Critical care medicine · 2026
Artificial Intelligence Algorithm to Monitor Inspiratory Muscle Effort and Patient-Ventilator Dyssynchrony During Mechanical Ventilation.
СтатьяНазвание и аннотация: Перевод в очереди
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Plens Glauco M, Morais Caio César Araújo, Gregol Thaís, Colpani Paula Breda, Alcala Glasiele C, Pacheco Éder, Xia Yu Hao Wang, Dos Santos Ana Carolina, Malbouisson Luiz Marcelo, Brochard Laurent, Kassis Elias Baedorf, Goligher Ewan C, Carvalho Carlos Roberto Ribeiro de, Amato Marcelo Britto Passos, Costa Eduardo Leite Vieira
Аннотация
OBJECTIVE: Current methods for estimating inspiratory muscle pressure ( Pmus ) during mechanical ventilation are either invasive or dependent on occlusion maneuvers. A noninvasive artificial intelligence (AI) algorithm estimating in real-time the amplitude and timing of Pmus , enabling continuous monitoring of patient effort, driving pressure, and synchrony with the ventilator was designed, and its performance was evaluated against the gold standard obtained with esophageal manometry ( Pmus,es ).
DESIGN: A prospective diagnostic accuracy study.
SETTING: Two ICUs from the University of São Paulo, Brazil.
PATIENTS: Adult patients under pressure support ventilation.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Pmus estimated using AI ( Pmus,AI ) was compared with Pmus,es and to values derived from occlusion maneuvers, the pressure muscle index and the occlusion pressure ( Pocc ). Automatic detection of dyssynchronies based on Pmus,AI was compared with experts' classification. A total of 48 participants with 4918 cycles were analyzed. Pmus,es varied from 1.0 to 28.4 cm H 2 O. Pmus,AI showed a bias of 0.9 cm H 2 O, 95% limits of agreement -5.1, 6.9 cm H 2 O and detected extreme values of both Pmus,es and dynamic driving pressure with area under the receiver operating characteristic curve greater than 0.8. Pmus,AI accuracy was comparable to occlusion-based techniques. Sensitivity and specificity to detect ineffective effort, autotriggering or reverse triggering were 86.5% and 77.4%, respectively.
CONCLUSIONS: AI presented good performance in detecting high and low Pmus , and allowed the automatic detection of specific types of dyssynchronies. This novel noninvasive method was comparable to intermittent techniques requiring occlusion maneuvers.
DESIGN: A prospective diagnostic accuracy study.
SETTING: Two ICUs from the University of São Paulo, Brazil.
PATIENTS: Adult patients under pressure support ventilation.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Pmus estimated using AI ( Pmus,AI ) was compared with Pmus,es and to values derived from occlusion maneuvers, the pressure muscle index and the occlusion pressure ( Pocc ). Automatic detection of dyssynchronies based on Pmus,AI was compared with experts' classification. A total of 48 participants with 4918 cycles were analyzed. Pmus,es varied from 1.0 to 28.4 cm H 2 O. Pmus,AI showed a bias of 0.9 cm H 2 O, 95% limits of agreement -5.1, 6.9 cm H 2 O and detected extreme values of both Pmus,es and dynamic driving pressure with area under the receiver operating characteristic curve greater than 0.8. Pmus,AI accuracy was comparable to occlusion-based techniques. Sensitivity and specificity to detect ineffective effort, autotriggering or reverse triggering were 86.5% and 77.4%, respectively.
CONCLUSIONS: AI presented good performance in detecting high and low Pmus , and allowed the automatic detection of specific types of dyssynchronies. This novel noninvasive method was comparable to intermittent techniques requiring occlusion maneuvers.