# Siglec 9 for the diagnosis of ARDS

> **NIH NIH R21** · UNIVERSITY OF CHICAGO · 2020 · $121,500

## Abstract

Project Summary/Abstract
Acute respiratory distress syndrome (ARDS) accounts for 10% of admissions and 30% of mortality in intensive
care units. Despite extensive basic and clinical research, hospital mortality for patients with ARDS remains as
high as 45%. Major reasons for the lack of effective therapies include the lack of tools for predicting which
patients will develop ARDS and the difficulty of differentiating ARDS from other causes of pulmonary edema
like congestive heart failure (CHF). Because of such diagnostic uncertainty, fewer than half of ARDS cases are
recognized, and ARDS-specific therapies are underutilized by 25% worldwide. Using plasma samples from the
Fluid and Catheter Treatment Trial (a randomized controlled trial of liberal versus conservative fluid
management) in patients with ARDS, and the Treatment of Preserved Cardiac Function Heart Failure with an
Aldosterone Antagonist trial (a randomized trial of spironolactone in patients with heart failure with preserved
ejection fraction), we will in AIM 1 determine how well serum Siglec-9 distinguishes between ARDS and CHF
and in AIM 2 Correlate Siglec-9 levels with ARDS severity and clinical outcomes. The results of this work have
tremendous potential to enable earlier diagnosis, treatment, and prognostic assessment of patients with ARDS.

## Key facts

- **NIH application ID:** 9972954
- **Project number:** 5R21HL148488-02
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Sajid Shahul
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $121,500
- **Award type:** 5
- **Project period:** 2019-07-04 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9972954

## Citation

> US National Institutes of Health, RePORTER application 9972954, Siglec 9 for the diagnosis of ARDS (5R21HL148488-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9972954. Licensed CC0.

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