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Perchiazzi G, Kawati R, Pellegrini M, Liangpansakul J, Colella R, Bollella P, Rangaiah P, Cannone A, Venkataramana DH, Perez M, Stramaglia S, Torsi L, Bellotti R, Augustine R. Imitating the respiratory activity of the brain stem by using artificial neural networks: exploratory study on an animal model of lactic acidosis and proof of concept. J Clin Monit Comput 2024:10.1007/s10877-024-01208-4. [PMID: 39162839 DOI: 10.1007/s10877-024-01208-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 08/07/2024] [Indexed: 08/21/2024]
Abstract
Artificial neural networks (ANNs) are versatile tools capable of learning without prior knowledge. This study aims to evaluate whether ANN can calculate minute volume during spontaneous breathing after being trained using data from an animal model of metabolic acidosis. Data was collected from ten anesthetized, spontaneously breathing pigs divided randomly into two groups, one without dead space and the other with dead space at the beginning of the experiment. Each group underwent two equal sequences of pH lowering with pre-defined targets by continuous infusion of lactic acid. The inputs to ANNs were pH, ΔPaCO2 (variation of the arterial partial pressure of CO2), PaO2, and blood temperature which were sampled from the animal model. The output was the delta minute volume (ΔVM), (the change of minute volume as compared to the minute volume the animal had at the beginning of the experiment). The ANN performance was analyzed using mean squared error (MSE), linear regression, and the Bland-Altman (B-A) method. The animal experiment provided the necessary data to train the ANN. The best architecture of ANN had 17 intermediate neurons; the best performance of the finally trained ANN had a linear regression with R2 of 0.99, an MSE of 0.001 [L/min], a B-A analysis with bias ± standard deviation of 0.006 ± 0.039 [L/min]. ANNs can accurately estimate ΔVM using the same information that arrives at the respiratory centers. This performance makes them a promising component for the future development of closed-loop artificial ventilators.
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Affiliation(s)
- Gaetano Perchiazzi
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden.
- Hedenstierna Laboratoriet, Akademiska sjukhuset ing 40 3 tr, Uppsala, 75185, Sweden.
| | - Rafael Kawati
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Mariangela Pellegrini
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Department of Anesthesia, Operation and Intensive Care, Uppsala University Hospital, Uppsala, Sweden
| | - Jasmine Liangpansakul
- The Hedenstierna Laboratory, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | | | - Paolo Bollella
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Pramod Rangaiah
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Annamaria Cannone
- Department of Anaesthesia and Intensive Care, "Madonna delle Grazie" Hospital, Matera, Italy
| | | | - Mauricio Perez
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
| | - Sebastiano Stramaglia
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Luisa Torsi
- Department of Chemistry, University of Bari Aldo Moro, Bari, Italy
| | - Roberto Bellotti
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari, Rome, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
| | - Robin Augustine
- Department of Electrical Engineering, Solid-State Electronics, Uppsala University, Uppsala, Sweden
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Nicolotti D, Grossi S, Nicolini F, Gallingani A, Rossi S. Difficult Respiratory Weaning after Cardiac Surgery: A Narrative Review. J Clin Med 2023; 12:jcm12020497. [PMID: 36675426 PMCID: PMC9867514 DOI: 10.3390/jcm12020497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Respiratory weaning after cardiac surgery can be difficult or prolonged in up to 22.7% of patients. The inability to wean from a ventilator within the first 48 h after surgery is related to increased short- and long-term morbidity and mortality. Risk factors are mainly non-modifiable and include preoperative renal failure, New York Heart Association, and Canadian Cardiac Society classes as well as surgery and cardio-pulmonary bypass time. The positive effects of pressure ventilation on the cardiovascular system progressively fade during the progression of weaning, possibly leading to pulmonary oedema and failure of spontaneous breathing trials. To prevent this scenario, some parameters such as pulmonary artery occlusion pressure, echography-assessed diastolic function, brain-derived natriuretic peptide, and extravascular lung water can be monitored during weaning to early detect hemodynamic decompensation. Tracheostomy is considered for patients with difficult and prolonged weaning. In such cases, optimal patient selection, timing, and technique may be important to try to reduce morbidity and mortality in this high-risk population.
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Affiliation(s)
- Davide Nicolotti
- Department of Anesthesia and Intensive Care Medicine, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, 43126 Parma, Italy
- Correspondence: ; Tel.: +39-0521-703286
| | - Silvia Grossi
- Department of Anesthesia and Intensive Care Medicine, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, 43126 Parma, Italy
| | - Francesco Nicolini
- Department of Cardiac Surgery, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, 43126 Parma, Italy
| | - Alan Gallingani
- Department of Cardiac Surgery, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, 43126 Parma, Italy
| | - Sandra Rossi
- Department of Anesthesia and Intensive Care Medicine, Azienda Ospedaliero-Universitaria di Parma, Via Gramsci 14, 43126 Parma, Italy
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