Prognostic models for predicting relapse or recurrence of major depressive disorder in adults

Risk prediction models for lung cancer in people who have never smoked: a protocol of a systematic review

BACKGROUND

Lung cancer is one of the most commonly diagnosed cancers and the leading cause of cancer-related death worldwide. Although smoking is the primary cause of the cancer, lung cancer is also commonly diagnosed in people who have never smoked. Currently, the proportion of people who have never smoked diagnosed with lung cancer is increasing. Despite this alarming trend, this population is ineligible for lung screening. With the increasing proportion of people who have never smoked among lung cancer cases, there is a pressing need to develop prediction models to identify high-risk people who have never smoked and include them in lung cancer screening programs. Thus, our systematic review is intended to provide a comprehensive summary of the evidence on existing risk prediction models for lung cancer in people who have never smoked.

METHODS

Electronic searches will be conducted in MEDLINE (Ovid), Embase (Ovid), Web of Science Core Collection (Clarivate Analytics), Scopus, and Europe PMC and Open-Access Theses and Dissertations databases. Two reviewers will independently perform title and abstract screening, full-text review, and data extraction using the Covidence review platform. Data extraction will be performed based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS). The risk of bias will be evaluated independently by two reviewers using the Prediction model Risk-of-Bias Assessment Tool (PROBAST) tool. If a sufficient number of studies are identified to have externally validated the same prediction model, we will combine model performance measures to evaluate the model's average predictive accuracy (e.g., calibration, discrimination) across diverse settings and populations and explore sources of heterogeneity.

DISCUSSION

The results of the review will identify risk prediction models for lung cancer in people who have never smoked. These will be useful for researchers planning to develop novel prediction models, and for clinical practitioners and policy makers seeking guidance for clinical decision-making and the formulation of future lung cancer screening strategies for people who have never smoked.

SYSTEMATIC REVIEW REGISTRATION

This protocol has been registered in PROSPERO under the registration number CRD42023483824.

Identifying relapse predictors in individual participant data with decision trees

BACKGROUND

Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions.

METHODS

Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis.

RESULTS

The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression.

CONCLUSIONS

Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.

Esketamine vs Midazolam in Boosting the Efficacy of Oral Antidepressants for Major Depressive Disorder: A Pilot Randomized Clinical Trial

Importance

Loss of a previously effective response while still using adequate antidepressant treatment occurs in a relatively high proportion of patients with major depressive disorder (MDD); therefore, there is a need to develop novel effective treatment strategies.

Objective

To assess the efficacy and safety of a single subanesthetic dose of esketamine in boosting the efficacy of oral antidepressants for treating fluctuating antidepressant response in MDD.

Design, Setting, and Participants

This single-center, double-blind, midazolam-controlled pilot randomized clinical trial was conducted at Beijing Anding Hospital, Capital Medical University in China. The study enrolled participants aged 18 years and older with fluctuating antidepressant response, defined as patients with MDD experiencing fluctuating symptoms after symptom relief and stabilization. Patient recruitment was conducted from August 2021 to January 2022, and participants were followed-up for 6 weeks. Data were analyzed as intention-to-treat from July to September 2022.

Interventions

All participants in the esketamine-treated group received intravenous esketamine at 0.2 mg/kg in 40 minutes. Participants in the midazolam control group received intravenous midazolam at 0.045 mg/kg in 40 minutes.

Main Outcomes and Measures

The primary outcome was the response rate at 2 weeks, defined as a 50% reduction in Montgomery-Åsberg Depression Rating Scale (MADRS). Secondary outcomes included response rate at 6 weeks, remission rates at 2 and 6 weeks, and change in MADRS and Clinical Global Impression-Severity score from baseline to 6 weeks; remission was defined by a MADRS score of 10 or lower.

Results

A total of 30 patients (median [IQR] age, 28.0 [24.0-40.0] years; 17 [56.7%] female) were randomized, including 15 patients randomized to midazolam and 15 patients randomized to esketamine; 29 patients completed the study. Response rates at 2 weeks were significantly higher in the esketamine-treated group than in the midazolam control group (10 patients [66.7%] vs 1 patient [6.7%]; P < .001). Participants treated with esketamine experienced significantly greater reduction in MADRS score from baseline to 2 weeks compared with those treated with midazolam (mean [SD] reduction, 15.7 [1.5] vs 3.1 [1.3]; P < .001). No serious adverse events were observed in this trial, and no psychotogenic effects and clinically significant manic symptoms were reported.

Conclusions and Relevance

This pilot randomized clinical trial found that a single subanesthetic dose of esketamine could boost the efficacy of oral antidepressants in treating fluctuating antidepressant response, with a good safety profile.

Trial Registration

Chinese Clinical Trial Registry Identifier: ChiCTR2100050335.

Clinical Practice Guideline: The Diagnosis and Treatment of Unipolar Depression—National Disease Management Guideline

BACKGROUND

Depression is one of the most common mental disorders worldwide. The German National Disease Management Guideline on Unipolar Depression (NDGM), (Nationale Versorgungsleitlinie, NVL), updated in 2022, contains recommendations on the diagnosis and treatment of acute and chronic depressive disorders.

METHODS

The update was based on the findings of a systematic review of the evidence (2013-2022) and was issued by a multidisciplinary panel after a formalized consensus process.

RESULTS

The structure of the guideline was fundamentally revised and is now based on the phases of depression and/or its treatment, as well as on the severity of the disease. There is newly added material with recommendations on Internet- and mobile-device based treatments, esketamine, repetitive magnetic stimulation, psychosocial therapies, rehabilitation, social participation, and complex forms of care. The guideline also emphasizes better coordination of all services in the care of patients with depression. This article covers the most important changes and additions among the 156 recommendations in the guideline. More information and accompanying materials are available at www.leitlinien.de/depression.

CONCLUSION

There are effective treatments for depression and a variety of supportive measures that can be applied with great benefit by primary care physicians, psychiatrists, psychotherapists, and complementary care providers. The updated guideline aims to further improve the early detection, definitive diagnosis, treatment, and interdisciplinary care of people with depression.

Relationship of insight to neurocognitive function and risk of recurrence in depression: A naturalistic follow-up study

Introduction

Major depressive disorder (MDD) is a highly recurrent mental illness accompanied by impairment of neurocognitive function. Lack of insight may affect patients’ motivation to seek treatment, resulting in poor clinical outcomes. This study explores the relationship of insight to neurocognitive function and the risk of recurrence of depressive episodes in patients with MDD.

Methods

Demographic, clinical variables, and neurocognitive function measured with Intra-Extra Dimensional Set Shift (IED) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were collected from 277 patients with MDD. Among them, 141 participants completed a follow-up visit within 1–5 years. Insight was measured using the 17-item Hamilton Depression Rating Scale (HAM-D). To explore the factors associated with recurrence, binary logistic regression models were used.

Results

Patients with MDD, without insight, had significantly higher total and factor scores (anxiety/somatization, weight, retardation, and sleep) on the HAM-D and worse performance in the neurocognition task, compared to those with insight. Furthermore, binary logistic regression revealed that insight and retardation can predict recurrence.

Conclusion

Lack of insight is associated with recurrence and impaired cognitive flexibility in patients with MDD.

The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial (N=424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z=2.8, p=.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [z=-2.7, p=.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.

Six-year prognosis of anxiety and depression caseness and their comorbidity in a prospective population-based adult sample

Background

Anxiety and depression are amongst the most prevalent mental health problems. Their pattern of comorbidity may inform about their etiology and effective treatment, but such research is sparse. Here, we document long-term prognosis of affective caseness (high probability of being a clinical case) of anxiety and depression, their comorbidity, and a no-caseness condition at three time-points across six years, and identify the most common prognoses of these four conditions.

Methods

Longitudinal population-based data were collected from 1,837 participants in 2010, 2013 and 2016. Based on the Hospital Anxiety and Depression Scale they formed the four groups of anxiety, depression and comorbidity caseness, and no caseness at baseline.

Results

The three-year associations show that it was most common to recover when being an anxiety, depression or comorbidity caseness (36.8 − 59.4%), and when not being a caseness to remain so (89.2%). It was also rather common to remain in the same caseness condition after three years (18.7 − 39.1%). In comorbidity it was more likely to recover from depression (21.1%) than from anxiety (5.4%), and being no caseness it was more likely to develop anxiety (5.9%) than depression (1.7%). The most common six-year prognoses were recovering from the affective caseness conditions at 3-year follow-up (YFU), and remain recovered at 6-YFU, and as no caseness to remain so across the six years. The second most common prognoses in the affective conditions were to remain as caseness at both 3-YFU and 6-YFU, and in no caseness to remain so at 3-YFU, but develop anxiety at 6-YFU.

Conclusions

The results suggest that only 37 − 60% of individuals in the general population with high probability of being a clinical case with anxiety, depression, and their comorbidity will recover within a three-year period, and that it is rather common to remain with these affective conditions after 6 years. These poor prognoses, for comorbidity in particular, highlight the need for intensified alertness of their prevalence and enabling treatment in the general population.

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

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.

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

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.

Prognostic models for predicting relapse or recurrence of major depressive disorder in adults

BACKGROUND

Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence.

OBJECTIVES

To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery.

SEARCH METHODS

We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status .

SELECTION CRITERIA

We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews.

MAIN RESULTS

We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model.

AUTHORS' CONCLUSIONS

Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.