Bio

I received my Bachelor's degree in Biomedical Engineering with a minor in Industrial Design from Georgia Institute of Technology in 2020. During my time at Georgia Tech, I worked as an undergraduate researcher in Dr. Ajit Yoganathan's Cardiovascular Fluid Mechanics Lab. My project was focused on studying the contribution of foreign materials to thrombosis in transcatheter aortic valves using an in vitro flow loop. Beyond my research interests, I was also actively involved in the Society of Women Engineers, promoting outreach activities and creating mentorship opportunities for women in STEM.

Institute Affiliations

Honors & Awards

  • American Scandinavian Foundation Fellowship, American Scandinavian Foundation (August 2023)
  • NSF Graduate Research Fellowship, National Science Foundation (April 2020)

Education & Certifications

  • Master of Science, Stanford University, BIOE-MS (2022)
  • B.S., Georgia Institute of Technology, Biomedical Engineering (2020)

Contact

  • Academic
    pnair7@stanford.edu
    University - Student Department: Bioengineering Position: Graduate

Additional Info

All Publications

  • Non-invasive Estimation of Pressure Drop Across Aortic Coarctations: Validation of 0D and 3D Computational Models with In Vivo Measurements. Annals of biomedical engineering Nair, P. J., Pfaller, M. R., Dual, S. A., McElhinney, D. B., Ennis, D. B., Marsden, A. L. 2024

    Abstract

    Blood pressure gradient ([Formula: see text]) across an aortic coarctation (CoA) is an important measurement to diagnose CoA severity and gauge treatment efficacy. Invasive cardiac catheterization is currently the gold-standard method for measuring blood pressure. The objective of this study was to evaluate the accuracy of [Formula: see text] estimates derived non-invasively using patient-specific 0D and 3D deformable wall simulations. Medical imaging and routine clinical measurements were used to create patient-specific models of patients with CoA (N = 17). 0D simulations were performed first and used to tune boundary conditions and initialize 3D simulations. [Formula: see text] across the CoA estimated using both 0D and 3D simulations were compared to invasive catheter-based pressure measurements for validation. The 0D simulations were extremely efficient ([Formula: see text] 15 s computation time) compared to 3D simulations ([Formula: see text] 30 h computation time on a cluster). However, the 0D [Formula: see text] estimates, unsurprisingly, had larger mean errors when compared to catheterization than 3D estimates (12.1 ± 9.9 mmHg vs 5.3 ± 5.4 mmHg). In particular, the 0D model performance degraded in cases where the CoA was adjacent to a bifurcation. The 0D model classified patients with severe CoA requiring intervention (defined as [Formula: see text] [Formula: see text] 20 mmHg) with 76% accuracy and 3D simulations improved this to 88%. Overall, a combined approach, using 0D models to efficiently tune and launch 3D models, offers the best combination of speed and accuracy for non-invasive classification of CoA severity.

    View details for DOI 10.1007/s10439-024-03457-5

    View details for PubMedID 38341399

https://orcid.org/0000-0001-6326-9777