Waveform data capture substantial variation in tidal volume and other respiratory parameters

RATIONALE: Patients receiving invasive mechanical ventilation (IMV) require accurate assessments of ventilator parameters. Documentation of these parameters in standard practice may fail to capture meaningful variation due to intermittent missingness.

OBJECTIVES: To assess variation in continuously-measured ventilator parameters and agreement with measurements documented as part of routine care in the electronic health record (EHR).

METHODS: We performed a retrospective cohort study of patients receiving IMV in a medical intensive care unit from November 2024—March 2025. We compared the observed tidal volume, minute ventilation, peak inspiratory pressure, and positive end-expiratory pressure, measured continuously from device waveforms with intermittent EHR documentation. We calculated descriptive statistics and measures of agreement between these sources.

MEASUREMENTS AND MAIN RESULTS: For 59 encounters, the median age was 65 (IQR, 59–72), 33 (56%) patients were male and 17 (29%) were Black. 34 (58%) patients died or were discharged to hospice. Among 358 patient-days of data, continuous measurements captured significantly more variation than EHR-documented measurements across all parameters. The largest errors were in observed tidal volume (mean absolute error 69 mL, 95% CI, 62–77 mL; correlation coefficient 0.540). Agreement in tidal volume was worse among patients receiving mandatory modes of ventilation (correlation coefficient 0.454).

CONCLUSIONS: Intermittent measurement of ventilator parameters fails to capture large variability observed in continuous, waveform-derived measurements. Poor agreement in parameters like tidal volume, even in mandatory modes of ventilation, highlights the potential for ventilator waveform data to improve care and advance research for patients with acute respiratory distress syndrome and others receiving IMV.

Emily E Moin, Rachel M Bennett, Alexander T Moffett, Benjamin E Schmid, Kevin Long, Nicholas J Seewald, John P Reilly, Gary E Weissman