# Data-Driven Identification of the Acute Respiratory Distress Syndrome

> **NIH NIH K01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $172,680

## Abstract

PROJECT SUMMARY/ABSTRACT
This K01 proposal will complete Michael Sjoding, MD, MSc's training towards his long-term career goal of
improving care of patients with acute respiratory disease. Dr. Sjoding is a Pulmonary and Critical Physician at
the University of Michigan with master's level training in clinical study design and biostatistics. This proposal
builds on Dr. Sjoding's prior expertise, providing protected time for additional training in data science, the
technical methods for deriving new knowledge about human disease from “Big Biomedical Data” in the rich
training environment at the University of Michigan. The project's research goal is to develop real-time systems
to improve accuracy and timeliness of Acute Respiratory Distress Syndrome (ARDS) diagnosis using
electronic health record data. ARDS is a critical illness syndrome affecting 200,000 people each year with high
mortality. Under-recognition of this syndrome is the key barrier to providing evidence-based care to patients
with ARDS. The research will be completed under the guidance of primary mentor Theodore J. Iwashyna, MD,
PhD and co-mentors Timothy P. Hofer, MD, MSc, and Kayvan Najarian, PhD, and a scientific advisory board
with additional expertise in data science and applied clinical informatics. The 5-year plan includes didactic
coursework, mentored research, and professional development activities, with defined milestones to ensure
successful transition to independence. The mentored research has 2 specific Aims:
Aim 1. Develop a novel system for identifying ARDS digital signatures in electronic health data to accurately
identify patients meeting ARDS criteria.
Aim 2. Define the early natural history of developing ARDS, to more accurately predict patients' future ARDS
risk.
Both Aims will utilize rigorous 2-part designs, with the ARDS diagnostic and prediction models developed in the
same retrospective cohort and validated in temporally distinct cohorts. In completing these high-level aims, the
research will leverage high-resolution electronic health record and beside-monitoring device data to study
ARDS with unprecedented detail, providing new insights into ARDS epidemiology and early natural history.
This work will build to at least two R01 proposals: (1) testing the impact of a real-time electronic health record-
based ARDS diagnostic system to improve evidence-based care practice, (2) defining ARDS subtypes using
deep clinical phenotypic data. The work will build toward a programmatic line of research using high-resolution
electronic health data to improve understanding of critical illness and respiratory disease. In completing this
proposal, Dr. Sjoding will acquire unique computational expertise in data science methods, complementing his
previous training, which he can then readily apply to address other research challenges in respiratory health.
The ambitious but feasible training and mentored research proposed during this K01 award will allow him to
achieve his goal o...

## Key facts

- **NIH application ID:** 9908166
- **Project number:** 5K01HL136687-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Michael William Sjoding
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $172,680
- **Award type:** 5
- **Project period:** 2017-04-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9908166

## Citation

> US National Institutes of Health, RePORTER application 9908166, Data-Driven Identification of the Acute Respiratory Distress Syndrome (5K01HL136687-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9908166. Licensed CC0.

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