The Correlation Between The Calibration of Ventilator While Operating in Volume Control – Continuous Mandatory Ventilation and Pressure Control – Continuous Mandatory Ventilation using The SMM 04-1 Standard Method

Abstract

The objective of this research was to study the correlation between the calibration of ventilator operating in Volume Control – Continuous Mandatory Ventilation (VC- CMV) and Pressure Control – Continuous Mandatory Ventilation (PC- CMV) , and to build prediction models for calibration in PC- CMV mode. This research was an analytical study conducted at Suranaree University of Technology Hospital. The sample in this research included 41 ventilators, which were used to measure monitoring accuracy calibration using the SMM 04-1 standard method, specifying three conditional calibration settings. The data were recorded using a recording form in each mode according to the parameters, including InspiratoryTidal Volume (VTI ), Expiratory Tidal Volume (VTE ), Positive End Expiratory Pressure (PEEP), Oxygen Concentration (O2 ) , and Inspiratory Pressure (PINSP ) . After that, the data were analyzed using the Pearson correlation coefficient and Simple Linear Regression. The results showed that VTE ,PEEP, and O2 values in VC-CMV mode had a high correlation with the same parameters in PC- CMV mode ( p<.001) . There was no correlation between PEEP values in VC-CMV mode and the PINSP value in PC-CMV mode (p>.05). When the model was built with parameters in VC-CMV mode to predict the same parameters in PC-CMV mode under conditions 1,3,and 5, the results showed that the VTE model had efficiencies of 61.7%, 87.3%, and 73.6%, respectively. The PEEP model had efficiencies of 49.7%, 75.3%, and 77.0%, respectively, and the O2 model had efficiencies of 12.1%, 45.3%, and 82.8%, respectively. The results indicated that both mode calibrations in the VTE , PEEP, and O2 parameters have a high correlation. Therefore, the calibration results of such parameters in VC- CMV mode can be representative of the calibration results in PC- CMV mode, And the models can be created calibration results from parameter in VC- CMV mode to predict parameters in PCCMV mode has a good performance.