Stochasticity of the respiratory mechanics during mechanical ventilation treatment

• Main Authors: Ang, Christopher Yew Shuen, Chiew, Yeong Shiong, Wang, Xin, Mat Nor, Mohd Basri, Chase, J. Geoffrey
• Format: Article
• Language: English; English
• Published: Elsevier 2023
• Subjects: RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
• Online Access: http://irep.iium.edu.my/109726/7/109726_Stochasticity%20of%20the%20respiratory%20mechanics.pdf; http://irep.iium.edu.my/109726/8/109726_Stochasticity%20of%20the%20respiratory%20mechanics_Scopus.pdf; http://irep.iium.edu.my/109726/; https://www.sciencedirect.com/science/article/pii/S2590123023003845

Stochastic models have been used to predict dynamic intra-patient respiratory system elastance (Ers) in mechanically ventilated (MV) patients. However, existing Ers stochastic models were developed using small cohorts, potentially showing bias and overestimation during prediction. Thus, there is a need to improve the stochastic model's performance. This research investigates the effect of the kernel density estimator (KDE) parameter tuned with a constant, c on the performance of a 30-min interval Ers stochastic model. Thirteen variations of a stochastic model were developed using varying KDE parameters. Model bias and overestimation were evaluated by the percentage of actual data captured within the 25th – 75th and 5th – 95th percentile lines (Pass50 and Pass90). The optimum range of c was chosen to tune the KDE parameter and minimise the temporal variations of model-predicted 25th – 75th and 5th – 95th percentile values of Ers (ΔRange50 and ΔRange90) in an independent retrospective clinical cohort of 14 patients. In this cohort, the values of ΔRange50 and ΔRange90 exhibit a converging behaviour, resulting in a cohort-optimised value of c = 0.4. Compared to c = 1.0 (benchmark study model), c = 0.4 significantly reduces model overestimation by up to 25.08% in the 25th – 75th percentile values of Ers. Overall, c = 0.3–1.0 presents as a generalised range of optimum c values, considering the trade-off between data overfitting and model overestimation. Optimisation of the KDE parameter enables more accurate and robust Ers stochastic models in cases of limited training data availability.