Predicting outcome with Intranasal Esketamine treatment: A machine-learning, three-month study in Treatment-Resistant Depression (ESK-LEARNING)
Mauro Pettorruso, Roberto Guidotti, Giacomo d’Andrea, Luisa De Risio, Antea D’Andrea, Stefania Chiappini, Rosalba Carullo, Sergio Barlati, Raffaella Zanardi, Gianluca Rosso, Sergio De Filippis, Marco Di Nicola, Ileana Andriola, Matteo Marcatili, Giuseppe Nicolò, Vassilis Martiadis, Roberta Bassetti, Domenica Nucifora, Pasquale De Fazio, Joshua D. Rosenblat, Massimo Clerici, Bernardo Maria Dell’Osso, Antonio Vita, Laura Marzetti, Stefano L. Sensi, Giorgio Di Lorenzo, Roger S. McIntyre, Giovanni Martinotti
Abstract
Treatment-resistant depression (TRD) represents a severe clinical condition with high social and economic costs. Esketamine Nasal Spray (ESK-NS) has recently been approved for TRD by EMA and FDA, but data about predictors of response are still lacking. Thus, a tool that can predict the individual patients' probability of response to ESK-NS is needed. This study investigates sociodemographic and clinical features predicting responses to ESK-NS in TRD patients using machine learning techniques. In a retrospective, multicentric, real-world study involving 149 TRD subjects, psychometric data (Montgomery-Asberg-Depression-Rating-Scale/MADRS, Brief-Psychiatric-Rating-Scale/BPRS, Hamilton-Anxiety-Rating-Scale/HAM-A, Hamilton-Depression-Rating-Scale/HAMD-17) were collected at baseline and at one month/T1 and three months/T2 post-treatment initiation. We trained three different random forest classifiers, able to predict responses to ESK-NS with accuracies of 68.53% at T1 and 66.26% at T2 and remission at T2 with 68.60% of accuracy. Features like severe anhedonia, anxious distress, mixed symptoms as well as bipolarity were found to positively predict response and remission. At the same time, benzodiazepine usage and depression severity were linked to delayed responses. Despite some limitations (i.e., retrospective study, lack of biomarkers, lack of a correct interrater-reliability across the different centers), these findings suggest the potential of machine learning in personalized intervention for TRD.