Cardiac output estimation using arterial blood pressure waveforms

Cardiac output (CO) is a cardinal parameter of cardiovascular state, and a fundamental determinant of global oxygen delivery. Historically, measurement of CO has been limited to critically-ill patients, using invasive indicator-dilution methods such as thermodilution via Swan-Ganz lines, which carry risks. Over the past century, the premise that CO could be estimated by analysis of the arterial blood pressure (ABP) waveform has captured the attention of many investigators. This approach of estimating CO is minimally invasive, cheap, and can be done continuously as long as ABP waveforms are available. Over a dozen different methods of estimating CO from ABP waveforms have been proposed and some are commercialized. However, the effectiveness of this approach is nebular. Performance validation studies in the past have mostly been conducted on a small set of subjects under well-controlled laboratory conditions. It is entirely possible that there will be circumstances in real world clinical practice in which CO estimation produces inaccurate results. In this thesis, our goals are to (1) build a computational system that estimates CO using 11 of the established methods; (2) evaluate and compare the performance of the CO estimation methods on a large set clinical data, using the simultaneously available thermodilution CO measurements as gold-standard; and (3) design and evaluate an algorithm that identifies and eliminates ABP waveform segments of poor quality. Out of the 11 CO estimation methods studied, there is one method (Liljestrand method) that is clearly more accurate than the rest. Across our study population of 120 subjects, the Liljestrand method has an error distribution with a 1 standard deviation error of 0.8 L/min, which is roughly twice that of thermodilution CO. These results suggest that although CO estimation methods may not generate the most precise values, they are still useful for detecting significant (>1 L/min) changes in CO.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2006

Author dc:creator

• Sun, James Xin

Advisor dc:contributor.advisor

• Roger G. Mark.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/41625

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/41625