Development and validation of a clinical prediction tool to estimate the individual risk of depressive relapse or recurrence in individuals with recurrent depression

Klein, Nicola S.; Holtman, Gea A; Bockting, Claudi; Heymans, Martijn W.; Burger, Huibert

doi:10.1016/j.jpsychires.2018.06.006

Cited by 24 publications

(30 citation statements)

References 53 publications

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“…However, such methods have not been applied to consider the longer-term prognosis of patients after the end of acute-phase treatment for depression, including continued chronic depression or relapse. A number of studies have attempted to develop models to predict relapses to depression [41][42][43]. However, these have been hampered by methodological problems, including small sample sizes, inappropriate handling of missing data, and a lack of validation, and they have largely failed to make accurate predictions [44].…”

Section: Introductionmentioning

confidence: 99%

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Cohen

Ambler

DeRubeis³

et al. 2021

JPM

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Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. Method: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. Results: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). Conclusions: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments.

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Section: Introductionmentioning

confidence: 99%

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Cohen

Ambler

DeRubeis³

et al. 2021

JPM

View full text Add to dashboard Cite

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“…However, many of these variables are not modifiable during treatment and are therefore unsuitable as predictors of sustainable therapeutic recovery. Moreover, even the latest clinical prediction tool, based on the most consistent findings and taking several differences between previously depressed patients into account, only modestly predicts individual relapse (Klein, Holtman, Bockting, Heymans, & Burger, 2018). In sum, overall sustainability of therapeutic outcomes of depression treatment seems limited and individual assessment of this sustainability is poor.…”

Section: Beyond Post-treatmentmentioning

confidence: 99%

Redefining Therapeutic Outcomes of Depression Treatment

Slofstra

Booij

Hoenders

et al. 2019

JPOR

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Responses to evidence-based interventions for depression are divergent: Some patients benefit more than others during treatment and some do not benefit at all or even deteriorate. Tailoring interventions to the individual may improve outcomes. However, such personalization of evidence-based treatment in depression requires investigation of individual outcomes and the individual trajectories towards these outcomes. This theoretical paper provides a critical reflection on individual outcomes of depression treatment. First, it is argued that outcomes should be broadened, from a focus on mainly depressive symptomatology to recovery in different domains. It is acknowledged that recovery from depression reflects a personal journey that differs from person to person. Second, outcome measures should be lengthened beyond the acute treatment phase, taking a lifetime perspective on depression. The challenge then is to discover which trajectories of what measures during what interventions result in personalized sustainable recovery and for whom. Routine outcome monitoring systems may be used to inform this quest towards assessment of personalized sustainable therapeutic outcomes. Adaptations to broaden and lengthen measurements in routine outcome monitoring systems are proposed to identify predictors of personalized sustainable recovery. Routine outcome monitoring systems may eventually be used to implement personalized treatments for depression that result in personalized sustainable recovery.

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“…There have been some previous attempts to develop relapse prediction models for depression [ 13 – 17 ]. These pre-existing prognostic models have some drawbacks with respect to successfully predicting relapse in a primary care context.…”

Section: Introductionmentioning

confidence: 99%

“…The most significant limitations were inadequate sample size, inappropriate handling of missing data and presentation of inappropriate performance statistics (calibration and discrimination not assessed) [ 18 ]. Furthermore, the developed models have either demonstrated insufficient predictive performance on external validation [ 13 ], or they could not be feasibly implemented in a primary care setting due to the large number and type of included predictors [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study

et al. 2021

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Background 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. The majority of patients will relapse within 6 months, and those with a history of relapse are more likely to relapse in the future than those with no such history. GPs see a largely undifferentiated case-mix of patients, and once patients with depression reach remission, there is limited guidance to help GPs stratify patients according to risk of relapse. We aim to develop a prognostic model to predict an individual’s risk of relapse within 6–8 months of entering remission. The long-term objective is to inform the clinical management of depression after the acute phase. Methods We will develop a prognostic model using secondary analysis of individual participant data drawn from seven RCTs and one longitudinal cohort study in primary or community care settings. We will use logistic regression to predict the outcome of relapse of depression within 6–8 months. We plan to include the following established relapse predictors in the model: residual depressive symptoms, number of previous depressive episodes, co-morbid anxiety and severity of index episode. We will use a “full model” development approach, including all available predictors. Performance statistics (optimism-adjusted C-statistic, calibration-in-the-large, calibration slope) and calibration plots (with smoothed calibration curves) will be calculated. Generalisability of predictive performance will be assessed through internal-external cross-validation. Clinical utility will be explored through net benefit analysis. Discussion We will derive a statistical model to predict relapse of depression in remitted depressed patients in primary care. Assuming the model has sufficient predictive performance, we outline the next steps including independent external validation and further assessment of clinical utility and impact. Study registration ClinicalTrials.gov ID: NCT04666662

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Development and validation of a clinical prediction tool to estimate the individual risk of depressive relapse or recurrence in individuals with recurrent depression

Cited by 24 publications

References 53 publications

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Redefining Therapeutic Outcomes of Depression Treatment

The development and validation of a prognostic model to PREDICT Relapse of depression in adult patients in primary care: protocol for the PREDICTR study

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