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Abstract Pulmonary artery catheterization (PAC) has been used as a clinical standard for cardiac output (CO) measurements on humans. On animals, however, an ultrasonic flow sensor (UFS) placed around the ascending aorta or pulmonary artery can measure CO and stroke volume (SV) more accurately. The objective of this paper is to compare CO and SV measurements using a noninvasive electrical impedance tomography (EIT) device and three invasive devices using UFS, PAC-CCO (continuous CO) and arterial pressure-based CO (APCO). Thirty-two pigs were anesthetized and mechanically ventilated. A UFS was placed around the pulmonary artery through thoracotomy in 11 of them, while the EIT, PAC-CCO and APCO devices were used on all of them. Afterload and contractility were changed pharmacologically, while preload was changed through bleeding and injection of fluid or blood. Twenty-three pigs completed the experiment. Among 23, the UFS was used on 7 pigs around the pulmonary artery. The percentage error (PE) between COUFS and COEIT was 26.1%, and the 10-min concordance was 92.5%. Between SVUFS and SVEIT, the PE was 24.8%, and the 10-min concordance was 94.2%. On analyzing the data from all 23 pigs, the PE between time-delay-adjusted COPAC-CCO and COEIT was 34.6%, and the 10-min concordance was 81.1%. Our results suggest that the performance of the EIT device in measuring dynamic changes of CO and SV on mechanically-ventilated pigs under different cardiac preload, afterload and contractility conditions is at least comparable to that of the PAC-CCO device. Clinical studies are needed to evaluate the utility of the EIT device as a noninvasive hemodynamic monitoring tool.

Abstract The importance of perioperative respiration monitoring is highlighted by high incidences of postoperative respiratory complications unrelated to the original disease. The objectives of this pilot study were to (1) simultaneously acquire respiration rate (RR), tidal volume (TV), minute ventilation (MV), SpO2 and PetCO2 from patients in post-anesthesia care unit (PACU) and (2) identify a practical continuous respiration monitoring method by analyzing the acquired data in terms of their ability and reliability in assessing a patient’s respiratory status. Thirteen non-intubated patients completed this observational study. A portable electrical impedance tomography (EIT) device was used to acquire RREIT, TV and MV, while PetCO2, RRCap and SpO2 were measured by a Capnostream35. Hypoventilation and respiratory events, e.g., apnea and hypopnea, could be detected reliably using RREIT, TV and MV. PetCO2 and SpO2 provided the gas exchange information, but were unable to detect hypoventilation in a timely fashion. Although SpO2 was stable, the sidestream capnography using the oronasal cannula was often unstable and produced fluctuating PetCO2 values. The coefficient of determination (R2) value between RREIT and RRCap was 0.65 with a percentage error of 52.5%. Based on our results, we identified RR, TV, MV and SpO2 as a set of respiratory parameters for robust continuous respiration monitoring of non-intubated patients. Such a respiration monitor with both ventilation and gas exchange parameters would be reliable and could be useful not only for respiration monitoring, but in making PACU discharge decisions and adjusting opioid dosage on general hospital floor. Future studies are needed to evaluate the potential clinical utility of such an integrated respiration monitor.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs occurred. A total of 36 measurements with 113 one-hour sub-datasets were collected from 14 patients. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 L per minute (LPM), respectively, compared to 4.05 and 3.82 LPM for the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability by at least two times, particularly in high-motion environments.