Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine..

To develop and characterize a machine learning algorithm to discriminate acute respiratory distress syndrome from other causes of respiratory failure using only ventilator waveform data.

DESIGN: Retrospective, observational cohort study.

SETTING: Academic medical center ICU.

PATIENTS: Adults admitted to the ICU requiring invasive mechanical ventilation, including 50 patients with acute respiratory distress syndrome and 50 patients with primary indications for mechanical ventilation other than hypoxemic respiratory failure.

INTERVENTIONS: None.

MEASUREMENTS AND MAIN RESULTS: Pressure and flow time series data from mechanical ventilation during the first 24-hours after meeting acute respiratory distress syndrome criteria (or first 24-hr of mechanical ventilation for non-acute respiratory distress syndrome patients) were processed to extract nine physiologic features. A random forest machine learning algorithm was trained to discriminate between the patients with and without acute respiratory distress syndrome. Model performance was assessed using the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Analyses examined performance when the model was trained using data from the first 24 hours and tested using withheld data from either the first 24 hours (24/24 model) or 6 hours (24/6 model). Area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.88, 0.90, 0.71, 0.77, and 0.90 (24/24); and 0.89, 0.90, 0.75, 0.83, and 0.83 (24/6).

CONCLUSIONS: Use of machine learning and physiologic information derived from raw ventilator waveform data may enable acute respiratory distress syndrome screening at early time points after intubation. This approach, combined with traditional diagnostic criteria, could improve timely acute respiratory distress syndrome recognition and enable automated clinical decision support, especially in settings with limited availability of conventional diagnostic tests and electronic health records.

Medienart:	E-Artikel

Erscheinungsjahr:	2021
Erschienen:	2021

Enthalten in:	Zur Gesamtaufnahme - volume:3
Enthalten in:	Critical care explorations - 3(2021), 1 vom: 08. Jan., Seite e0313

Sprache:	Englisch

Beteiligte Personen:	Rehm, Gregory B [VerfasserIn] Cortés-Puch, Irene [VerfasserIn] Kuhn, Brooks T [VerfasserIn] Nguyen, Jimmy [VerfasserIn] Fazio, Sarina A [VerfasserIn] Johnson, Michael A [VerfasserIn] Anderson, Nicholas R [VerfasserIn] Chuah, Chen-Nee [VerfasserIn] Adams, Jason Y [VerfasserIn]

Links:	Volltext

Themen:	Acute respiratory distress syndrome Classification Critical care Journal Article Mechanical ventilation Population surveillance Respiratory failure

Anmerkungen:	Date Revised 20.04.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE

doi:	10.1097/CCE.0000000000000313

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM320212904