# Predicting earliest safe extubation time in pediatric patients

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $2,500

## Abstract

PROJECT SUMMARY
Determining when to extubate patients in the pediatric intensive care unit (PICU) is a challenge clinicians face
each day. Consensus guidelines for pediatric extubation are lacking and, in light of this, most pediatric studies
conclude that the decision to extubate relies ultimately on clinician judgment. The resulting variation in care
translates to increased morbidity, mortality, and costs that arise from both unnecessary ventilator days from
delayed extubation and re-intubation from extubation failure. The long-term goal of this project is to harness
the power of artificial intelligence to optimize identification of extubation readiness in the PICU. The objective
of this proposal is to create machine learning models using a large electronic health record (EHR)
dataset to predict when to extubate patients and to estimate how many ventilator days could be saved
if such models were used in practice. Deploying such models in the EHR as a real-time decision support
tool could safely shorten extubation times by decreasing variation in care and identifying subsets of patients for
earlier, safe extubation. This study will use EHR data from mechanically ventilated PICU patients at the
University of California, San Francisco to build models to estimate extubation readiness for PICU patients (Aim
1). The investigators will apply human factor design principles, which aim to increase usefulness of tools and
help humans do their jobs with higher reliability, to improve model performance. We will use a novel method,
expert-augmented machine learning, to incorporate clinician knowledge directly into our models (Aim 2). The
performance of the models will be evaluated with standard metrics, as well as with an estimate of number of
ventilator days saved, reflecting the potential health impact (Aim 3). This project will advance extubation
practices for critically ill children, yielding a predictive tool ready for prospective testing in the EHR that moves
toward delivering high reliability healthcare for patients with respiratory failure. This research will advance
NHLBI's mission of using data science to improve treatment of patients with lung diseases. The proposed
training, guided by an expert mentorship team, will enrich the applicant's knowledge of and skills in data
science, machine learning and prediction, and clinical informatics. The content expertise, research
competency, and training in quantitative methods the applicant will receive will prepare her well to improve
scientific knowledge and clinical practice in her career as an independent researcher.

## Key facts

- **NIH application ID:** 10832789
- **Project number:** 3F31HL156498-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Jean Digitale
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $2,500
- **Award type:** 3
- **Project period:** 2021-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10832789

## Citation

> US National Institutes of Health, RePORTER application 10832789, Predicting earliest safe extubation time in pediatric patients (3F31HL156498-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10832789. Licensed CC0.

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