Estimation of cardiac output based on PRAM algorithm and ARX model from noninvasive radial pressure wave

There is an increasing need for the prevention, diagnosis and treatment of cardiovascular disease (CVD). Reduced cardiac output (CO) is frequently associated with CVD. Thus, accurate measurement of CO aids its diagnosis and helps to guide treatment. A convenient, real-time, noninvasive way to estimate CO is the Pressure Recording Analytic Method (PRAM). It is based on the non-invasive detection of the pressure wave in a peripheral artery and does not require invasive measurements to calibrate the model parameters. Unfortunately, its accuracy is limited due to its dependence on the patient's height, weight, heart rate, and the mechanical properties of each individual's arteries. Furthermore, compared to the peripheral arterial pressure wave, the aortic pressure wave provides a more accurate and efficient means of estimating CO. Therefore, to improve the accuracy of the original PRAM method, this study incorporates height, weight, and heart rate measurements, as well as an Auto-Regressive with eXogenous input (ARX) model, enabling adaptive estimation of the aortic pressure waveform from radial artery pressure wave measurement. The CO estimations of the original peripheral PRAM (CO_PRAMper), the improved peripheral PRAM (CO_IPRAMper) and the improved central PRAM (CO_IPRAMcen) were compared to MRI results (CO_MRI), as the ground truth. The correlation coefficients (R²) between the CO estimates using the 3 algorithms and CO_MRI were 0.271, 0.548 and 0.757, respectively. These R² values were statistically significant and showed that CO_IPRAMcen performed best. The mean difference between the CO estimates using the 3 algorithms and CO_MRI were −0.15 ± 0.44, −0.07 ± 0.24 and −0.04 ± 0.17 L/min, respectively.