Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M-mode or artificial intelligence: a prospective study on mechanically ventilated patients.

Critical care Inferior vena cava Subcostal Transhepatic Ultrasound

Journal

Intensive care medicine experimental
ISSN: 2197-425X
Titre abrégé: Intensive Care Med Exp
Pays: Germany
ID NLM: 101645149

Informations de publication

Date de publication:
10 Jul 2023
Historique:
received: 17 03 2023
accepted: 03 06 2023
medline: 10 7 2023
pubmed: 10 7 2023
entrez: 9 7 2023
Statut: epublish

Résumé

Variation of inferior vena cava (IVC) is used to predict fluid-responsiveness, but the IVC visualization with standard sagittal approach (SC, subcostal) cannot be always achieved. In such cases, coronal trans-hepatic (TH) window may offer an alternative, but the interchangeability of IVC measurements in SC and TH is not fully established. Furthermore, artificial intelligence (AI) with automated border detection may be of clinical value but it needs validation. Prospective observational validation study in mechanically ventilated patients with pressure-controlled mode. Primary outcome was the IVC distensibility (IVC-DI) in SC and TH imaging, with measurements taken both in M-Mode or with AI software. We calculated mean bias, limits of agreement (LoA), and intra-class correlation (ICC) coefficient. Thirty-three patients were included. Feasibility rate was 87.9% and 81.8% for SC and TH visualization, respectively. Comparing imaging from the same anatomical site acquired with different modalities (M-Mode vs AI), we found the following IVC-DI differences: (1) SC: mean bias - 3.1%, LoA [- 20.1; 13.9], ICC = 0.65; (2) TH: mean bias - 2.0%, LoA [- 19.3; 15.4], ICC = 0.65. When comparing the results obtained from the same modality but from different sites (SC vs TH), IVC-DI differences were: (3) M-Mode: mean bias 1.1%, LoA [- 6.9; 9.1], ICC = 0.54; (4) AI: mean bias 2.0%, LoA [- 25.7; 29.7], ICC = 0.32. In patients mechanically ventilated, AI software shows good accuracy (modest overestimation) and moderate correlation as compared to M-mode assessment of IVC-DI, both for SC and TH windows. However, precision seems suboptimal with wide LoA. The comparison of M-Mode or AI between different sites yields similar results but with weaker correlation. Trial registration Reference protocol: 53/2022/PO, approved on 21/03/2022.

Sections du résumé

BACKGROUND BACKGROUND
Variation of inferior vena cava (IVC) is used to predict fluid-responsiveness, but the IVC visualization with standard sagittal approach (SC, subcostal) cannot be always achieved. In such cases, coronal trans-hepatic (TH) window may offer an alternative, but the interchangeability of IVC measurements in SC and TH is not fully established. Furthermore, artificial intelligence (AI) with automated border detection may be of clinical value but it needs validation.
METHODS METHODS
Prospective observational validation study in mechanically ventilated patients with pressure-controlled mode. Primary outcome was the IVC distensibility (IVC-DI) in SC and TH imaging, with measurements taken both in M-Mode or with AI software. We calculated mean bias, limits of agreement (LoA), and intra-class correlation (ICC) coefficient.
RESULTS RESULTS
Thirty-three patients were included. Feasibility rate was 87.9% and 81.8% for SC and TH visualization, respectively. Comparing imaging from the same anatomical site acquired with different modalities (M-Mode vs AI), we found the following IVC-DI differences: (1) SC: mean bias - 3.1%, LoA [- 20.1; 13.9], ICC = 0.65; (2) TH: mean bias - 2.0%, LoA [- 19.3; 15.4], ICC = 0.65. When comparing the results obtained from the same modality but from different sites (SC vs TH), IVC-DI differences were: (3) M-Mode: mean bias 1.1%, LoA [- 6.9; 9.1], ICC = 0.54; (4) AI: mean bias 2.0%, LoA [- 25.7; 29.7], ICC = 0.32.
CONCLUSIONS CONCLUSIONS
In patients mechanically ventilated, AI software shows good accuracy (modest overestimation) and moderate correlation as compared to M-mode assessment of IVC-DI, both for SC and TH windows. However, precision seems suboptimal with wide LoA. The comparison of M-Mode or AI between different sites yields similar results but with weaker correlation. Trial registration Reference protocol: 53/2022/PO, approved on 21/03/2022.

Identifiants

pubmed: 37423948
doi: 10.1186/s40635-023-00529-z
pii: 10.1186/s40635-023-00529-z
pmc: PMC10329966
doi:

Types de publication

Journal Article

Langues

eng

Pagination

40

Informations de copyright

© 2023. The Author(s).

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Auteurs

Filippo Sanfilippo (F)

Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy. filipposanfi@yahoo.it.
School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy. filipposanfi@yahoo.it.

Luigi La Via (L)

Department of Anaesthesia and Intensive Care, A.O.U. Policlinico-San Marco, Site "Policlinico G. Rodolico", Via S. Sofia N 78, 95123, Catania, Italy.
School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy.

Veronica Dezio (V)

School of Anaesthesia and Intensive Care, University Hospital "G. Rodolico", University of Catania, 95123, Catania, Italy.

Paolo Amelio (P)

School of Anaesthesia and Intensive Care, University "Magna Graecia", Catanzaro, Italy.

Giulio Genoese (G)

Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy.

Federico Franchi (F)

Anesthesia and Intensive Care Unit, University Hospital of Siena, University of Siena, Siena, Italy.

Antonio Messina (A)

Humanitas Clinical and Research Center, IRCCS, Milan, Italy.
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, MI, Italy.

Chiara Robba (C)

Department of Surgical Science and Diagnostic Integrated, University of Genoa, Genoa, Italy.
IRCCS Ospedale Policlinico San Martino, Genoa, Italy.

Alberto Noto (A)

Department of Human Pathology of the Adult and Evolutive Age "Gaetano Barresi", Division of Anesthesia and Intensive Care, University of Messina, Policlinico "G. Martino", Messina, Italy.

Classifications MeSH