Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches

BACKGROUND

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

METHODS

Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months.

RESULTS

Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact.

CONCLUSIONS

Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials

BACKGROUND

Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes.

METHOD

Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators.

RESULTS

Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms.

CONCLUSION

The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

Understanding personalized dynamics to inform precision medicine: a dynamic time warp analysis of 255 depressed inpatients

BACKGROUND

Major depressive disorder (MDD) shows large heterogeneity of symptoms between patients, but within patients, particular symptom clusters may show similar trajectories. While symptom clusters and networks have mostly been studied using cross-sectional designs, temporal dynamics of symptoms within patients may yield information that facilitates personalized medicine. Here, we aim to cluster depressive symptom dynamics through dynamic time warping (DTW) analysis.

METHODS

The 17-item Hamilton Rating Scale for Depression (HRSD-17) was administered every 2 weeks for a median of 11 weeks in 255 depressed inpatients. The DTW analysis modeled the temporal dynamics of each pair of individual HRSD-17 items within each patient (i.e., 69,360 calculated "DTW distances"). Subsequently, hierarchical clustering and network models were estimated based on similarities in symptom dynamics both within each patient and at the group level.

RESULTS

The sample had a mean age of 51 (SD 15.4), and 64.7% were female. Clusters and networks based on symptom dynamics markedly differed across patients. At the group level, five dynamic symptom clusters emerged, which differed from a previously published cross-sectional network. Patients who showed treatment response or remission had the shortest average DTW distance, indicating denser networks with more synchronous symptom trajectories.

CONCLUSIONS

Symptom dynamics over time can be clustered and visualized using DTW. DTW represents a promising new approach for studying symptom dynamics with the potential to facilitate personalized psychiatric care.

Using network analysis to examine links between individual depressive symptoms, inflammatory markers, and covariates

BACKGROUND

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

METHODS

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

RESULTS

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with 'sleep problems', 'energy level', and 'weight/appetite changes'; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms 'insomnia', 'hypersomnia', and 'aches and pain' showed unique positive relations to all inflammatory markers.

CONCLUSIONS

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

Exploring the psychology of suicidal ideation: A theory driven network analysis

Two leading theories within the field of suicide prevention are the interpersonal psychological theory of suicidal behaviour (IPT) and the integrated motivational-volitional (IMV) model. The IPT posits that suicidal thoughts emerge from high levels of perceived burdensomeness and thwarted belongingness. The IMV model is a multivariate framework that conceptualizes defeat and entrapment as key drivers of suicide ideation. We applied network analysis to cross-sectional data collected as part of the Scottish Wellbeing Study, in which a nationally representative sample of 3508 young adults (18-34 years) completed a battery of psychological measures. Network analysis can help us to understand how the different theoretical components interact and how they relate to suicide ideation. Within a network that included only the core factors from both models, internal entrapment and perceived burdensomeness were most strongly related to suicide ideation. The core constructs defeat, external entrapment and thwarted belonginess were mainly related to other factors than suicide ideation. Within the network of all available psychological factors, 12 of the 20 factors were uniquely related to suicide ideation, with perceived burdensomeness, internal entrapment, depressive symptoms and history of suicide ideation explaining the most variance. None of the factors was isolated, and we identified four larger clusters: mental wellbeing, interpersonal needs, personality, and suicide-related factors. Overall, the results suggest that relationships between suicide ideation and psychological risk factors are complex, with some factors contributing direct risk, and others having indirect impact.

A Network Model of Resilience Factors for Adolescents with and without Exposure to Childhood Adversity

Resilience factors (RFs) help prevent mental health problems after childhood adversity (CA). RFs are known to be related, but it is currently unknown how their interrelations facilitate mental health. Here, we used network analysis to examine the interrelations between ten RFs in 14-year-old adolescents exposed ('CA'; n = 638) and not exposed to CA ('no-CA'; n = 501). We found that the degree to which RFs are assumed to enhance each other is higher in the no-CA compared to the CA group. Upon correction for general distress levels, the global RF connectivity also differed between the two groups. More specifically, in the no-CA network almost all RFs were positively interrelated and thus may enhance each other, whereas in the CA network some RFs were negatively interrelated and thus may hamper each other. Moreover, the CA group showed more direct connections between the RFs and current distress. Therefore, CA seems to influence how RFs relate to each other and to current distress, potentially leading to a dysfunctional RF system. Translational research could explore whether intervening on negative RF interrelations so that they turn positive and RFs can enhance each other, may alter 'RF-mental distress' relations, resulting in a lower risk for subsequent mental health problems.

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk

BACKGROUND

Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD?

METHODS

A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests.

RESULTS

Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71).

LIMITATIONS

This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men.

CONCLUSIONS

Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.

How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

BACKGROUND

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node - its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.

METHODS

We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.

RESULTS

Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.

CONCLUSIONS

Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.