A minimal recruitment model can be used to guide mechanical ventilation PEEP selection for acute respiratory distress syndrome (ARDS) patients. However, implementation of this model requires a specific clinical protocol and is computationally expensive, and thus not suitable for bedside application. This work aims to improve the performance and bedside utility of the minimal recruitment model through simplifying the model, and improving the identification algorithm without compromising the model's physiological relevance to the disease. Identifying the model requires fitting of 8 unique parameters to pressure-volume data at multiple levels of positive end-expiratory pressure (PEEP). A minimal algorithm is proposed to improve the model's performance. The algorithm utilises a non-linear least-squares solver to estimate a global set of the parameters to a pressure-volume curve at one PEEP level. These global parameters were then subsequently used to estimate other parameters at different pressure-volume curves. The accuracy and computational performance of the minimal algorithm is compared to the grid search algorithm for 2 ARDS patient cohorts. The median [IQR] absolute percentage curve fitting error over all patients for grid search algorithm is 1.40% [0.55-3.75], and for the minimal algorithm is 2.43% [0.83-8.09] (p < 0.005). The median [IQR] computational time for all patients for the grid search algorithm is 394.51s [284.79-630.45], and for the minimal algorithm is 0.72s [0.39-1.46], where a 600% of reduction of computational time was found for the minimal algorithm. The estimated parameters using the minimal algorithm are correlated with the grid search algorithm with median person's correlation of R2 > 0.9. The model fitting error for the minimal algorithm is higher than the grid search algorithm. However the model was able capture similar trends in physiologically relevant parameters without the loss of important clinical information. The minimal algorithm is less computationally intensive than the grid search algorithm, whilst still providing a means of selecting PEEP with only a small increase in model fitting error. The minimal algorithm is able to improve computational performance while maintaining the ability to capture physiological parameters as the grid search algorithm. The significant reduction in computational time encourages its clinical application at the bedside for decision making.
- Mechanical ventilation
- Recruitment model