# CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patients

> **NIH NIH UL1** · ALBERT EINSTEIN COLLEGE OF MEDICINE · 2020 · $1,003,424

## Abstract

PROJECT SUMMARY
The Einstein-Montefiore Institute for Clinical and Translational Research (ICTR) proposes an Administrative
Supplement pursuant to NOT-TR-20-011, CTSA Program Applications to Address 2019 Novel Coronavirus
(Covid-19). Specifically, this application addresses the urgent need for research on the coronavirus pandemic
with a project focusing on informatics and data science to preemptively identify patients with the life-
threatening complications of SARS-CoV-2, using CTSA-supported core resources. Characterized by severe
hypoxemia, tachypnea, and decreased lung compliance, the diagnosis of acute respiratory failure (ARF) is a
bad prognostic sign, and in a subset, leads to development of acute respiratory distress syndrome (ARDS).
The rates of Covid-19 infection and death in the Bronx have been higher than any other borough of NYC. As
the major regional health system, our experience with Covid-19 provides guideposts that may prevent future
victims of this pandemic. The bleak picture for ARDS in the 4,452 patients admitted showed that 78% of our
intubated Covid-19 patients developed ARDS, with 42% mortality. The overall goal of this proposal is to
leverage our novel informatics and analytics platforms enabled by the Einstein-Montefiore CTSA (NIH/NCATS
1ULTR002556), and extensive Artificial Intelligence and Deep Learning resources to implement a novel,
situational awareness and clinical decision support system for ARF and ARDS (SA-ARDS). We will re-train our
existing deep learning models with data collected from Covid-19 patients and contextualize its implementation
with data from the Covid-19 response during the pandemic in NYC. The SA-ARDS data platform will provide
longitudinally integrated clinical data for research and multi-institutional and national collaborations, with the
following specific aims: Aim 1: To integrate, re-train, and validate our novel, near real-time, Electronic Risk
Assessment System (ERAS 1.0) optimized for early recognition of ARF, ARDS, and inpatient mortality; Aim 2:
To develop an evidence based, real-time, and context appropriate Situational Awareness clinical decision
support system targeting ARF and ARDS response (SA-ARDS); and Aim 3: Through our partner CTSA
organizations, to standardize and disseminate ERAS 1.0 and the SA-ARDS to other health systems, including
the NYC consortium of CTSA hubs and the PCORI INSIGHT network. We will use the clinical data underlying
the SA-ARDS to support research in local, regional, and national collaborations. All the methods and tools
developed will be shared with the CTSA community via NCATS' National Center for Data to Health (CD2H).

## Key facts

- **NIH application ID:** 10158737
- **Project number:** 3UL1TR002556-04S2
- **Recipient organization:** ALBERT EINSTEIN COLLEGE OF MEDICINE
- **Principal Investigator:** MARLA J KELLER
- **Activity code:** UL1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,003,424
- **Award type:** 3
- **Project period:** 2020-07-09 → 2021-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10158737

## Citation

> US National Institutes of Health, RePORTER application 10158737, CTSA Administrative Supplement for Informatics Core: A novel AI/ML system to predict respiratory failure and ARDS in Covid-19 patients (3UL1TR002556-04S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10158737. Licensed CC0.

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