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We have developed a new technique to estimate the clinically relevant aortic pressure waveform from multiple, less invasively measured peripheral artery pressure waveforms. The technique is based on multichannel blind system identification in which two or more measured outputs (peripheral artery pressure waveforms) of a single-input, multi-output system (arterial tree) are mathematically analyzed so as to reconstruct the common unobserved input (aortic pressure waveform) to within an arbitrary scale factor. The technique then invokes Poiseuille's law to calibrate the reconstructed waveform to absolute pressure. Consequently, in contrast to previous related efforts, the technique does not utilize a generalized transfer function or any training data and is therefore entirely patient and time specific. To demonstrate proof of concept, we have evaluated the technique with respect to four swine in which peripheral artery pressure waveforms from the femoral and radial arteries and a reference aortic pressure waveform from the descending thoracic aorta were simultaneously measured during diverse hemodynamic interventions. We report that the technique reliably estimated the entire aortic pressure waveform with an overall root mean squared error (RMSE) of 4.6 mmHg. For comparison, the average overall RMSE between the peripheral artery pressure and reference aortic pressure waveforms was 8.6 mmHg. Thus the technique reduced the RMSE by 47%. As a result, the technique also provided similar improvements in the estimation of systolic pressure, pulse pressure, and the ejection interval. With further successful testing, the technique may ultimately be employed for more precise monitoring and titration of therapy in, for example, critically ill and hypertension patients. arterial tree; blood pressure; model; system identification; transfer function doi:10.1152/ajpheart.01159.2006

Byline: S. Sondergaard (1), S. Karason (2), J. Wiklund (3), S. Lundin (1), O. Stenqvist (1) Keywords: Respiratory mechanics Alveolar pressure Dynostatic algorithm Abstract: Objectives. We evaluated an algorithm for continuous on-line monitoring of alveolar pressure over time in a lung model with lower and upper inflection points and variable resistance ratios and in patients with acute lung injury. The algorithm is based on 'static' pressure/volume curves obtained from tracheal pressure measurements under dynamic conditions. Design and setting. Experimental and clinical evaluation of algorithm in a university hospital laboratory and intensive care unit. Patients. Ten patients undergoing postoperative respiratory therapy (feasibility of tracheal measurement) and ten patients with acute lung injury undergoing ventilator treatment (evaluation of algorithm). Measurements and results. Direct tracheal pressure measurements with a catheter inserted through the endotracheal tube. Comparison of measured alveolar and the dynostatic alveolar pressure vs. time in a lung model with changes in five ventilatory parameters. Examples of clinical monitoring are reported. In the model there was excellent agreement between alveolar pressures obtained by the algorithm, the dynostatic alveolar pressure, and measured alveolar pressure at all ventilator settings. For inspiratory/expiratory resistance ratios between 1:2.1--2.1:1, the dynostatic alveolar pressure was within +-1.5 cm [H.sub.2]O of measured alveolar pressure. In patients the technique for direct tracheal pressure measurement using a catheter inserted through the endotracheal tube functioned satisfactorily with intermittent air flushes for cleansing. Conclusions. Using a thin tracheal pressure catheter inserted through the endotracheal tube alveolar pressure allows continuous bedside monitoring with ease and precision using the dynostatic algorithm. The method is unaffected by tube and connector geometry or by secretions. Author Affiliation: (1) Department of Anaesthesia and Intensive Care, Sahlgrenska University Hospital, Gothenburg, Sweden (2) Department of Anaesthesia and Intensive Care, Landspitali University Hospital, Reykjavik, Iceland (3) Department of Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden Article History: Received Date: 05/02/2002 Accepted Date: 24/02/2003 Article note: Electronic Publication