New automated algorithm to calculate the percentage of muscle unloading during mechanical ventilation
CCCF ePoster library. Telias I. Oct 31, 2016; 150884; 6 Disclosure(s): Matías Madorno is the president and partner of MBMed SA, a company that develops a respiratory mechanics monitor. Dr. Laurent Brochards' laboratory has received equipement or research grants from Maquet, General Electric, Fisher and Paykel, Air Liquide and Philips.
Dr. Irene Telias
Dr. Irene Telias
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Topic: Basic or Translational Science

New automated algorithm to calculate the percentage of muscle unloading during mechanical ventilation


Madorno, Matías1; Telias, Irene G2,3,4, Rodríguez, Pablo O5; Carteaux, Guillaume6,7,8; Brochard Laurent J2,4
1Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina.2Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, Canada. 3 Division of Respirology, Department of Medicine, University Health Network and Mount Sinai Hospital, Toronto, Canada.4Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, Canada. 5Intensive Care Unit and Pulmonary Medicine Section, Department of Medicine, Instituto Universitario CEMIC (Centro de Educación Médica e Investigaciones Clínicas), Buenos Aires, Argentina. 6Assistance Publique-Hôpitaux de Paris, CHU Henri Mondor, DHUA-TVB, Service de Réanimation Médicale, Créteil, France. 7Université Paris Est Créteil, Faculté de Médecine de Créteil, Groupe de recherche clinique CARMAS, Créteil, France. 8INSERM, Unité U955, IMRB, Créteil, France



Abstract:

Introduction
Titration of ventilatory support is a challenge that needs to take into account patients’ ventilatory demand, risk of ventilation induced lung injury (VILI) and diaphragmatic dysfunction (VIDD). The use of monitoring tools to titrate ventilatory support to patients’ needs is mandatory. An index of muscle unloading (MUI) has been previously described that correlates the amount of positive pressure delivered by the ventilator with the total pressure applied to the system.1 However the calculation is based on one point in time, at peak muscular pressure (Pmus). In order to calculate the forces applied to the respiratory system by the ventilator and the activity of the respiratory muscles as well as its relationship, the analysis of the whole inspiratory cycle could be considered.
Objective
Development and validation of an automated algorithm to calculate the percentage of muscle unloading during mechanical ventilation using the pressure-time product (PTP).
Methods
The new algorithm is based on the equation of motion and calculates the total pressure-time product applied to the respiratory system (PTPsys) as the sum of the muscular pressure developed during inspiration (PTPpat) and the pressure applied by the ventilator (PTPvent). The PTPpat is the integral of the esophageal pressure swing (Peso), from the beginning of the effort until the end of mechanical inspiration limited by the chest wall recoil pressure. PTPvent is the integral of the airway pressure over end-expiratory pressure during the mechanical inspiration. The pressure-time product muscle unloading index (PTPMUI) is the relation between PTPvent and PTPsys and equals (PTPvent/PTPsys) x100.
Recordings of 10 patients recovering from acute respiratory failure ventilated with 13 levels of support (during pressure support ventilation –PSV- and neurally adjust ventilatory assist –NAVA) were analyzed. Patient 5 was excluded due to questionable esophageal balloon placement and 2 recordings from Patient 6 were discarded due to balloon deflation. 2091 breaths were finally included. Based on airway pressure (Paw), flow and Peso signal, PTPMUI and MUI were calculated using a dedicated software. Correlation between PTPMUI and MUI was assessed by Pearson correlation and the accuracy was evaluated using the Bland and Altman method.
Results
The correlation between PTPMUI and MUI was 0.891 (p<0.001) (FIGURE 1A) the bias was 2% SD (7%). (FIGURE 1B) The correlation between PTPMUI and the level of assistance of PSV was 0.829 (p<0.001) and with the level of assistance of NAVA 0.648 (p<0.001). At lower levels of assistance, the percentage of muscle unloading using MUI is more homogeneous between patients than the one calculated using MUIPTP. (FIGURE 2A, 2B, 2C, 2D)
Conclusion
There is a good correlation and small bias between MUI and PTPMUI. PTPMUI has shown to correlate with the level of assistance in proportional and non-proportional modes of ventilation. The higher difference in the calculation of muscle unloading between the two methods at lower levels of support was probably due to the heterogeneity of the patients’ inspiratory effort, only evident with PTPMUI. At lower levels of assistance the peak value could be less representative of the whole inspiratory cycle. While the calculation of PTPMUI appears to be more complex, it can be done automatically and used at the bedside. (FIGURE 3)


References:

1 Carteaux G, Mancebo J, Dellamonica J, Richard JCM,  Aguirre-Bermeo H, Kouatchert A, Beduneau G, Thielle AW, Brochard L. Bedside Adjustment of Proportional Assist Ventilation to Target a Predefined Range of Respiratory Effort. Criti Care Med 2013;41:2125:2132



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