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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2023 · $732,017

## 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.

## Key facts

- **NIH application ID:** 10573201
- **Project number:** 5R01HL157985-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Atul Malhotra
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $732,017
- **Award type:** 5
- **Project period:** 2022-02-15 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10573201

## Citation

> US National Institutes of Health, RePORTER application 10573201, VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19 (5R01HL157985-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10573201. Licensed CC0.

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