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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Lisanby, SH; Sampson, S; Husain, MM; Petrides, G; Knapp, RG; McCall, WV; Young, RC; Prudic, J; Kellner, CH

Published Date

  • September 2008

Published In

Volume / Issue

  • 24 / 3

Start / End Page

  • 179 - 182

PubMed ID

  • 18708943

Pubmed Central ID

  • PMC2743247

Electronic International Standard Serial Number (EISSN)

  • 1533-4112

Digital Object Identifier (DOI)

  • 10.1097/YCT.0b013e318185fa6b


  • eng

Conference Location

  • United States