Stochastic integrated model‑based protocol for volume‑controlled ventilation setting

• Main Authors: Wai Lee, Jay Wing, Chiew, Yeong Shiong, Wang, Xin, Mat Nor, Mohd Basri, Chase, Geoffrey, Desaive, Thomas
• Format: Article
• Language: English; English
• Published: Springer 2022
• Subjects: RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
• Online Access: http://irep.iium.edu.my/96785/7/96785_Stochastic%20integrated%20model%E2%80%91based%20protocol%20for%20volume%E2%80%91controlled.pdf; http://irep.iium.edu.my/96785/13/96785_Stochastic%20integrated%20model-based%20protocol_Scopus.pdf; http://irep.iium.edu.my/96785/; https://biomedical-engineering-online.biomedcentral.com/track/pdf/10.1186/s12938-022-00981-0.pdf
• Institution: Universiti Islam Antarabangsa Malaysia

Background and objective: Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specifc and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. Methods: A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the diference in reduction of possible MV settings achieved by each. Results: From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. Conclusions: Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specifc MV settings. It specifcally considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.