The utility of a classificatory decision tree approach to assist clinical differentiation of melancholic and non-melancholic depression

The utility of the Sydney Melancholia Prototype Index (SMPI) for predicting response to electroconvulsive therapy in depression: A CARE Network study

An enhanced understanding of clinical predictors of positive ECT outcome could assist with the decision to prescribe ECT for select patients. Reliable predictors of ECT response such as psychotic symptoms and age have been identified, however, studies of melancholia and ECT response have been inconsistent. The Sydney Melancholia Prototype Index (SMPI) is a clinical measure designed to differentiate melancholic and non-melancholic depression. This study aimed to investigate whether melancholic depression (as measured by the clinician rated version of the SMPI) predicted a better response to ECT than non-melancholic depression. The study included data collated from four participating sites in the Clinical Alliance for ECT and Related treatments (CARE) network. The primary outcome was response (>50% improvement) on the Montgomery Asberg Depression Rating Scale (MADRS) and the secondary outcome was raw change in MADRS score. Of the 329 depressed patients included in the study, 81% had melancholic features and 76% met criteria for clinical response. SMPI defined melancholia was associated with older age, higher pre-treatment mood scores and presence of psychosis. Melancholia as defined by the SMPI, however, did not significantly predict either clinical response or overall mood improvement with ECT in multivariate analyses. Instead, older age, greater pre-treatment depression severity and the use of bifrontal compared to right unilateral ultrabrief ECT were significant predictors of mood improvement. Path analysis showed that higher pre-treatment mood score and older age were independently associated with mood improvement with ECT.

A Clinical Case Series of Acute and Maintenance Home Administered Transcranial Direct Current Stimulation in Treatment-Resistant Depression

OBJECTIVES

Transcranial direct current stimulation (tDCS) is a noninvasive neurostimulation technique being translated clinically for the treatment of depression. There is limited research documenting the longer-term effectiveness and safety of tDCS treatment. This case series is the first report of remotely supervised, home-administered tDCS (HA-tDCS) for depression in a clinical setting.

METHODS

We report clinical, cognitive, and safety outcomes from 16 depressed patients who received acute and/or maintenance HA-tDCS. We retrospectively examined clinical data from up to 2.5 years of treatment. Descriptive statistics are reported to document patient outcomes.

RESULTS

Twelve patients received acute treatment for a current depressive episode and 4 commenced tDCS maintenance therapy after responding to ECT or repetitive transcranial magnetic stimulation (rTMS). The cohort was highly treatment-resistant wherein 15 of 16 patients failed 3 trials or more of antidepressant medication in the current episode, and 6 patients failed to gain significant benefit from prior ECT or rTMS. Five of 12 patients responded to acute tDCS within 6 weeks, and 9 patients who received tDCS for more than 12 weeks maintained improvements over several months. Cognitive tests showed no evidence of impairments in cognitive outcomes after up to 2 years of treatment. Two patients were withdrawn from treatment because of blurred vision or exacerbation of tinnitus. Transcranial direct current stimulation was otherwise safe and well tolerated.

CONCLUSIONS

Transcranial direct current stimulation given for at least 6 weeks may be of clinical benefit even in treatment-resistant depression. Results provide support for long-term effectiveness, safety, and feasibility of remotely supervised HA-tDCS and suggest a role for maintenance tDCS after acute treatment with tDCS, rTMS, or ECT.

First-time administration of the Sydney Melancholia Prototype Index (SMPI) to non-English-speaking patients: a study from Brazil

Objective:

The Sydney Melancholia Prototype Index (SMPI) is a scale that uses a non-conventional strategy to assess melancholia status based on prototypic symptoms and illness course variables. This study aimed to evaluate the performance of the first translation of this instrument in a non-English-speaking population.

Methods:

A sample comprising 106 Brazilian outpatients with depression was evaluated simultaneously with the Brazilian version of the self-rated SMPI (SMPI-SR) and clinician-rated SMPI (SMPI-CR). The kappa coefficient and t test were used to evaluate concurrent validity vs. DSM-IV, CORE system, Hamilton Depression Rating Scale-6 item (HAM-D6), and HAM-D17 assignments to a melancholic or non-melancholic class. The prevalence of melancholia as well as sensitivity and specificity were calculated across instruments.

Results:

The prevalence of melancholia was highest using DSM-IV criteria (56.6%). The kappa agreement between SMPI-CR and DSM-IV melancholia assignment was moderate (kappa 0.44, p ≤ 0.001). SMPI-CR-assigned melancholic patients had significantly higher CORE, HAM-D17, and HAM-D6 scores. The test-retest consistency values for the SMPI were modest at best, and somewhat superior for the CR version.

Conclusion:

The Brazilian SMPI-CR presented satisfactory psychometric properties (which were superior to those of the SMPI-SR), and therefore appears to be a useful option for identifying melancholia and studying its causes and optimal treatments.

Carving depression at its joints? Psychometric properties of the Sydney Melancholia Prototype Index

Parker and colleagues developed the Sydney Melancholia Prototype Index (SMPI), a 24-item measure to assess a potential subtype of depression: melancholia. While research supports the validity of the measure, no study has assessed its psychometric properties. We recruited 1633 participants online, of whom 487 reported a lifetime period of depressed mood or anhedonia and were administered the SMPI. We conducted confirmatory factor analyses (CFA) of the SMPI, to assess the proposed fit of the measure. We also conducted exploratory factor analyses (EFA) to explore the structure implied by the current data. CFA did not support the hypothesized factor structure of the SMPI, no matter what structure we assumed as primary (i.e., a one factor, two factor, or bifactor model). An EFA suggested a five-factor solution wherein several items did not appear to co-vary reliably and other factors captured the severity of melancholic symptoms, negative mood reactivity, positive mood reactivity, emotionality and family relationships, and early life adversity. The SMPI may not measure a single construct. Future research should explore the longitudinal association between depression severity, contaminant symptoms, positive and negative mood reactivity, and early life experiences.

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND

The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders.

METHODS

PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models.

FINDINGS

Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy.

INTERPRETATION

To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.