Wednesday, February 5, 2025 2/5/2025

VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19

Award Number: R01HL157985
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Group Awards By:

View Award Abstract

Abstract The COVID-19 pandemic has led to massive challenges for health care systems and for global economics. The surge in cases which occurred abruptly strained the existing resources to care for the volume of patients, leading to a shortage of supply in many medications, personnel and equipment. The mechanical ventilator became a particular problem as one newly published study reported that 18 out of 24 patients with COVID-19 in the study (75%) required mechanical ventilation. During the early months of the pandemic many providers decided to intubate early on the assumption that patients would eventually need mechanical ventilation so as to avoid ‘crash intubation’ and potential contamination. A recent observational study of intensive care unit patients with COVID-19 suffering from acute hypoxemic respiratory failure revealed that early invasive mechanical ventilation was associated with an increased risk of day-60 mortality. One central problem in this context was caregivers’ inability to predict which patients may need mechanical ventilation since existing methods using clinical parameters are often subjective and inconsistent across different institutions. We have thus applied machine learning algorithms to commonly available data in electronic health records (EHR) to develop and validate a predictive model for 24-hours ahead prediction of respiratory failure. This novel predictive model has demonstrated AUCs in the range of 0.90-0.94 in our internal and external COVID-19 datasets. That is, we have a robust ability now to predict which patients may need mechanical ventilation and which will not. We are now planning to deploy clinically and to improve iteratively on our model by adding other data streams such as imaging to not only improve our predictive ability but also to make the predictions more ‘actionable’, so that clinicians can pursue timely interventions rather than just being told a prognosis. We are further addressing the many barriers to implementation by addressing ‘clinician buy-in’ which involves making the underlying reasoning of our algorithms more transparent, making the predictions seamlessly integrated into clinical workflow, and finding actionable parameters that will allow both predictions and therapeutic interventions. Such an algorithm will enhance the ability of clinicians to estimate the risk for respiratory failure, and ideally, to anticipate and respond to patient needs in a timely fashion. Moreover, given a long enough prediction horizon (48-72 hours) such systems can facilitate triage and optimization of related resources (ventilators and personnel) within a given hospital and across healthcare systems. Finally, while the COVID-19 pandemic highlighted the need for optimizing the timing of mechanical ventilation, the techniques developed under this proposal are broadly applicable to other causes of respiratory failure and to other types of organ support technologies.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $651,706 )
2025	2025	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	000	4	1/4/2025	NON-COMPETING CONTINUATION	$651,706
														Subtotal = $651,706

Issue Date FY: 2024 ( Subtotal = $709,635 )
2024	2024	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	001	3	6/20/2024	NON-COMPETING CONTINUATION	$57,929
2024	2024	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	000	3	3/1/2024	NON-COMPETING CONTINUATION	$651,706
														Subtotal = $709,635

Issue Date FY: 2023 ( Subtotal = $732,017 )
2023	2023	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	001	2	1/26/2023	NON-COMPETING CONTINUATION	$732,017
2023	2022	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	000	1	1/24/2023	NEW	$0
														Subtotal = $732,017

Issue Date FY: 2022 ( Subtotal = $700,109 )
2022	2022	UNIVERSITY OF CALIFORNIA, SAN DIEGO	9500 GILMAN DR	LA JOLLA	CA	92093	SAN DIEGO	USA	Lung Diseases Research	000	1	2/15/2022	NEW	$700,109
														Subtotal = $700,109

Grand Total All Awards = $2,793,467

Top

All Categories

About

Search

Reports

Data Submission

Award Information

VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19

Award Number: R01HL157985

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

Federal Websites

Department of Health & Human Services

HHS Operating Divisions

HHS Staff Divisions

Download A Document Viewer