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TitleComputers in Critical Care and Pulmonary Medicine: Volume 2
File Size6.0 MB
Total Pages219
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Page 109


possible using inexpensive temperature measuring equipment which can
be readily implemented in the laboratory of surgical amphitheatre.
Time varying, or position varying blood flow can be estimated, but
with a substantial loss of accuracy.3 Engineering sensitivity
analysis has outlined the experimental errors most likely to cause
error in the predicted tissue blood flow rate. Errors in temperature
measurement are more important, by two orders of magnitude, than
errors in thermal conductivity, heat generation tate, and other model
parameters in distorting the perfusion estimate. Absolute system
measurement accuracy of ~ .05 0 e is necessary to predict perfusion
rate to 20% accuracy in a one dimensional perfusion model.

If the assumptions underlying the bio-heat transfer equation are
appropriate for the organ bed in question, the bio-heat transfer
equation can be used to adequately predict the macroscopic temperature
distribution, and solution of the inverse problem, i.e. matching of
experimental and theoretical temperature profiles, provides a low
cost, semi-continuous means for measuring the perfusion rate.


1. R. C. Eberhardt, A. Shitzer, E. H. Hernandez, Thermodilution
methods: Estimation of tissue blood flow and metabolism,
Ann. NY Academy of Sciences, 335:107-131 (1980).

2. H. F. Bowman, T. A. Balasubramaniam, M. Woods, Determination of
tissue perfusion from thermal conductivity measurements, Paper
77-WA/HT-40. ASME, United Engineering Center, New York

3. A. Shitzer, A. B. Elkowitz, R. C. Eberhart, Temperature profiles
calculated in tissues subjected to non uniform blood flow dis-
tributions. Advances in Biomedical Engineering. ASME, United
Engineering Center, New York (1980).

Page 110



R.C. Eberhart, T.I. Thomasson, R. Sken, K. Wiemer,
G. Cumming, M. Judy and G. Szabo

Department of Surgery
University of Texas Health Science Center
Dallas, Texas

A pH sens1t1ve field effect transistor is a semiconductor device
in which the metal gate is removed and the exposed silicon nitride/
oxide insulating layers chemically interact with H+ ions, thereby
modulating the transistor voltage-current relation. 1 An early model
of our pH ISFET is depicted in Fig. 1. Further treatment of the
insulating layer with an ion selective membrane, such as an ionophore-
loaded polymer over the active region allows selective filtration of
the ion of interest, e.g. Na+, K+, Ca++, which may then also modulate
the transistor electrical characteristics. 2 Using such techniques a
number of chemical sensors, called ion sensitive field effect tran-
sistors, (ISFET) may be created. The number of sensors which may fit
on a single chip is only limited by the mechanical placement of the
ion selective membrane.

ISFETs are potentially attractive for indwelling blood electro-
lyte analysis, owing to their small size, economy, robust nature and
the ability to combine several sensors and logic designs in a single
chip by conventional photomask semiconductor processing techniques.
Recent work in ISFET technology has focused on three fundamental
problems: the principles of operation of the device, stable operation
in electrolyte solutions and the avoidance of thrombogenesis and other
artifacts at the sensing surface.

Original investigations of the ISFET indicated ion exchange
between the electrolyte and nitride layer dominated the electro-
chemical process and produced a Nernst response, e.g., voltage shift
proportional to log (ion concentration). However, at high electrolyte
concentrations and in the absence of appropriate impurity atoms in
the nitride layer, space charge effects modify the potential dis-
tribution at the interface, and thus change the relationship between


Page 218


P0 2, measurements, computer
analysis, 101-103

positive end-expiratory pressure,
optimum, determination,

Pressure-flow-loop, analysis, body
plethysmography, 71-73

Pulmonary edema
monitoring airway pressure and

compliance as indicators,

thermodilution, 123-125
tomographic imaging of lung

density, 41-43
Pulmonary function

in the intensive care unit,

multicompartmental analysis,
system identification
techniques, 35-37

see also Lung function

Respiratory quotient test,
automated continuous,

Respiratory testing, intermittent,
hierarchical, in intensive
care unit, 151-157

Restrictive lung impairments,
diagnosis, comparison of
algorithms, 63-66

Scattering, Compton, in tomo-
graphic imaging of lung
density, 41-43

Semiconductor electrolyte analysis
and indwelling chemical
sensors, 121-122

Series dead space in gas mixing,

Shock trauma unit, monitoring and
data acquisition, 167-170

Shunts, circulatory, during arti-
ficial ventilation,
automatic analysis,

model, 219
Single breath test,

automatic computation, 95-97


Single breath test (continued)
diffusing-capacity, automated

measurement in routine
testing, 99-100

for C02 for dead space analysis,

Smoking and chronic bronchitis, 14
Solitary pulmonary nodule,

decision analysis, 109-111
Specific airways conductance in

bronc homo tor tone
estimation, 83-84

Spirometry in diagnosis of obstruc-
tive and restrictive lung
function, 63-66

Surgical intensive care unit
cardiac surgery, metabolic moni-

toring after, 235-237
principle findings, 236-237

cardiopulmonary monitoring,

alarm system, objectives,

evaluation, 217
methods, 216
results and discussion, 216-217

computer assisted, 173-174
heart rate, 174

monitoring and data acquisition,

automated physiologic profile,
167, 168

see also Intensive care unit
System identification techniques

in multicompartmental
analysis, pulmonary
function, 235-237

Terminal airway structure, diagnos-
tic information, from
expiratory concentration
volume diagrams, 137-140

Terminal ventilation unit,
structure, 10-12

branching patterns, 10
morphometric data, 10

Thermal-dye extravascular lung
water, system for
quantitating, 123-125

Page 219


ln prediction of tissue

perfusion and heat gener-
ation rates, 117-119

ln pulmonary edema quantitation,

Tidal volume, continuous distri-
bution from 02 N2 wash-in
and wash-out, ICU, 191-192

Tissue perfusion, prediction by
improved heat clearance
technique, 117-119

Total lung capacity, automatic
computation, 95-97

Transfer factor, measurement by
single breath test, 99-100

Transthoracic impedances, computer
assisted analysis, 113-115

changes in pulmonary volumes,

Trauma, multiple, monitoring
computer-assisted ICU,

Velocity oxygen and nitrogen ln
trachea, 8-11

Venous oxygen measurement in
assessment of pulmonary
function, 155-156

alveolar, measurements, from

Xenon-133 wash-in curves,

alveolar, PaC02 levels, 154, 155
anatomical features, 9-10
applied anatomy, 9-11
and bulk flow, 11
circulatory shunts,

automatic analysis, 219-221
local density random walk

function, 219
model, 219

data system, 171-172
off-line, 172

distribution, continuous,
computing, 15-19

linear programing methods, 18
multibreath nitrogen wash~out,

multi-compartment model, 15-18


Ventilation (continued)
distribution, continuous,

computing (continued)
two-compartment approach, 17,18

and duct structure, 11-12
effect, computerized analysis

with PEEP, 211-213
feedback controlled, 207-209
in infancy, Krypton 81m, 45-46
intermittent, hierarchical,

intensive care unit,

intermittent mandatory, in inten-
sive care unit, 152, 154

PaC0 2 levels, 154, 155
mechanical, PC02 measurements,

comparisons, 21-33
see also PC02 measurement,

mean alveolar
weaning from, interactive

computer program, 201-205
weaning from, hemodynamic and

gas exchange data, 204
and perfusion, distribution, 5, 8
terminal unit, structure, 10

Ventilator management
critical care, 163-166
interaction between patient,

clinician, monitor and,

rules, 164-165
status, 164, 165
therapy, 164, 165

Weaning from mechanical ventilation,
interactive computer
program, 201-205

Xenon-133 wash-out curves,
analysis methods, 49-53

cost, 51-52
ln lung volumes and ventilation,


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