Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model
Background and objective: Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury,...
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my.iium.irep.978542022-05-12T00:06:24Z http://irep.iium.edu.my/97854/ Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model Mat Nor, Mohd Basri Yeong, Shiong Chiew Damanhuri, Nor Salwa Mohd zainol, Nurhidayah Othman, Nor Azlan Chase, Geoffrey Muhammad, Zuraida R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid Background and objective: Mechanical ventilation (MV) provides breathing support for acute respiratory distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However, model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model. Methods: Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining 400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data. Results and discussion: The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients. The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the lowest percentage of SB incidence which proved that NARX model is able to perform for both higher incidence of SB effort or when there is a lack of SB effort. Conclusion: This model is able to produce the SB incidence rate based on 10% threshold. Hence, the proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has the potential to further assist clinical decisions and optimize MV settings. Elsevier 2022-04-21 Article PeerReviewed application/pdf en http://irep.iium.edu.my/97854/1/97854_Estimating%20the%20incidence%20of%20spontaneous%20breathing%20effort%20of%20mechanically%20ventilated%20patients%20using%20a%20non-linear%20auto%20regressive%20%28NARX%29%20model.pdf Mat Nor, Mohd Basri and Yeong, Shiong Chiew and Damanhuri, Nor Salwa and Mohd zainol, Nurhidayah and Othman, Nor Azlan and Chase, Geoffrey and Muhammad, Zuraida (2022) Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model. Computer Methods and Programs in Biomedicine, 220. pp. 1-9. ISSN 0169-2607 https://www.journals.elsevier.com/computer-methods-and-programs-in-biomedicine https://doi.org/10.1016/j.cmpb.2022.106835 |
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R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid Mat Nor, Mohd Basri Yeong, Shiong Chiew Damanhuri, Nor Salwa Mohd zainol, Nurhidayah Othman, Nor Azlan Chase, Geoffrey Muhammad, Zuraida Estimating the incidence of spontaneous breathing effort of mechanically ventilated patients using a non-linear auto regressive (NARX) model |
description |
Background and objective: Mechanical ventilation (MV) provides breathing support for acute respiratory
distress syndrome (ARDS) patients in the intensive care unit, but is difficult to optimize. Too much, or too
little of pressure or volume support can cause further ventilator-induced lung injury, increasing length of
MV, cost and mortality. Patient-specific respiratory mechanics can help optimize MV settings. However,
model-based estimation of respiratory mechanics is less accurate when patient exhibit un-modeled spontaneous breathing (SB) efforts on top of ventilator support. This study aims to estimate and quantify SB
efforts by reconstructing the unaltered passive mechanics airway pressure using NARX model.
Methods: Non-linear autoregressive (NARX) model is used to reconstruct missing airway pressure due
to the presence of spontaneous breathing effort in mv patients. Then, the incidence of SB patients is estimated. The study uses a total of 10,000 breathing cycles collected from 10 ARDS patients from IIUM
Hospital in Kuantan, Malaysia. In this study, there are 2 different ratios of training and validating methods. Firstly, the initial ratio used is 60:40 which indicates 600 breath cycles for training and remaining
400 breath cycles used for testing. Then, the ratio is varied using 70:30 ratio for training and testing data.
Results and discussion: The mean residual error between original airway pressure and reconstructed airway pressure is denoted as the magnitude of effort. The median and interquartile range of mean residual
error for both ratio are 0.0557 [0.0230 - 0.0874] and 0.0534 [0.0219 - 0.0870] respectively for all patients.
The results also show that Patient 2 has the highest percentage of SB incidence and Patient 10 with the
lowest percentage of SB incidence which proved that NARX model is able to perform for both higher
incidence of SB effort or when there is a lack of SB effort.
Conclusion: This model is able to produce the SB incidence rate based on 10% threshold. Hence, the
proposed NARX model is potentially useful to estimate and identify patient-specific SB effort, which has
the potential to further assist clinical decisions and optimize MV settings. |
format |
Article |
author |
Mat Nor, Mohd Basri Yeong, Shiong Chiew Damanhuri, Nor Salwa Mohd zainol, Nurhidayah Othman, Nor Azlan Chase, Geoffrey Muhammad, Zuraida |
author_facet |
Mat Nor, Mohd Basri Yeong, Shiong Chiew Damanhuri, Nor Salwa Mohd zainol, Nurhidayah Othman, Nor Azlan Chase, Geoffrey Muhammad, Zuraida |
author_sort |
Mat Nor, Mohd Basri |
title |
Estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (NARX) model |
title_short |
Estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (NARX) model |
title_full |
Estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (NARX) model |
title_fullStr |
Estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (NARX) model |
title_full_unstemmed |
Estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (NARX) model |
title_sort |
estimating the incidence of spontaneous breathing effort of
mechanically ventilated patients using a non-linear auto regressive (narx) model |
publisher |
Elsevier |
publishDate |
2022 |
url |
http://irep.iium.edu.my/97854/1/97854_Estimating%20the%20incidence%20of%20spontaneous%20breathing%20effort%20of%20mechanically%20ventilated%20patients%20using%20a%20non-linear%20auto%20regressive%20%28NARX%29%20model.pdf http://irep.iium.edu.my/97854/ https://www.journals.elsevier.com/computer-methods-and-programs-in-biomedicine https://doi.org/10.1016/j.cmpb.2022.106835 |
_version_ |
1732945021901996032 |
score |
13.160551 |