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Wittenstein J, Scharffenberg M, Fröhlich J, Rothmann C, Ran X, Zhang Y, Chai Y, Yang X, Müller S, Koch T, Huhle R, Gama de Abreu M. Effects of Positive End-expiratory Pressure on Pulmonary Perfusion Distribution and Intrapulmonary Shunt during One-lung Ventilation in Pigs: A Randomized Crossover Study. Anesthesiology 2024; 141:44-55. [PMID: 38625679 DOI: 10.1097/aln.0000000000005014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
BACKGROUND During one-lung ventilation (OLV), positive end-expiratory pressure (PEEP) can improve lung aeration but might overdistend lung units and increase intrapulmonary shunt. The authors hypothesized that higher PEEP shifts pulmonary perfusion from the ventilated to the nonventilated lung, resulting in a U-shaped relationship with intrapulmonary shunt during OLV. METHODS In nine anesthetized female pigs, a thoracotomy was performed and intravenous lipopolysaccharide infused to mimic the inflammatory response of thoracic surgery. Animals underwent OLV in supine position with PEEP of 0 cm H2O, 5 cm H2O, titrated to best respiratory system compliance, and 15 cm H2O (PEEP0, PEEP5, PEEPtitr, and PEEP15, respectively, 45 min each, Latin square sequence). Respiratory, hemodynamic, and gas exchange variables were measured. The distributions of perfusion and ventilation were determined by IV fluorescent microspheres and computed tomography, respectively. RESULTS Compared to two-lung ventilation, the driving pressure increased with OLV, irrespective of the PEEP level. During OLV, cardiac output was lower at PEEP15 (5.5 ± 1.5 l/min) than PEEP0 (7.6 ± 3 l/min) and PEEP5 (7.4 ± 2.9 l/min; P = 0.004), while the intrapulmonary shunt was highest at PEEP0 (PEEP0: 48.1% ± 14.4%; PEEP5: 42.4% ± 14.8%; PEEPtitr: 37.8% ± 11.0%; PEEP15: 39.0% ± 10.7%; P = 0.027). The relative perfusion of the ventilated lung did not differ among PEEP levels (PEEP0: 65.0% ± 10.6%; PEEP5: 68.7% ± 8.7%; PEEPtitr: 68.2% ± 10.5%; PEEP15: 58.4% ± 12.8%; P = 0.096), but the centers of relative perfusion and ventilation in the ventilated lung shifted from ventral to dorsal and from cranial to caudal zones with increasing PEEP. CONCLUSIONS In this experimental model of thoracic surgery, higher PEEP during OLV did not shift the perfusion from the ventilated to the nonventilated lung, thus not increasing intrapulmonary shunt. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Jakob Wittenstein
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Martin Scharffenberg
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Jonathan Fröhlich
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Carolin Rothmann
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Xi Ran
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany; Department of Intensive Care, Chongqing General Hospital, University of Chinese Academy of Science, Chongqing, China
| | - Yingying Zhang
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany; Department of Anesthesiology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yusen Chai
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Xiuli Yang
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Sabine Müller
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Thea Koch
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Robert Huhle
- Department of Anesthesiology and Intensive Care Medicine, Pulmonary Engineering Group, University Hospital Carl Gustav Carus Dresden at Technische Universität Dresden, Dresden, Germany
| | - Marcelo Gama de Abreu
- Department of Intensive Care and Resuscitation, Department of Outcomes Research, and Department of Cardiothoracic Anesthesia, Anesthesiology Institute, Cleveland Clinic, Cleveland, Ohio
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Penarrubia L, Verstraete A, Orkisz M, Davila E, Boussel L, Yonis H, Mezidi M, Dhelft F, Danjou W, Bazzani A, Sigaud F, Bayat S, Terzi N, Girard M, Bitker L, Roux E, Richard JC. Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS. Intensive Care Med Exp 2023; 11:8. [PMID: 36797424 PMCID: PMC9934943 DOI: 10.1186/s40635-023-00495-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/24/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Assessing measurement error in alveolar recruitment on computed tomography (CT) is of paramount importance to select a reliable threshold identifying patients with high potential for alveolar recruitment and to rationalize positive end-expiratory pressure (PEEP) setting in acute respiratory distress syndrome (ARDS). The aim of this study was to assess both intra- and inter-observer smallest real difference (SRD) exceeding measurement error of recruitment using both human and machine learning-made lung segmentation (i.e., delineation) on CT. This single-center observational study was performed on adult ARDS patients. CT were acquired at end-expiration and end-inspiration at the PEEP level selected by clinicians, and at end-expiration at PEEP 5 and 15 cmH2O. Two human observers and a machine learning algorithm performed lung segmentation. Recruitment was computed as the weight change of the non-aerated compartment on CT between PEEP 5 and 15 cmH2O. RESULTS Thirteen patients were included, of whom 11 (85%) presented a severe ARDS. Intra- and inter-observer measurements of recruitment were virtually unbiased, with 95% confidence intervals (CI95%) encompassing zero. The intra-observer SRD of recruitment amounted to 3.5 [CI95% 2.4-5.2]% of lung weight. The human-human inter-observer SRD of recruitment was slightly higher amounting to 5.7 [CI95% 4.0-8.0]% of lung weight, as was the human-machine SRD (5.9 [CI95% 4.3-7.8]% of lung weight). Regarding other CT measurements, both intra-observer and inter-observer SRD were close to zero for the CT-measurements focusing on aerated lung (end-expiratory lung volume, hyperinflation), and higher for the CT-measurements relying on accurate segmentation of the non-aerated lung (lung weight, tidal recruitment…). The average symmetric surface distance between lung segmentation masks was significatively lower in intra-observer comparisons (0.8 mm [interquartile range (IQR) 0.6-0.9]) as compared to human-human (1.0 mm [IQR 0.8-1.3] and human-machine inter-observer comparisons (1.1 mm [IQR 0.9-1.3]). CONCLUSIONS The SRD exceeding intra-observer experimental error in the measurement of alveolar recruitment may be conservatively set to 5% (i.e., the upper value of the CI95%). Human-machine and human-human inter-observer measurement errors with CT are of similar magnitude, suggesting that machine learning segmentation algorithms are credible alternative to humans for quantifying alveolar recruitment on CT.
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Affiliation(s)
- Ludmilla Penarrubia
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France
| | - Aude Verstraete
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Maciej Orkisz
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France
| | - Eduardo Davila
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France
| | - Loic Boussel
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France ,grid.413852.90000 0001 2163 3825Service de Radiologie, Hôpital De La Croix Rousse, Hospices Civils de Lyon, Lyon, France
| | - Hodane Yonis
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Mehdi Mezidi
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Francois Dhelft
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France ,grid.7849.20000 0001 2150 7757Université de Lyon, Université Claude Bernard Lyon 1, Villeurbanne, France
| | - William Danjou
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Alwin Bazzani
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Florian Sigaud
- grid.410529.b0000 0001 0792 4829Service de Médecine-Intensive Réanimation, CHU Grenoble-Alpes, Grenoble, France
| | - Sam Bayat
- grid.450307.50000 0001 0944 2786Synchrotron Radiation for Biomedicine Laboratory (STROBE), INSERM UA07, Univ. Grenoble Alpes, Grenoble, France ,grid.410529.b0000 0001 0792 4829Department of Pulmonology and Physiology, Grenoble University Hospital, Grenoble, France
| | - Nicolas Terzi
- grid.411154.40000 0001 2175 0984Maladies Infectieuses et Réanimation Médicale, CHU Rennes, Rennes, France ,grid.410368.80000 0001 2191 9284Faculté de Médecine, Biosit, Université Rennes1, Rennes, France ,grid.410368.80000 0001 2191 9284INSERM-CIC-1414, Faculté de Médecine, IFR 140, Université Rennes I, Rennes, France
| | - Mehdi Girard
- grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Laurent Bitker
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France ,grid.413852.90000 0001 2163 3825Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004 Lyon, France
| | - Emmanuel Roux
- grid.7849.20000 0001 2150 7757Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France
| | - Jean-Christophe Richard
- Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, INSERM, CREATIS UMR 5220, U1294, Université de Lyon, Villeurbanne, France. .,Service de Médecine Intensive Réanimation, Hôpital de la Croix Rousse, Hospices Civils de Lyon, 103 Grande Rue de La Croix Rousse, 69004, Lyon, France.
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