Toward individualized post-electroconvulsive therapy care: piloting the Symptom-Titrated, Algorithm-Based Longitudinal ECT (STABLE) intervention.

OBJECTIVES: Effective strategies to prolong remission after electroconvulsive therapy (ECT) are urgently needed. Fixed schedules for continuation ECT (C-ECT) cannot adapt to early signs of impending relapse. Symptom-Titrated, Algorithm-Based Longitudinal ECT (STABLE) is proposed as a novel patient-focused approach to individualize the ECT schedule. In STABLE, the ECT schedule adapts to symptom fluctuations to prevent overtreatment of those who do not need it and to recapture response in those who might have otherwise relapsed with a rigid dosing schedule. Here we back-test STABLE to optimize the algorithm for subsequent testing in a prospective trial.
METHODS: Three variations of the STABLE algorithm, differing in cutoff points to trigger or withhold additional ECT, were back-tested in a data set of 89 patients randomized to the C-ECT arm in the CORE (Consortium for Research on ECT) Study comparing C-ECT with combination pharmacotherapy.
RESULTS: The selected algorithm identified 100% of patients who ultimately relapsed as requiring additional ECT at an average of 2.2 weeks before relapse, while exposing 20% of sustained remitters to additional ECT. Other variations either failed to capture impending relapse or exposed an unacceptably large percentage of patients to potentially unnecessary ECT.
CONCLUSIONS: This patient-focused approach to relapse prevention is an attempt to provide the first operationalized guidance to the field regarding how to conduct C-ECT. The effectiveness of this approach should be tested in a randomized controlled trial.

RCT Entities:   Population Interventions Outcomes