Author:
Benrimoh David,Whitmore Kate,Richard Maud,Golden Grace,Perlman Kelly,Jalali Sara,Friesen Timothy,Barkat Youcef,Mehltretter Joseph,Fratila Robert,Armstrong Caitrin,Israel Sonia,Popescu Christina,Karp Jordan F.,Parikh Sagar V.,Golchi Shirin,Moodie Erica EM,Shen Junwei,Gifuni Anthony J.,Ferrari Manuela,Sapra Mamta,Kloiber Stefan,Pinard Georges-F.,Dunlop Boadie W.,Looper Karl,Ranganathan Mohini,Enault Martin,Beaulieu Serge,Rej Soham,Hersson-Edery Fanny,Steiner Warren,Anacleto Alexandra,Qassim Sabrina,McGuire-Snieckus Rebecca,Margolese Howard C.
Abstract
AbstractMajor Depressive Disorder (MDD) is a leading cause of disability and there is a paucity of tools to personalize and manage treatments. A cluster-randomized, patient-and-rater-blinded, clinician-partially-blinded study was conducted to assess the effectiveness and safety of the Aifred Clinical Decision Support System (CDSS) facilitating algorithm-guided care and predicting medication remission probabilities using clinical data. Clinicians were randomized to the Active (CDSS access) or Active-Control group (questionnaires and guidelines access). Primary outcome was remission (<11 points on the Montgomery Asberg Depression Rating Scale (MADRS) at study exit). Of 74 eligible patients, 61 (42 Active, 19 Active-Control) completed at least two MADRS (analysis set). Remission was higher in the Active group (n = 12/42 (28.6%)) compared to Active-Control (0/19 (0%)) (p = 0.01, Fisher’s exact test). No adverse events were linked to the CDSS. This is the first effective and safe longitudinal use of an artificial intelligence-powered CDSS to improve MDD outcomes.
Publisher
Cold Spring Harbor Laboratory