| Autor: |
Daniel Oliveira Silva, Patrícia Nery de Souza, Mayson Laercio de Araujo Sousa, Caio Cesar Araujo Morais, Juliana Carvalho Ferreira, Marcelo Alcantara Holanda, Wellington Pereira Yamaguti, Laerte Pastore Junior, Eduardo Leite Vieira Costa |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Critical Care, Vol 27, Iss 1, Pp 1-7 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1364-8535 |
| DOI: |
10.1186/s13054-023-04414-9 |
| Popis: |
Abstract Background Patient-ventilator asynchronies are usually detected by visual inspection of ventilator waveforms but with low sensitivity, even when performed by experts in the field. Recently, estimation of the inspiratory muscle pressure (P mus) waveforms through artificial intelligence algorithm has been proposed (Magnamed®, São Paulo, Brazil). We hypothesized that the display of these waveforms could help healthcare providers identify patient-ventilator asynchronies. Methods A prospective single-center randomized study with parallel assignment was conducted to assess whether the display of the estimated Pmus waveform would improve the correct identification of asynchronies in simulated clinical scenarios. The primary outcome was the mean asynchrony detection rate (sensitivity). Physicians and respiratory therapists who work in intensive care units were randomized to control or intervention group. In both groups, participants analyzed pressure and flow waveforms of 49 different scenarios elaborated using the ASL-5000 lung simulator. In the intervention group the estimated P mus waveform was displayed in addition to pressure and flow waveforms. Results A total of 98 participants were included, 49 per group. The sensitivity per participant in identifying asynchronies was significantly higher in the P mus group (65.8 ± 16.2 vs. 52.94 ± 8.42, p |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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