Materiały źródłowe

• #1 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Treatment outcomes for major depressive disorder (MDD) need to be improved. Presently, no clinically relevant tools have been established for stratifying subgroups or predicting outcomes. […] The results show that early recognition and treatment are crucial, as duration of untreated depression correlates with worse outcomes. Early improvement is associated with response and remission, while comorbidities prolong course of illness. […] Clear evidence of an inverse relationship between duration of episode and treatment outcome (either response or remission) underscores the importance of early intervention in MDD. In particular, replicable prospective and retrospective studies indicate that shorter duration of untreated disease both in terms of first and recurrent episodes is a prognostic factor indicating better treatment response and better long-term outcomes.

• #1 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Another important clinical variable is time to antidepressant response. For instance, one meta-analysis found that early improvement was positively linked to antidepressant treatment outcome in 15 of 16 studies. […] Early response to antidepressant treatment appears to occur independently of treatment modality or outcome parameters. […] Lower baseline function and quality of life including longer duration of the current index episode have been associated with lower remission rates to various types of antidepressant treatments. […] Worse outcomes in more severely ill patients at baseline were also reported in elderly patients treated in primary-care settings. […] Psychiatric comorbidity has been shown to influence outcome in both treated and untreated patients. […] Studies have found that elevated baseline anxiety symptoms or comorbid anxiety disorder are associated with worse antidepressant response to first-line selective serotonin reuptake inhibitors (SSRIs) or second-line treatment strategies.

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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9899563/

  This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. […] 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. […] One factor consistently found to be associated with prognosis of depression is the severity of depressive symptoms pre-treatment.

• #1 The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study | Diagnostic and Prognostic Research | Full Text

  https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-021-00101-x

  Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. […] We aim to develop a prognostic model to predict an individuals risk of relapse within 6 to 8 months of entering remission. […] We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. […] The objective is to develop and validate a multivariable prognostic model to predict relapse within 6 to 8 months in patients with remitted depression in primary care. […] This protocol outlines the methods for the development and validation of a novel prognostic model to predict an individuals risk of relapse of depression in a primary care setting. […] If it demonstrates sufficient predictive performance, it could be used to guide the allocation of interventions to prevent relapse in a primary care setting, improving outcomes for patients and ensuring efficient use of healthcare resources.

• #1 Plasma Brain-Derived Neurotrophic Factor Levels Predict the Clinical Outcome of Depression Treatment in a Naturalistic Study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039212

  Plasma BDNF levels are associated with clinical outcomes during the treatment of depression. […] We suggest that plasma BDNF could potentially serve as a prognostic biomarker for depression, predicting clinical outcome. […] Our results show that plasma BDNF levels are associated with clinical outcomes during the treatment of depression. […] To determine whether plasma BDNF levels could predict treatment outcome, we examined plasma BDNF levels in the remission and non-responder groups at the initial depressive syndrome stage. […] This important observation suggests that the biological backgrounds of patients with treatment-responsive MDD and patients with treatment-resistant MDD might differ, and that high plasma BDNF levels during the depressive syndrome stage may be indicative of treatment-resistant MDD patients. […] Therefore, it is very likely that plasma BDNF levels play an important role in MDD. […] Our naturalistic preliminary study reveals that plasma BDNF could represent a useful biomarker for predicting clinical outcome during the course of treatment for MDD.

• #1 Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197268

  Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve major depressive disorder (MDD) care. […] Early Identification of risk factors of resistance using baseline characteristics and initial response may be useful to guide treatment selection, avoid inefficient trial-and-error, alter disease course, and improve major depressive disorder (MDD) care. […] The risk factors for TRD as reviewed by Bennabi et al., 2014 include clinical risk factors such as comorbid anxiety disorder, current suicidal risk, non-response to the first antidepressant received in the patient’s lifetime and presence of melancholic features; bipolarity, early onset of first depressive episode, high rate of depressive recurrences, and lack of full remission after a previous episode; low reward dependence and low cooperativeness; high neuroticism, low extraversion, low openness, and low conscientiousness.

• #1 Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings | Neuropsychopharmacology

  https://www.nature.com/articles/s41386-020-00943-x

  Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians ability to accurately predict a specific patients eventual response to therapy. […] Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. […] Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77%. […] The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants.

• #1

  https://link.springer.com/article/10.1007/s00406-022-01418-4

  Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. […] The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. […] Models predicting treatment outcome on the basis of individual baseline characteristics can inform the stratification of patients according to their response chances and consequently, the physicians choice of individualized treatment strategies. […] However, in psychiatry, prediction models have not yielded any reliable and valid (bio)markers that are ready for incorporation into clinical tools to support diagnoses or guide treatment decisions.

• #1

  https://link.springer.com/article/10.1007/s00406-022-01418-4

  In general, prediction models of MDD treatment outcome based on sample sizes of at least several hundred patients can predict treatment outcome with moderate to good accuracies of 65%75%. […] Most models that have been published so far have confirmed that the most reliable predictors of MDD treatment outcome come from established clinical and sociodemographic factors that had already been identified in earlier studies. […] The main challenge in MDD outcome prediction seems to be the lack of powerful models and established predictive patient characteristics. […] The main issue of this lacking translation seems to be the absence of robust and generalizable predictors of treatment outcome, especially of biological and other objectively measurable markers.

• #2 The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study | Diagnostic and Prognostic Research | Full Text

  https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-021-00101-x

  Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. […] We aim to develop a prognostic model to predict an individuals risk of relapse within 6 to 8 months of entering remission. […] We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. […] The objective is to develop and validate a multivariable prognostic model to predict relapse within 6 to 8 months in patients with remitted depression in primary care. […] This protocol outlines the methods for the development and validation of a novel prognostic model to predict an individuals risk of relapse of depression in a primary care setting. […] If it demonstrates sufficient predictive performance, it could be used to guide the allocation of interventions to prevent relapse in a primary care setting, improving outcomes for patients and ensuring efficient use of healthcare resources.

• #2

  https://link.springer.com/article/10.1007/s00406-022-01418-4

  Standardized approaches in the treatment of MDD, such as guideline- and measurement-based treatments, can help to improve treatment success rates. […] With the current lack of personalized treatment, it is more likely that a chosen treatment will be inefficient than efficient for a certain patient. […] Thus, a better understanding of individual factors contributing to treatment outcome in MDD continues to be a major topic in psychiatry. […] The endeavor of finding indicators of treatment efficacy in MDD has led to a remarkable amount of publications from different psychiatric subfields. […] Overall, the most consistently identified and most predictive factors were derived from sociodemographic and clinical characteristics. […] The majority of predictive ML models of MDD treatment outcome have thus been created on data from large patient cohorts coming either from clinical trials or from observational studies.

• #2 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Another important clinical variable is time to antidepressant response. For instance, one meta-analysis found that early improvement was positively linked to antidepressant treatment outcome in 15 of 16 studies. […] Early response to antidepressant treatment appears to occur independently of treatment modality or outcome parameters. […] Lower baseline function and quality of life including longer duration of the current index episode have been associated with lower remission rates to various types of antidepressant treatments. […] Worse outcomes in more severely ill patients at baseline were also reported in elderly patients treated in primary-care settings. […] Psychiatric comorbidity has been shown to influence outcome in both treated and untreated patients. […] Studies have found that elevated baseline anxiety symptoms or comorbid anxiety disorder are associated with worse antidepressant response to first-line selective serotonin reuptake inhibitors (SSRIs) or second-line treatment strategies.

• #2 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  Data from the Sequential Treatment Alternatives to Relieve Depression (STAR*D) study, which included patients who were seeking medical care in routine medical or psychiatric outpatient treatment, indicate that roughly one-third (34.8%) of all MDD patients are free of any comorbidity; the most frequent comorbid Axis-I disorders are social phobia, generalized anxiety disorder, PTSD, and obsessive-compulsive disorder. […] The largest functional magnetic resonance imaging (fMRI) study of MDD patients conducted to date reported neurophysiological subtypes based on connectivity patterns within limbic and frontostriatal brain areas. […] While these interesting results suggest that fMRI measures could ultimately help classify biological subtypes of depression, these methods are far from ready for clinical application and results will have to be reproduced.

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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9899563/

  The present paper aims to fill this gap and further the consideration of the development of models that can be translated into clinical settings. […] The primary outcome was the BDI-II score at 34 months post-baseline. The secondary outcome was remission at 34 months post-baseline, defined as a score of 10 on the BDI-II. […] The first seven models all outperformed the null models on all metrics for primary and secondary outcomes. […] The large difference in observed remission rates between those predicted to have high compared to low BDI-II scores at 34 months informs the potential clinical relevance of these models. […] Prognoses generated by the models developed here could be informative for depressed patients seeking treatment in primary care. However, there were few differences between the models, with no clear advantage in using individual items over sum scores, or in using network models or factor analytic models to weight individual items, in order to derive prognostic predictions.

• #2 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  Relapse of depression is common and contributes to the overall associated morbidity and burden. […] We lack evidence-based tools to estimate an individuals risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. […] The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. […] Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p0.001) were associated with relapse. […] The validated model had low discrimination (C-statistic 0.60 (0.550.65)) and miscalibration concerns (calibration slope 0.81 (0.311.31)). […] On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.280.67), p0.001); this remained statistically significant after correction for multiple significance testing.

• #2 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. […] Relationship status warrants further research to explore its role as a prognostic factor for relapse. […] Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. […] We found that it is not possible to accurately predict individualised risk of relapse using prognostic factors that are routinely collected and available in primary care. […] Future prognosis research in this area should focus on exploring the feasibility of routinely measuring and documenting additional prognostic factors in primary care (eg, adverse childhood events, relationship status and social support) and including these in prognostic models.

• #2 Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197268

  Although there are many studies using baseline clinical risk factors/symptom clusters and/or early treatment response to predict longer term antidepressant treatment outcome within the same treatment regime in clinical ascertained samples, the studies using baseline clinical characteristics and initial response to earlier treatment regime to predict response to next treatment option are limited. […] We developed a series of machine learning models to predict TRD after two trials of treatment regimens using clinical and socio-demographic data. […] The models were trained using three sets of features, namely, all set of features (using all available STAR*D features from enrollment and week 0 (all features) and week 2 (only symptom severity score and percentage change from baseline were used) of level 1 treatment), top n features (a subset of top 30 representative features selected by clustering-2 and elastic net), and overlapping features (a subset of 22 overlapping features between STAR*D and RIS-INT-93) were externally validated and performed reasonably well with AUC in the range from 0.70 to-0.78 in the STAR*D hold-out testing dataset and 0.740.78 in RIS-INT-93 independent dataset.

• #2 Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings | Neuropsychopharmacology

  https://www.nature.com/articles/s41386-020-00943-x

  Hence, there is a significant need to derive accurate and quantitatively-based prognoses of eventual treatment outcomes, given a set of measured changes in symptom severity at an intermediate timepoint. […] The high variability of depressive symptom presentations and clinical trajectories of MDD present formidable challenges for clinician decision making. […] The prognostic depressive symptoms in this work were defined based on observed homogeneity in their responses at all timepoints, while demonstrating differential patterns of change under antidepressant treatment that were prognostic of clinical outcomes at 8 weeks. […] Our approach extends this prior work by establishing the prognostic capabilities of these symptoms using an unbiased approach. […] The consistently high predictive accuracies across numerous commonly-prescribed antidepressants observed in this work have several important implications that fit well with observations from the STAR*D trial: even with rigorously conducted antidepressant treatment, only 53% of patients may be expected to remit after 6 months. […] Our approach, which relied on only a limited number of depressive symptoms in addition to total depression scores to predict treatment outcomes, may introduce needed efficiencies into busy practices in addition to optimizing predictive accuracies.

• #2 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  The ability to predict an individual patients risk of relapse after an episode of depression might assist clinicians in targeting relapse prevention interventions towards those at greatest risk. […] We concluded that we currently lack evidence-based tools to assist clinicians with risk prediction of depressive relapse in any clinical setting and that new models are required to give accurate risk predictions in primary care settings.

• #3 Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/predicting-prognosis-for-adults-with-depression-using-individual-symptom-data-a-comparison-of-modelling-approaches/DB2C74C9B69380288FA6179EB7E2B84F

  This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. […] 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. […] One factor consistently found to be associated with prognosis of depression is the severity of depressive symptoms pre-treatment.

• #3

  https://link.springer.com/article/10.1007/s00406-022-01418-4

  In general, prediction models of MDD treatment outcome based on sample sizes of at least several hundred patients can predict treatment outcome with moderate to good accuracies of 65%75%. […] Most models that have been published so far have confirmed that the most reliable predictors of MDD treatment outcome come from established clinical and sociodemographic factors that had already been identified in earlier studies. […] The main challenge in MDD outcome prediction seems to be the lack of powerful models and established predictive patient characteristics. […] The main issue of this lacking translation seems to be the absence of robust and generalizable predictors of treatment outcome, especially of biological and other objectively measurable markers.

• #3 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. […] Relationship status warrants further research to explore its role as a prognostic factor for relapse. […] Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. […] We found that it is not possible to accurately predict individualised risk of relapse using prognostic factors that are routinely collected and available in primary care. […] Future prognosis research in this area should focus on exploring the feasibility of routinely measuring and documenting additional prognostic factors in primary care (eg, adverse childhood events, relationship status and social support) and including these in prognostic models.

• #3 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  Relapse of depression is common and contributes to the overall associated morbidity and burden. […] We lack evidence-based tools to estimate an individuals risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. […] The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. […] Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p0.001) were associated with relapse. […] The validated model had low discrimination (C-statistic 0.60 (0.550.65)) and miscalibration concerns (calibration slope 0.81 (0.311.31)). […] On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.280.67), p0.001); this remained statistically significant after correction for multiple significance testing.

• #3 Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0197268

  The overall accuracy of the GBDT model using top 30 features (k = 75) was 0.70 for the STAR*D testing dataset (predicted outcome ranging from -1 to 1, using mid-point 0 as the threshold to infer TRD vs. non-TRD status). […] The accuracy in predicting TRD and non-TRD were comparable (0.72 and 0.68, respectively). […] For the model using overlapping features, the overall accuracy of the GBDT model was 0.82 for RIS-INT-93 dataset.

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

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9899563/

  This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. […] 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. […] One factor consistently found to be associated with prognosis of depression is the severity of depressive symptoms pre-treatment.

• #3 The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study | Diagnostic and Prognostic Research | Full Text

  https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-021-00101-x

  Most patients who present with depression are treated in primary care by general practitioners (GPs). Relapse of depression is common (at least 50% of patients treated for depression will relapse after a single episode) and leads to considerable morbidity and decreased quality of life for patients. […] We aim to develop a prognostic model to predict an individuals risk of relapse within 6 to 8 months of entering remission. […] We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. […] The objective is to develop and validate a multivariable prognostic model to predict relapse within 6 to 8 months in patients with remitted depression in primary care. […] This protocol outlines the methods for the development and validation of a novel prognostic model to predict an individuals risk of relapse of depression in a primary care setting. […] If it demonstrates sufficient predictive performance, it could be used to guide the allocation of interventions to prevent relapse in a primary care setting, improving outcomes for patients and ensuring efficient use of healthcare resources.

• #4 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  Relapse of depression is common and contributes to the overall associated morbidity and burden. […] We lack evidence-based tools to estimate an individuals risk of relapse after treatment in primary care, which may help us more effectively target relapse prevention. […] The objective was to develop and validate a prognostic model to predict risk of relapse of depression in primary care. […] Residual depressive symptoms (OR: 1.13 (95% CI: 1.07 to 1.20), p0.001) and baseline depression severity (OR: 1.07 (1.04 to 1.11), p0.001) were associated with relapse. […] The validated model had low discrimination (C-statistic 0.60 (0.550.65)) and miscalibration concerns (calibration slope 0.81 (0.311.31)). […] On secondary analysis, being in a relationship was associated with reduced risk of relapse (OR: 0.43 (0.280.67), p0.001); this remained statistically significant after correction for multiple significance testing.

• #4 Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches – PubMed

  https://pubmed.ncbi.nlm.nih.gov/33952358/

  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. […] 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.

• #4 Prognosis and improved outcomes in major depression: a review | Translational Psychiatry

  https://www.nature.com/articles/s41398-019-0460-3

  This review outlines important clinical, psychosocial, and biological factors associated with response and remission to antidepressant treatment. Recent studies have led to important insights into neurobiological disease markers that could result in improved disease stratification and response prediction in the near future.

• #5 Development and validation of a prognostic model to predict relapse in adults with remitted depression in primary care: secondary analysis of pooled individual participant data from multiple studies | BMJ Mental Health

  https://mentalhealth.bmj.com/content/27/1/e301226

  We could not predict risk of depression relapse with sufficient accuracy in primary care data, using routinely recorded measures. […] Relationship status warrants further research to explore its role as a prognostic factor for relapse. […] Until we can accurately stratify patients according to risk of relapse, a universal approach to relapse prevention may be most beneficial, either during acute-phase treatment or post remission. […] We found that it is not possible to accurately predict individualised risk of relapse using prognostic factors that are routinely collected and available in primary care. […] Future prognosis research in this area should focus on exploring the feasibility of routinely measuring and documenting additional prognostic factors in primary care (eg, adverse childhood events, relationship status and social support) and including these in prognostic models.

• #5 Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches | Psychological Medicine | Cambridge Core

  https://www.cambridge.org/core/journals/psychological-medicine/article/predicting-prognosis-for-adults-with-depression-using-individual-symptom-data-a-comparison-of-modelling-approaches/DB2C74C9B69380288FA6179EB7E2B84F

  The large difference in observed remission rates between those predicted to have high compared to low BDI-II scores at 34 months informs the potential clinical relevance of these models. […] The present study used prognostic outcomes including depressive symptom severity at 34 months and remission, but both of these relied on sum scores from the BDI-II. […] Prognoses generated by the models developed here could be informative for depressed patients seeking treatment in primary care. However, there were few differences between the models, with no clear advantage in using individual items over sum scores, or in using network models or factor analytic models to weight individual items, in order to derive prognostic predictions.

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