  Sepsis is a life-threatening condition affecting one million people per year
in the US in which dysregulation of the body's own immune system causes damage
to its tissues, resulting in a 28 - 50% mortality rate. Clinical trials for
sepsis treatment over the last 20 years have failed to produce a single
currently FDA approved drug treatment. In this study, we attempt to discover an
effective cytokine mediation treatment strategy for sepsis using a previously
developed agent-based model that simulates the innate immune response to
infection: the Innate Immune Response agent-based model (IIRABM). Previous
attempts at reducing mortality with multi-cytokine mediation using the IIRABM
have failed to reduce mortality across all patient parameterizations and
motivated us to investigate whether adaptive, personalized multi-cytokine
mediation can control the trajectory of sepsis and lower patient mortality. We
used the IIRABM to compute a treatment policy in which systemic patient
measurements are used in a feedback loop to inform future treatment. Using deep
reinforcement learning, we identified a policy that achieves 0% mortality on
the patient parameterization on which it was trained. More importantly, this
policy also achieves 0.8% mortality over 500 randomly selected patient
parameterizations with baseline mortalities ranging from 1 - 99% (with an
average of 49%) spanning the entire clinically plausible parameter space of the
IIRABM. These results suggest that adaptive, personalized multi-cytokine
mediation therapy could be a promising approach for treating sepsis. We hope
that this work motivates researchers to consider such an approach as part of
future clinical trials. To the best of our knowledge, this work is the first to
consider adaptive, personalized multi-cytokine mediation therapy for sepsis,
and is the first to exploit deep reinforcement learning on a biological
simulation.
