Prediction of individual treatment allocation between electroconvulsive therapy or ketamine using the Personalized Advantage Index.

ABSTRACT: Electroconvulsive therapy (ECT) and ketamine are effective treatments for depression; however, evidence-based guidelines are needed to inform individual treatment selection. We adapted the Personalized Advantage Index (PAI) using machine learning to predict optimal treatment assignment to ECT or ketamine using EHR data on 2506 ECT and 196 ketamine patients. Depressive symptoms were evaluated using the Quick Inventory of Depressive Symptomatology (QIDS) before and during acute treatment. Propensity score matching across treatments was used to address confounding by indication, yielding a sample of 392 patients (n = 196 per treatment). Models predicted differential minimum QIDS scores (min-QIDS) over acute treatment using pretreatment EHR measures and SHAP values identified prescriptive pred

SUBMITTER: Wade BSC 

PROVIDER: S-EPMC11868618 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature