# Learning-Enabled Autonomous Decision-Support for Blood Pressure Management in Hemorrhage Resuscitation via Population-Informed Statistical Inference

> **NIH NIH R21** · UNIV OF MARYLAND, COLLEGE PARK · 2024 · $323,968

## Abstract

PROJECT SUMMARY/ABSTRACT
Hemorrhage is accountable for approximately 40% of deaths due to traumatic injuries worldwide as well as the
leading cause of mortality in Americans 1-46 years of age. Since high rate of hemorrhage-induced deaths
occur before reaching definitive care, providing immediate life-saving interventions to hemorrhaging patients is
of paramount importance. Blood pressure (BP) management is a very important component of hemorrhage
resuscitation due to its central role in (i) reducing the hemorrhage-induced mortality as well as in (ii) developing
novel hemorrhage resuscitation protocols in clinical trials. But, clinicians are not effective at maintaining BP
within a goal range, and BP management protocol failures are common in clinical trials. Regardless, there is
no mature technology ready for clinical use to support clinicians with BP management.
 By extending its ongoing success with an autonomous vasopressor administration guidance technology
currently undergoing a clinical trial under an FDA IDE, the investigative team proposes to develop a learning-
enabled autonomous decision-support (LEAD) system for BP management during hemorrhage resuscitation,
which can predict future BP in a patient and recommend timings and doses of resuscitation fluid administration
in order to maintain the patient’s BP within a clinician-specified goal range, while continuously optimizing its
accuracy by learning the patient’s response to administration of fluids. The LEAD system will be suitable for
clinical use in ICUs, EDs, and even pre-hospital environments. The LEAD system will be most impactful when
a clinician is novice, distracted, or tired. In addition, by maintaining clinicians in the loop, there will be much
reduced regulatory risk, allowing for rapid transition to a clinical trial and dissemination. In this way, the LEAD
system has the potential to enable tight BP management during hemorrhage resuscitation by enhancing the
awareness of clinicians on a patient’s dynamic treatment trajectory.
 Key innovations pertaining to the LEAD system are (i) a novel population-informed, recursive, collective
statistical inference approach to prediction of future BP in a patient based on a physics-based physiological
model and a collective inference developed by the investigative team and (ii) its real-world implementation into
a computational user interface platform being ready for clinical use. To realize and validate the LEAD system,
we will (i) develop a BP prediction algorithm for the LEAD system via population-informed recursive collective
inference (SA1); (ii) evaluate the LEAD BP prediction algorithm using clinical datasets (SA2); and (iii) realize
the LEAD system using a computational user interface platform and conduct simulated real-time testing (SA3).
 If this project is successful, the investigative team will proceed to technology commercialization and
translation by pursuing a follow-up R01 proposal to optimize the LEAD system algorithm an...

## Key facts

- **NIH application ID:** 10911284
- **Project number:** 5R21EB034835-02
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Jin-Oh Hahn
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $323,968
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10911284

## Citation

> US National Institutes of Health, RePORTER application 10911284, Learning-Enabled Autonomous Decision-Support for Blood Pressure Management in Hemorrhage Resuscitation via Population-Informed Statistical Inference (5R21EB034835-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10911284. Licensed CC0.

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