Lessons Learned from Massively Parallel Model of Ventilator Splitting

Authors: Mike Kaplan, Charles Kneifel, Victor Orlikowski, James Dorff, and Mike Newton (Duke University); Andy Howard (Microsoft Corporation); and Don Shin, Muath Bishawi, Simbarashe Chidyagwai, Peter Balogh, Simbarashe Chidyagwai, and Amanda Randles (Duke University)

Abstract: There has been a pressing need for an expansion of the ventilator capacity in response to the recent COVID19 pandemic. To help address this need, a patient-specific airflow simulation was developed to support clinical decision-making for efficacious and safe splitting of a ventilator among two or more patients with varying lung compliances and tidal volume requirements. The computational model provides guidance regarding how to split a ventilator among two or more patients with differing respiratory physiologies. There was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. In order to support FDA submission, a large-scale and robust cloud instance was designed and deployed within 24 hours, and 800,000 compute hours were utilized in a 72-hour period.

Extended Abstract: pdf
Presentation: pdf



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