most common residual symptoms in all three studies. They are also among the most common residual symptoms in medically well patients who are treated for depression. The severity of pre-treatment anxiety predicted the post-treatment persistence of all these symptoms except for loss of energy.CONCLUSIONS:The most common post-treatment residual symptoms found in this study of patients with coronary heart disease and comorbid major depression are the same as those that have been reported in previous studies of medically-well depressed patients. This suggests that they may be resistant to standard depression treatments across diverse patient populations. More effective treatments for these symptoms are needed.

Background Modern psychometric methods make it possible to eliminate nonperforming items and reduce measurement error. Application of these methods to existing outcome measures can reduce variability in scores, and may increase treatment effect sizes in depression treatment trials. Aims We aim to determine whether using confirmatory factor analysis techniques can provide better estimates of the true effects of treatments, by conducting secondary analyses of individual patient data from randomised trials of antidepressant therapies. Method We will access individual patient data from antidepressant treatment trials through Clinicalstudydatarequest.com and Vivli.org, specifically targeting studies that used the Hamilton Rating Scale for Depression (HRSD) as the outcome measure. Exploratory and confirmatory factor analytic approaches will be used to determine pre-treatment (baseline) and post-treatment models of depression, in terms of the number of factors and weighted scores of each item. Differences in the derived factor scores between baseline and outcome measurements will yield an effect size for factor-informed depression change. The difference between the factor-informed effect size and each original trial effect size, calculated with total HRSD-17 scores, will be determined, and the differences modelled with meta-analytic approaches. Risk differences for proportions of patients who achieved remission will also be evaluated. Furthermore, measurement invariance methods will be used to assess potential gender differences. Conclusions Our approach will determine whether adopting advanced psychometric analyses can improve precision and better estimate effect sizes in antidepressant treatment trials. The proposed methods could have implications for future trials and other types of studies that use patient-reported outcome measures.