# Prematurity-Related Ventilatory Control: Leadership Data and Coordination Center (LDCC)

> **NIH NIH U01** · UNIVERSITY OF VIRGINIA · 2020 · $249,963

## Abstract

Project summary/abstract
Fundamental gaps in prevention of chronic lung disease in premature infants include the lack of
understanding of mechanisms by which maturation of ventilatory control allows maintenance of
adequate oxygenation, and how immature breathing phenotypes contribute to outcomes. Achieving
the long-term goal of trials of effective preventive measures and treatments includes detection and
analysis of immature breathing patterns in a large database of clinical information and
cardiorespiratory monitoring data from multiple Neonatal ICUs, including vital signs and waveforms.
The objectives of this proposal are (1) automated, validated detection of immature breathing patterns
by teams of clinicians and mathematicians, and (2) a Leadership and Data Coordination Center
(LDCC) for this NIH cooperative agreement to study a prospective observational cohort. The central
hypothesis is that quantification of immature breathing will identify physiological biomarkers that can
serve as targets for prevention and treatment that improve outcomes. A proposed multicenter protocol
has Aims 1 and 2 to develop predictive models for immature breathing, and to relate them to clinically
significant respiratory outcomes. The proposed LDCC builds on the experience of this university in
successful completion of the heart rate characteristics monitoring trial, the largest RCT in premature
infants, NIH-funded and completed on time and on budget. The computing requirements will be met by
a new University of Virginia Center and in concert with our partners Lawrence Livermore National
Laboratory and Intel Corporation. We will isolate and store DNA in our Biorepository and Tissue
Research Facility, and manage sites with our Clinical Trials Office. Large-scale computing clusters
dedicated for this work are in daily use. The contributions are expected to be (1) computational tools
for prediction of respiratory outcomes, and (2) effective LDCC performance in data management,
computational modeling, biorepository, and clinical studies management. The proposed research will
be significant because it is the first step in programs for better therapies and preventive measures for
chronic lung disease in premature infants. The proposed advanced analysis of monitoring data is
innovative because of the cutting edge solutions to advanced computing and data security that may
also inform other NIH multicenter studies of Big Data.

## Key facts

- **NIH application ID:** 10140480
- **Project number:** 3U01HL133708-05S1
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** DOUGLAS E LAKE
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,963
- **Award type:** 3
- **Project period:** 2016-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10140480

## Citation

> US National Institutes of Health, RePORTER application 10140480, Prematurity-Related Ventilatory Control: Leadership Data and Coordination Center (LDCC) (3U01HL133708-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10140480. Licensed CC0.

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