A poster titled “Using Machine Learning and Real-World Data to Identify Patients with Early Stage Bipolar-I Disorder for whom Aripiprazole Monohydrate LAIs are Likely to be a Favorable Treatment Option” was presented at the American Society of Clinical Psychopharmacology Annual Meeting. Authors: Mauricio Tohen, Jason Jones, Bharath Ravichandran, Karimah S. Bell Lynum, Norman Atkins Jr., Soma S. Nag, Kristine Harrsen, Anne M. Hutson Walker, Tiffany Yu, Bartek Augustyniak, Isaac Kirk-Koffi, Vincent Marino, Rachel Linker, and Christoph U. Correll.
The authors are affiliated with the University of New Mexico School of Medicine, Atropos Health, Guidehouse Inc., Otsuka Pharmaceutical Development & Commercialization Inc., H. Lundbeck A/S, Lundbeck LLC, Charité Universitätsmedizin Berlin, Donald and Barbara Zucker School of Medicine, Zucker Hillside Hospital, German Center for Mental Health, and the Einstein Center for Population Diversity.
Short Summary:
This study applied machine learning to real-world patient data to determine which early-stage Bipolar-I patients are most likely to respond favorably to Aripiprazole Monohydrate long-acting injectable (LAI) medications, with the goal of supporting more informed, personalized treatment decisions.
Key Conclusion:
- Findings suggest that LAIs may be underutilized in early-stage Bipolar-I and that earlier initiation could benefit patients compared to current practice