Dr. Vesela Kovacheva’s lab is focused on utilizing big data, genetics and artificial intelligence approaches to personalize disease management and drug administration during pregnancy. She/her group performed a genome-wide association study (GWAS) on patients with post-partum hemorrhage (PPH) from the UK Biobank and identified several putative novel genetic loci near genes involved in cell interactions and immunity. The top SNP was near the LGALS3BP gene that modulates cell-cell and cell-matrix interactions and the immune response associated with natural killer and lymphokine-activated killer cytotoxicity. Understanding the underlying pathophysiology of PPH may facilitate the development of novel predictive methods and individualize the treatment options for PPH.
Using AI methods, they created an algorithm able to predict the systolic blood pressure (SBP) and determine the phenylephrine dose needed to maintain hemodynamic stability in parturients after spinal anesthesia. They developed a software application for clinical use that generates predictions for the SBP and phenylephrine dose every minute. Due to the transparent nature of their methods, they demonstrate the decision making of the algorithm and empower the physician to take care of patients even with conditions that were not present in the training cohort. They envision connecting their model to receive vital signs directly from the anesthesia monitor and drive an infusion pump, thus personalizing and automating the titration of drug infusions. These projects are supported by funding from the Foundation for Anesthesia Education and Research, Partners Innovation and Brigham Research Institute.