1
|
Weaver L, Shamohammadi H, Saffaran S, Tonelli R, Laviola M, Laffey JG, Camporota L, Scott TE, Hardman JG, Clini E, Bates DG. Digital Twins of Acute Hypoxemic Respiratory Failure Patients Suggest a Mechanistic Basis for Success and Failure of Noninvasive Ventilation. Crit Care Med 2024; 52:e473-e484. [PMID: 39145711 PMCID: PMC11321607 DOI: 10.1097/ccm.0000000000006337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
OBJECTIVES To clarify the mechanistic basis for the success or failure of noninvasive ventilation (NIV) in acute hypoxemic respiratory failure (AHRF). DESIGN We created digital twins based on mechanistic computational models of individual patients with AHRF. SETTING Interdisciplinary Collaboration in Systems Medicine Research Network. SUBJECTS We used individual patient data from 30 moderate-to-severe AHRF patients who had failed high-flow nasal cannula (HFNC) therapy and subsequently underwent a trial of NIV. INTERVENTIONS Using the digital twins, we evaluated lung mechanics, quantified the separate contributions of external support and patient respiratory effort to lung injury indices, and investigated their relative impact on NIV success or failure. MEASUREMENTS AND MAIN RESULTS In digital twins of patients who successfully completed/failed NIV, after 2 hours of the trial the mean (sd) of the change in total lung stress was -10.9 (6.2)/-0.35 (3.38) cm H2O, mechanical power -13.4 (12.2)/-1.0 (5.4) J/min, and total lung strain 0.02 (0.24)/0.16 (0.30). In the digital twins, positive end-expiratory pressure (PEEP) produced by HFNC was similar to that set during NIV. In digital twins of patients who failed NIV vs. those who succeeded, intrinsic PEEP was 3.5 (0.6) vs. 2.3 (0.8) cm H2O, inspiratory pressure support was 8.3 (5.9) vs. 22.3 (7.2) cm H2O, and tidal volume was 10.9 (1.2) vs. 9.4 (1.8) mL/kg. In digital twins, successful NIV increased respiratory system compliance +25.0 (16.4) mL/cm H2O, lowered inspiratory muscle pressure -9.7 (9.6) cm H2O, and reduced the contribution of patient spontaneous breathing to total driving pressure by 57.0%. CONCLUSIONS In digital twins of AHRF patients, successful NIV improved lung mechanics, lowering respiratory effort and indices associated with lung injury. NIV failed in patients for whom only low levels of positive inspiratory pressure support could be applied without risking patient self-inflicted lung injury due to excessive tidal volumes.
Collapse
Affiliation(s)
- Liam Weaver
- School of Engineering, University of Warwick, Coventry, United Kingdom
| | | | - Sina Saffaran
- School of Engineering, University of Warwick, Coventry, United Kingdom
| | - Roberto Tonelli
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Marianna Laviola
- Anaesthesia and Critical Care, Injury Inflammation and Recovery Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - John G Laffey
- Anaesthesia and Intensive Care Medicine, Galway University Hospitals and School of Medicine, University of Galway, Galway, Ireland
| | - Luigi Camporota
- Intensive Care Medicine, Guy's and St Thomas' NHS Foundation Trust and Division of Asthma Allergy and Lung Biology, King's College London, London, United Kingdom
| | - Timothy E Scott
- Academic Department of Military Anaesthesia and Critical Care, Royal Centre for Defence Medicine, ICT Centre, Birmingham, United Kingdom
| | - Jonathan G Hardman
- Anaesthesia and Critical Care, Injury Inflammation and Recovery Sciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
- Nottingham University Hospitals NHS Trust, Nottingham, United Kingdom
| | - Enrico Clini
- Respiratory Diseases Unit, Department of Medical and Surgical Sciences, University Hospital of Modena, University of Modena and Reggio Emilia, Modena, Italy
| | - Declan G Bates
- School of Engineering, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
2
|
Fu W, Liu X, Guan L, Lin Z, He Z, Niu J, Huang Q, Liu Q, Chen R. Prognostic analysis of high-flow nasal cannula therapy and non-invasive ventilation in mild to moderate hypoxemia patients and construction of a machine learning model for 48-h intubation prediction-a retrospective analysis of the MIMIC database. Front Med (Lausanne) 2024; 11:1213169. [PMID: 38495114 PMCID: PMC10941954 DOI: 10.3389/fmed.2024.1213169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 02/13/2024] [Indexed: 03/19/2024] Open
Abstract
Background This study aims to investigate the clinical outcome between high-flow nasal cannula (HFNC) and non-invasive ventilation (NIV) therapy in mild to moderate hypoxemic patients on the first ICU day and to develop a predictive model of 48-h intubation. Methods The study included adult patients from the MIMIC III and IV databases who first initiated HFNC or NIV therapy due to mild to moderate hypoxemia (100 < PaO2/FiO2 ≤ 300). The 48-h and 30-day intubation rates were compared using cross-sectional and survival analysis. Nine machine learning and six ensemble algorithms were deployed to construct the 48-h intubation predictive models, of which the optimal model was determined by its prediction accuracy. The top 10 risk and protective factors were identified using the Shapley interpretation algorithm. Result A total of 123,042 patients were screened, of which, 673 were from the MIMIC IV database for ventilation therapy comparison (HFNC n = 363, NIV n = 310) and 48-h intubation predictive model construction (training dataset n = 471, internal validation set n = 202) and 408 were from the MIMIC III database for external validation. The NIV group had a lower intubation rate (23.1% vs. 16.1%, p = 0.001), ICU 28-day mortality (18.5% vs. 11.6%, p = 0.014), and in-hospital mortality (19.6% vs. 11.9%, p = 0.007) compared to the HFNC group. Survival analysis showed that the total and 48-h intubation rates were not significantly different. The ensemble AdaBoost decision tree model (internal and external validation set AUROC 0.878, 0.726) had the best predictive accuracy performance. The model Shapley algorithm showed Sequential Organ Failure Assessment (SOFA), acute physiology scores (APSIII), the minimum and maximum lactate value as risk factors for early failure and age, the maximum PaCO2 and PH value, Glasgow Coma Scale (GCS), the minimum PaO2/FiO2 ratio, and PaO2 value as protective factors. Conclusion NIV was associated with lower intubation rate and ICU 28-day and in-hospital mortality. Further survival analysis reinforced that the effect of NIV on the intubation rate might partly be attributed to the other impact factors. The ensemble AdaBoost decision tree model may assist clinicians in making clinical decisions, and early organ function support to improve patients' SOFA, APSIII, GCS, PaCO2, PaO2, PH, PaO2/FiO2 ratio, and lactate values can reduce the early failure rate and improve patient prognosis.
Collapse
Affiliation(s)
- Wei Fu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaoqing Liu
- Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Lili Guan
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zhimin Lin
- Department of Critical Care Medicine, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Zhenfeng He
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jianyi Niu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qiaoyun Huang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qi Liu
- Emergency Intensive Care Department, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, Hena, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| |
Collapse
|
3
|
Mistry S, Scott TE, Jugg B, Perrott R, Saffaran S, Bates DG. An in-silico porcine model of phosgene-induced lung injury predicts clinically relevant benefits from application of continuous positive airway pressure up to 8 h post exposure. Toxicol Lett 2024; 391:45-54. [PMID: 38092154 DOI: 10.1016/j.toxlet.2023.12.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/28/2023] [Accepted: 12/08/2023] [Indexed: 01/14/2024]
Abstract
We present the first computational model of the pathophysiological consequences of phosgene-induced lung injury in porcine subjects. Data from experiments previously performed in several cohorts of large healthy juvenile female pigs (111 data points from 37 subjects), including individual arterial blood gas readings, respiratory rate and heart rate, were used to develop the computational model. Close matches are observed between model outputs (PaO2 and PaCO2) and the experimental data, for both terminally anaesthetised and conscious subjects. The model was applied to investigate the effectiveness of continuous positive airway pressure (CPAP) as a pre-hospital treatment method when treatment is initiated at different time points post exposure. The model predicts that clinically relevant benefits are obtained when 10 cmH2O CPAP is initiated within approximately 8 h after exposure. Supplying low-flow oxygen (40%) rather than medical air produced larger clinical benefits than applying higher CPAP pressure levels. This new model can be used as a tool for conducting investigations into ventilation strategies and pharmaceutical treatments for chemical lung injury of diverse aetiology, and for helping to refine and reduce the use of animals in future experimental studies.
Collapse
Affiliation(s)
- Sonal Mistry
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Timothy E Scott
- Royal Centre for Defence Medicine, ICT Centre, Birmingham B15 2SQ, UK
| | - Bronwen Jugg
- CBR Division, Dstl Porton Down, Salisbury SP4 OJQ, UK
| | - Rosi Perrott
- CBR Division, Dstl Porton Down, Salisbury SP4 OJQ, UK
| | - Sina Saffaran
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Declan G Bates
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
| |
Collapse
|
4
|
Giosa L, Collins PD, Sciolla M, Cerrone F, Di Blasi S, Macrì MM, Davicco L, Laguzzi A, Gorgonzola F, Penso R, Steinberg I, Muraccini M, Perboni A, Russotto V, Camporota L, Bellani G, Caironi P. Effects of CPAP and FiO 2 on respiratory effort and lung stress in early COVID-19 pneumonia: a randomized, crossover study. Ann Intensive Care 2023; 13:103. [PMID: 37847454 PMCID: PMC10581975 DOI: 10.1186/s13613-023-01202-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND in COVID-19 acute respiratory failure, the effects of CPAP and FiO2 on respiratory effort and lung stress are unclear. We hypothesize that, in the compliant lungs of early Sars-CoV-2 pneumonia, the application of positive pressure through Helmet-CPAP may not decrease respiratory effort, and rather worsen lung stress and oxygenation when compared to higher FiO2 delivered via oxygen masks. METHODS In this single-center (S.Luigi Gonzaga University-Hospital, Turin, Italy), randomized, crossover study, we included patients receiving Helmet-CPAP for early (< 48 h) COVID-19 pneumonia without additional cardiac or respiratory disease. Healthy subjects were included as controls. Participants were equipped with an esophageal catheter, a non-invasive cardiac output monitor, and an arterial catheter. The protocol consisted of a random sequence of non-rebreather mask (NRB), Helmet-CPAP (with variable positive pressure and FiO2) and Venturi mask (FiO2 0.5), each delivered for 20 min. Study outcomes were changes in respiratory effort (esophageal swing), total lung stress (dynamic + static transpulmonary pressure), gas-exchange and hemodynamics. RESULTS We enrolled 28 COVID-19 patients and 7 healthy controls. In all patients, respiratory effort increased from NRB to Helmet-CPAP (5.0 ± 3.7 vs 8.3 ± 3.9 cmH2O, p < 0.01). However, Helmet's pressure decreased by a comparable amount during inspiration (- 3.1 ± 1.0 cmH2O, p = 0.16), therefore dynamic stress remained stable (p = 0.97). Changes in static and total lung stress from NRB to Helmet-CPAP were overall not significant (p = 0.07 and p = 0.09, respectively), but showed high interpatient variability, ranging from - 4.5 to + 6.1 cmH2O, and from - 5.8 to + 5.7 cmH2O, respectively. All findings were confirmed in healthy subjects, except for an increase in dynamic stress (p < 0.01). PaO2 decreased from NRB to Helmet-CPAP with FiO2 0.5 (107 ± 55 vs 86 ± 30 mmHg, p < 0.01), irrespective of positive pressure levels (p = 0.64). Conversely, with Helmet's FiO2 0.9, PaO2 increased (p < 0.01), but oxygen delivery remained stable (p = 0.48) as cardiac output decreased (p = 0.02). When PaO2 fell below 60 mmHg with VM, respiratory effort increased proportionally (p < 0.01, r = 0.81). CONCLUSIONS In early COVID-19 pneumonia, Helmet-CPAP increases respiratory effort without altering dynamic stress, while the effects upon static and total stress are variable, requiring individual assessment. Oxygen masks with higher FiO2 provide better oxygenation with lower respiratory effort. Trial registration Retrospectively registered (13-May-2021): clinicaltrials.gov (NCT04885517), https://clinicaltrials.gov/ct2/show/NCT04885517 .
Collapse
Affiliation(s)
- Lorenzo Giosa
- Department of Critical Care Medicine, Guy's and St. Thomas' National Health Service Foundation Trust, St. Thomas' Hospital, Westminster Bridge Road, London, SE17EH, UK.
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK.
| | - Patrick Duncan Collins
- Department of Critical Care Medicine, Guy's and St. Thomas' National Health Service Foundation Trust, St. Thomas' Hospital, Westminster Bridge Road, London, SE17EH, UK
| | - Martina Sciolla
- Department of Pulmonary Medicine, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | | | - Salvatore Di Blasi
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Matteo Maria Macrì
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Luca Davicco
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Andrea Laguzzi
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Fabiana Gorgonzola
- Department of Pulmonary Medicine, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Roberto Penso
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Irene Steinberg
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Department of Anaesthesia, Intensive Care and Emergency, Città della Salute e della Scienza University Hospital, Turin, Italy
| | | | - Alberto Perboni
- Department of Pulmonary Medicine, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
| | - Vincenzo Russotto
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
- Department of Oncology, University of Turin, Turin, Italy
| | - Luigi Camporota
- Department of Critical Care Medicine, Guy's and St. Thomas' National Health Service Foundation Trust, St. Thomas' Hospital, Westminster Bridge Road, London, SE17EH, UK
- Centre for Human and Applied Physiological Sciences, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Giacomo Bellani
- Centre for Medical Sciences - CISMed, University of Trento, Trento, Italy
- Department of Anesthesia and Intensive Care, Santa Chiara Regional Hospital, APSS Trento, Trento, Italy
| | - Pietro Caironi
- Department of Anesthesia and Critical Care, AOU S. Luigi Gonzaga, Orbassano, Turin, Italy
- Department of Oncology, University of Turin, Turin, Italy
| |
Collapse
|
5
|
Tsolaki V, Zakynthinos GE. Simulation to minimise patient self-inflicted lung injury: are we almost there? Br J Anaesth 2022; 129:150-153. [PMID: 35729011 PMCID: PMC9551385 DOI: 10.1016/j.bja.2022.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/21/2022] [Accepted: 05/12/2022] [Indexed: 11/25/2022] Open
Abstract
Computational modelling has been used to enlighten pathophysiological issues in patients with acute respiratory distress syndrome (ARDS) using a sophisticated, integrated cardiopulmonary model. COVID-19 ARDS is a pathophysiologically distinct entity characterised by dissociation between impairment in gas exchange and respiratory system mechanics, especially in the early stages of ARDS. Weaver and colleagues used computational modelling to elucidate factors contributing to generation of patient self-inflicted lung injury, and evaluated the effects of various spontaneous respiratory efforts with different oxygenation and ventilatory support modes. Their findings indicate that mechanical forces generated in the lung parenchyma are only counterbalanced when the respiratory support mode reduces the intensity of respiratory efforts.
Collapse
Affiliation(s)
- Vasiliki Tsolaki
- Department of Intensive Care Medicine, General University of Larissa, University of Thessaly, Faculty of Medicine, Larissa, Thessaly, Greece.
| | - George E Zakynthinos
- Department of Intensive Care Medicine, General University of Larissa, University of Thessaly, Faculty of Medicine, Larissa, Thessaly, Greece
| |
Collapse
|