SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F25%3A43928601" target="_blank" >RIV/00064173:_____/25:43928601 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21230/25:00384159 RIV/68407700:21730/25:00384159 RIV/00216208:11120/25:43928601

Result on the web

<a href="https://doi.org/10.1016/j.cmpb.2025.108927" target="_blank" >https://doi.org/10.1016/j.cmpb.2025.108927</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cmpb.2025.108927" target="_blank" >10.1016/j.cmpb.2025.108927</a>

Result language

angličtina

Original language name

SmartAlert: Machine learning-based patient-ventilator asynchrony detection system in intensive care units

Original language description

BACKGROUND AND OBJECTIVE: Patient-ventilator asynchronies (PVA) are associated with ventilator-induced lung injury and increased mortality. Current detection methods rely on static thresholds, extensive preprocessing, or proprietary ventilator data. This study aimed to develop and validate a fully online, real-time system that detects and classifies PVAs directly from ventilator screen data while alerting clinicians based on severity. METHODS: The SmartAlert system was developed using ventilator screen recordings from ICU patients. It extracts pressure and flow waveforms from video recordings, converts them into time-series data, and employs deep neural networks to classify asynchronies and assign alarm levels from no urgency to most urgent. A dataset of 381,280 double-breath units was independently annotated by two expert intensivists. Two deep learning models were trained: one for alarm prediction and another for asynchrony classification (ineffective triggering, double cycling, high inspiratory effort, no asynchrony). Performance was evaluated using accuracy, sensitivity, specificity, and AUC-ROC, compared to expert consensus. RESULTS: SmartAlert demonstrated strong performance for alarm level prediction (overall accuracy: 83.8 %, weighted AUC-ROC: 0.943 [95 % CI: 0.941-0.945]) and PVA classification (weighted accuracy: 89.3 %, weighted AUC-ROC: 0.951 [95 % CI: 0.950-0.953]). It showed high specificity for urgent alarms (99.9 % for level 3) and PVA types (98.5 % for ineffective triggering, 96.9 % for double cycling, 94.8 % for high inspiratory effort). CONCLUSIONS: We developed and internally validated SmartAlert, an automated system that detects PVAs, classifies severity, and alerts clinicians in real time. Its potential to reduce alarm fatigue, optimize ventilator settings, and improve patient outcomes remains to be tested in clinical trials.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30221 - Critical care medicine and Emergency medicine

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computer Methods and Programs in Biomedicine

ISSN

0169-2607

e-ISSN

1872-7565

Volume of the periodical

269

Issue of the periodical within the volume

September

Country of publishing house

IE - IRELAND

Number of pages

11

Pages from-to

108927

UT code for WoS article

001525899100001

EID of the result in the Scopus database

2-s2.0-105009237320