Protocol conception for safe selection of mechanical ventilation settings for respiratory failure Patients. (February 2022)

• Record Type: Journal Article
• Authors: Lee, Jay Wing Wai; Chiew, Yeong Shiong; Wang, Xin; Tan, Chee Pin; Mat Nor, Mohd Basri; Cove, Matthew E.; Damanhuri, Nor Salwa; Chase, J. Geoffrey
• Is Part Of: Computer methods and programs in biomedicine. Volume 214(2022)
• Publication Date: 2022-02
• Subjects: Respiratory failure -- Mechanical ventilation -- Respiratory mechanics -- and decision making; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106577
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Mechanical ventilation setting selection protocol is developed using model-based methods. Application case study shows potential in reducing an overwhelming number of possible mechanical ventilation setting combinations. Comparison of developed protocol with retrospective clinically implemented settings shows protocol's potential to prevent harmful settings. Abstract: Background and Objective: Mechanical ventilation is the primary form of care provided to respiratory failure patients. Limited guidelines and conflicting results from major clinical trials means selection of mechanical ventilation settings relies heavily on clinician experience and intuition. Determining optimal mechanical ventilation settings is therefore difficult, where non-optimal mechanical ventilation can be deleterious. To overcome these difficulties, this research proposes a model-based method to manage the wide range of possible mechanical ventilation settings, while also considering patient-specific conditions and responses. Methods: This study shows the design and development of the "VENT" protocol, which integrates the single compartment linear lung model with clinical recommendations from landmark studies, to aid clinical decision-making in selecting mechanical ventilation settings. Using retrospective breath data from a cohort of 24 patients, 3, 566 and 2, 447 clinically implemented VC and PC settings were extracted respectively. Using this data, a VENT protocol application case study and clinical comparison is performed, and the prediction accuracy of the VENT protocol is validated against actual measured outcomes of pressure and volume. Results: The study shows the VENT protocols' potential use in narrowing an overwhelming number of possible mechanical ventilation setting combinations by up to 99.9%. The comparison with retrospective clinical data showed that only 33% and 45% of clinician settings were approved by the VENT protocol. The unapproved settings were mainly due to exceeding clinical recommended settings. When utilising the single compartment model in the VENT protocol for forecasting peak pressures and tidal volumes, median [IQR] prediction error values of 0.75 [0.31 – 1.83] cmH 2 O and 0.55 [0.19 – 1.20] mL/kg were obtained. Conclusions: Comparing the proposed protocol with retrospective clinically implemented settings shows the protocol can prevent harmful mechanical ventilation setting combinations for which clinicians would be otherwise unaware. The VENT protocol warrants a more detailed clinical study to validate its potential usefulness in a clinical setting.