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Thought Leader Session

Guidelines Session

Aging Adult Track

Additional Registration Fee

Research Snapshot Theater: Quality and Patient Safety/Epidemiology - Outcomes, Adult

Development of an Electronic Health Record Phenotype for Spontaneous Awakening and Breathing Trials

Sunday, March 22, 2026

11:00 AM - 12:00 PM Central Time

Location: Connections Central - RST 11

Snigdha Jain, MD (she/her/hers)

Yale Medicine, Connecticut

Disclosure information not submitted.
VC

Vaishvik Chaudhari, MS

Data Scientist
Rush University Medical Center

Disclosure information not submitted.
KC

Kaveri Chhikara, MS

Data Scientist
University of Chicago

Disclosure information not submitted.
BP

Bhakti Patel, MD

Assistant Professor
University of Chicago Medical Center

Disclosure information not submitted.
Catherine Gao, MD MS (she/her/hers)

Northwestern University

Disclosure(s): No relevant financial relationship(s) to disclose.
AB

Anna Barker, MD

Clinical Instructor
University of Michigan Hospital and Health Systems, Michigan

Disclosure information not submitted.
CH

Chad Hochberg, MD

Assistant Professor
Johns Hopkins Hospital

Disclosure information not submitted.
NI

Nicholas Ingraham, MD

Assistant Professor
University of Minnesota

Disclosure information not submitted.
WP

William Parker, MD

Associate Professor
University of Chicago

Disclosure information not submitted.
Juan C. Rojas, MD (he/him/his)

Illinois

Disclosure(s): No relevant financial relationship(s) to disclose.

Introduction: Spontaneous awakening trials (SATs) and spontaneous breathing trials (SBTs) improve patient outcomes. However, measurement of their delivery in the electronic health record (EHR) depends on clinician documentation. We aimed to develop rule-based algorithms using structured EHR data to automate the detection of SAT and SBT delivery.

Methods: We identified mechanically ventilated adults at 13 US hospitals across 5 health systems in the Common Longitudinal Intensive Care Unit Format (CLIF) consortium from January 1st, 2020, to December 31, 2022. SAT eligibility was defined on days with mechanical ventilation, no paralytics, and ≥4 hours of continuous sedatives or opiates use from 10 PM prior day to 6 AM current day. SBT eligibility required ≥12 hours of controlled ventilation and ≥2 hours of stable hemodynamics and respiratory status (FiO2 ≤50%, PEEP ≤8, SpO2 ≥88%, norepinephrine or equivalent ≤0.2 mcg/kg/min). SAT delivery was defined as ≥30 minutes off sedatives and opiates and RASS≥0 within 45 minutes. SBT delivery was defined as transition to support mode (pressure support and PEEP ≤8 or CPAP ≤5 cm H₂O) for ≥2 minutes. Algorithm performance was evaluated at CLIF sites with available clinician flowsheet documentation, using accuracy, precision, recall, and specificity.

Results: Among 68,862 mechanical ventilation encounters [mean age 61 years (SD 16); 41% female], 102,172 patient-ventilator days were eligible for SAT detection. The SAT algorithm identified delivery on a median 17% (IQR 16%–20%) of eligible days, with moderate concordance against flowsheets: accuracy 0.59 (IQR 0.50–0.63), precision 0.61 (0.36–0.83), recall 0.23 (0.20–0.27), and specificity 0.89 (0.84–0.93). Of 159,715 SBT-eligible days, the algorithm identified SBTs in 34% (IQR 30%–41%), with stronger concordance: accuracy 0.72 (0.44–0.80), precision 0.44 (0.23–0.69), recall 0.80 (0.69–0.85), and specificity 0.76 (0.25–0.89).

Conclusions: Structured EHR data can be used to detect SAT and SBT delivery via rule-based algorithms. Variable concordance with flowsheet documentation may reflect differences in documentation practices or clinical performance of SATs and SBTs. These findings support the use of algorithmic surveillance in detection of SATs and SBTs.