Precision ECT for major depressive disorder: A review of clinical factors, laboratory, and physiologic biomarkers as predictors of response and remission

Yao, Zhixing; McCall, William Vaughn; Essali, Norah; Wohl, Ethan; Parker, Carmen Black; Rosenquist, Peter B.; Youssef, Nagy A.

doi:10.1016/j.pmip.2019.07.001

Cited by 8 publications

(11 citation statements)

References 53 publications

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“…These results show that neuroimaging data can provide a good prediction of ECT remission for individual patients across different centers. In line with recent meta-analyses, older age and higher depression severity at baseline were associated with better ECT outcome 7, 43, 44 . However, our classification results show that this information is not sufficient for making individual predictions, highlighting the relevance of obtaining neuroimaging data for accurate predictions.…”

Section: Discussionsupporting

confidence: 86%

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Bruin

Oltedal

Bartsch

et al. 2021

Preprint

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Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (MRI) and functional MRI biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. Here, we used MRI data of 189 depressed patients from seven participating centers of the Global ECT-MRI Research Collaboration (GEMRIC) to develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting. We used multimodal data (i.e., clinical, structural MRI and resting-state functional MRI) and evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (minimal symptoms after treatment) using a support vector machine (SVM) classifier. Remission classification using a combination of gray matter volume with functional connectivity led to good performing models with 0.82-0.84 area under the curve (AUC) when trained and tested on samples coming from all centers, and remained acceptable when validated on other centers with 0.71-0.73 AUC. These results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

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Section: Discussionsupporting

confidence: 86%

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Bruin

Oltedal

Bartsch

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Some of them identified a few variables that appear to replicate across studies. Better outcomes have been found for older patients, those with psychotic depres sion, those with high suicidal intent, and those who exhibit early symptom changes 90,96 . However, due to the small sample size in most ECT trials, and the typically nonrandomized study de signs, this area has not seen much progress.…”

Section: Neurobiological Treatmentsmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

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“…factors associated with response to ECT, with factors including older age, presence of psychotic symptoms, and increased severity of depression being associated with improved response. [3][4][5][6][7] These studies, however, have had limited sample sizes, and meta-analyses have been challenged by the heterogeneity of study design including variations in the definition of remission/response as well as the specific predictors included in the analysis. 8 Additionally, many studies focus on predictors of short-term response to ECT over a few treatments, without examining predictors that predict response over a greater number of treatments.…”

mentioning

confidence: 99%

Factors associated with early and late response to electroconvulsive therapy

Hart

McCoy

Henry

et al. 2023

Acta Psychiatr Scand

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Objective: Electroconvulsive therapy (ECT) is an effective treatment for severe depressive symptoms, yet more research is needed to examine predictors of treatment response, and factors associated with response in patients not initially improving with treatment. This study reports factors associated with time to response (early vs. late) to ECT in a real-world setting.Methods: This was a retrospective, single-center cohort study of patients endorsing moderate to severe depressive symptoms using the Quick Inventory of Depressive Symptomatology (QIDS; QIDS>10). Response was defined as 50% or greater decrease in QIDS score from baseline. We used logistic regression to predict response at treatment #5 (early response) as well as after treatment #5 (late response) and followed patients through ECT discontinuation or through treatment #20.Results: Of the 1699 patients included in this study, 555 patients (32.7%) responded to ECT treatment at treatment #5 and 397 (23.4%) responded after treatment #5. Among patients who did not respond by treatment #5, those who switched to brief pulse width ECT from ultrabrief pulse ECT had increased odds of response after treatment #5 compared with patients only receiving ultrabrief pulse (aOR = 1.55, 95% CI: 1.16-2.07). Additionally, patients with less improvement in QIDS from baseline to treatment #5 had decreased odds of response after treatment #5 (aOR = 0.97, 95% CI = 0.97-0.98). Conclusion:Among depressed patients treated with ECT, response occurred in 56.0% of patients by treatment #20. Patient receiving ultrabrief pulse ECT at baseline and who did not respond by treatment #5 had greater odds of subsequent response if switched to brief pulse ECT than if continued with ultrabrief pulse.affective disorders, cohort studies, electroconvulsive therapy, real world evidence | INTRODUCTIONElectroconvulsive therapy (ECT) is an effective treatment for severe depressive symptoms, with reported remission rates ranging from 50% to 70%. 1,2 Given the cost and potential side effects of ECT, an improved understanding of predictors of ECT response would help guide referral for ECT treatment. Multiple prior studies have explored

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Precision ECT for major depressive disorder: A review of clinical factors, laboratory, and physiologic biomarkers as predictors of response and remission

Cited by 8 publications

References 53 publications

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

The promise of machine learning in predicting treatment outcomes in psychiatry

Factors associated with early and late response to electroconvulsive therapy

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