Bioelectrical impedance spectroscopy as a fluid management system in heart failure.
Episodes of hospitalization for heart failure patients are frequent and are often accompanied by fluid accumulations. The change of the body impedance, measured by bioimpendace spectroscopy, is an indicator of the water content. The hypothesis was that it is possible to detect edema from the impedance data. First, a finite integration technique was applied to test the feasibility and allowed a theoretical analysis of current flows through the body. Based on the results of the simulations, a clinical study was designed and conducted. The segmental impedances of 25 patients suffering from heart failure were monitored over their recompensation process. The mean age of the patients was 73.8 and their mean body mass index was 28.6. From these raw data the model parameters from the Cole model were deduced by an automatic fitting algorithm. These model data were used to classify the edema status of the patient. The baseline values of the regression lines of the extra- and intracellular resistance from the transthoracic measurement and the baseline value of the regression line of the extracellular resistance from the foot-to-foot measurement were identified as important parameters for the detection of peripheral edema. The rate of change of the imaginary impedance at the characteristic frequency and the mean intracellular resistance from the foot-to-foot measurement were identified as important parameters for the detection of pulmonary edema. To classify the data, two decision trees were considered: One should detect pulmonary edema (n(pulmonary) = 13, n(none) = 12) and the other peripheral edema (n(peripheral) = 12, n(none) = 13). Peripheral edema could be detected with a sensitivity of 100% and a specificity of 90%. The detection of pulmonary edema showed a sensitivity of 92.31% and a specificity of 100%. The leave-one-out cross-validation-error for the peripheral edema detection was 12% and 8% for the detection of pulmonary edema. This enables the application of BIS as an early warning system for cardiac decompensation with the potential to optimize patient care.
Philips Chair of Medical Information Technology, RWTH Aachen University, Aachen, Germany.
Sören
S
Weyer
Matthias Daniel
MD
Zink
Tobias
T
Wartzek
Lennart
L
Leicht
Karl
K
Mischke
Thomas
T
Vollmer
Steffen
S
Leonhardt
D000368:Aged; D001826:Body Fluids / Q000378:metabolism*; D001835:Body Weight; D003198:Computer Simulation; D003663:Decision Trees; D058266:Dielectric Spectroscopy / Q000379:methods*; D017097:Electric Impedance; D004566:Electrodes; D005260:Female; D006333:Heart Failure / Q000503:physiopathology*; D006801:Humans; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000378:metabolism; D010446:Peptide Fragments / Q000378:metabolism; D011654:Pulmonary Edema / Q000175:diagnosis
D010446:Peptide Fragments; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain