REVIEW
Noninvasive haemodynamic monitoring
to predict outcome and guide therapy
in acute critical illness
Aim: To compare invasive pulmonary artery catheter (PAC) data
with continuous noninvasive haemodynamic monitoring using
a programme to predict outcome and guide therapy beginning
shortly after emergency department (ED) admission in a university-run inner city public hospital.
Methods: We compared PAC data with noninvasively monitored:
cardiac function by cardiac output (CI), mean arterial blood pressure (MAP), and heart rate (HR); respiratory function by arterial
oxygen saturation (SapO2); tissue perfusion/oxygenation by
transcutaneous tensions of CO2 and O2 indexed to FIO2. A search
and display programme calculated survival probabilities (SP)
and a decision support programme predicted effects of various
therapies.
Results: Survivors’ MAP, CI, SapO2, and PtcO2,/FIO2, and SP were
significantly higher (p <0.05) than nonsurvivors’ values in each
diagnostic category.
Conclusion: Compared with the PAC, noninvasive monitoring is
safer, simpler, easier, quicker, cheaper, reasonably accurate,
and available anywhere in the hospital or prehospital areas.
Increased CI and tissue oxygenation determined by the distribution of metarteriolar flow are underlying haemodynamic patterns associated with survival.
A
bout two million persons die annually in the USA
and half of these die of acute circulatory problems,
including shock, trauma, haemorrhage, surgery,
burns, sepsis, stroke and acute cardiac conditions. The
most urgent problem is to recognise the circulatory abnormalities of shock at their earliest possible stage so that they
can be most effectively treated.
Shock syndromes from high-risk surgery, trauma, dehydration, sepsis, burns, stroke, and acute cardiac conditions
are circulatory conditions that can be described by haemodynamic monitoring.1–6 Early monitoring identifies
haemodynamic abnormalities before irreversible changes
occur. The basic underlying problem in shock is tissue
hypoxia that can be measured by the net cumulative oxygen debt, which if prevented or corrected early, will reduce
organ failures and deaths. Crowell and Smith7 continuously monitored VO2 in controlled experimental studies in
anaesthetised and bled animals and showed that those who
accumulated an oxygen debt of 100 mL/kg all survived,
while those that accumulated a debt of 140 mL/kg all died.
They concluded that oxygen debt measured by reduced
rates of VO2 was the major determinant of outcome.7,8
Oxygen debt was also calculated before, during and after
high-risk surgery in a large series of high-risk surgical
patients.9 The patients who survived without organ failure
had an average of nine litres of oxygen debt which lasted
an average of 12 hours. The survivors with organ failures
had an average oxygen debt of 22 litres that lasted about 24
hours. The nonsurvivors, all of whom died with organ failures, had continuing oxygen debt averaging 33 litres during the 48-hour observation period. Clearly, oxygen debt
was a major determinant of outcome in these patients.9
The delivery of oxygen (DO2) is a well-defined direct
measurement of the overall circulatory function and its
increase represents the capacity of the circulation to compensate for increased body metabolism of stress, trauma,
surgical operations, sepsis and other acute illnesses.
Oxygen consumption (VO2) directly measures the body’s
overall metabolic activity. Survivors of high-risk surgery
were found empirically to have CI 4.5 L/min/m2, DO2 800
L/min/m2 and VO2 170 L/min/m2, while normal preoperative values were CI 3.2 +0.2 (SD) L/min/m2, DO2 500 +50
mL/min/m2, and VO2 130 +15 mL/min/m2.10,11 In a
prospective, randomised trial, protocol patients with survivors’ values as therapeutic goals were compared with
control patients with normal values as goals. The protocol patients reached their optimal goals within eight hours
postoperatively, and had significantly reduced mortality
(from 32% to 4%).10 Two meta-analyses of 22 randomised, controlled trials showed significantly reduced
mortality from 30% in control groups to 7% in protocol
groups, but outcome did not improve with goal-oriented
therapy, when monitoring was started late, defined as >24
hours after admission or after onset of an organ failure.12,13
More recently, Wilson et al.8 in randomised trials also
showed that early goal-directed therapy improved outcome when given <12 hours postoperatively, and Rivers
et al.14 in randomised trials showed significant reductions
in mortality in septic patients where therapy was started
in the ED within one hour of admission. Noninvasive and
minimally invasive haemodynamic monitoring, which
can be started in the ED, were developed to meet these
challenges.
Bayard et al.20,21 developed and tested a mathematical
approach based on a large database of clinical and haemodynamic patterns to predict outcome and guide therapy.
The present report reviews the application of this stochastic probability analysis and display model for emergency
patients, including injuries to the head, chest, abdomen,
fractures, extremities and paediatric injuries. In each of
these subsets of acute injuries, the cardiac index (CI) and
tissue oxygenation was greater in survivors than in the corresponding nonsurvivors.
WC Shoemaker MD,
CC-J Wo BS,
D Demetriades MD,
M Beez, PhD
Departments of Surgery,
LAC+USC Medical Center,
Keck School of Medicine,
University of Southern
California,
Los Angeles CA,
USA
METHODS
Clinical series
The present study reviews invasive haemodynamically
monitored data of 267 high-risk patients who had severe
life-threatening blunt or penetrating wounds of chest,
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NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
solution in the OR, followed by 500 mL of starch with 1,000
mL of lactated Ringer’s solution in the ICU. The data was
down-loaded every 30 seconds, averaged over five-minute
intervals and entered into the database. When consistent
haemodynamic patterns were demonstrated, they were
averaged over one- to eight-hour periods to describe cardiac, pulmonary and tissue perfusion patterns from the
time of ED admission to the patient’s recovery or death
(Figure 2).
Acute circulatory patterns are visually displayed by continuous, on-line, real-time, noninvasive haemodynamic
monitoring anywhere in the hospital, including the emergency department and the prehospital areas.22–25 Similarly
to early diagnosis and therapy for cancer, early diagnosis
with vigorous therapy for shock may be more effective
than therapy delayed until later stages when the diagnosis
is more certain. The serial haemodynamic patterns of circulatory abnormalities that ultimately lead to shock, organ
failure and death can be identified early, and their survival
probability (SP) may be predicted by mathematical analysis of their sequential clinical-haemodynamic patterns.22,23 Acute injury was chosen for these studies
because major injuries occurred shortly before ED admission and the time course of circulatory events can be monitored from admission until stable conditions are achieved
or the patient succumbs.
8
Cl
6
4
2
0
MAP
120
100
80
60
40
20
0
100
SapO2
98
96
94
992
0
PtcO2/FIO2
500
400
300
200
100
0
1.0
0.80
SP
Figure 1. Haemodynamic patterns
and the effects of resuscitation
therapy on haemodynamic patterns
and survival probability of a 23year old male who sustained gun
shot wounds to the abdomen and
left flank. Upper row: Cardiac index
(CI); Second row: Mean arterial
pressure; Third row: Pulse oximetry
(SapO2); Fourth row: PtcO2/FiO2;
Lowest row: Survival Probability.
Time, in hours from ED admission,
is noted below the bottom
horizontal line. Therapies are
outlined in boxes. FFP, fresh frozen
plasma; HES, hydroxyethyl starch;
RBC, packed red blood cell
transfusion; DOB, dobutamine,
and LR, Lactated Ringer’s solution,
which was given at the rate 150
mL/hr postoperatively. Time in OR
and ICU indicated at lowest line.
Note: the CI was maintained
above 4 L/min/hr, the MAP after
a plateau of 60 to 80 mmHg
was maintained above 80 mmHg,
the SapO2 was maintained above
98%, the PtcO2/FiO2 was above
200 torr throughout, and the SP
above 80% throughout.
He survived.
0.60
0.40
0.20
0.00
0
ER
2
RBC 7U
FFP 4U
LR 8L
OR
4
6
Hep 0.5L
LR 1L
8
10
12
Time (hours)
ICU
14
16
RBC 1U
FFP 2U
18
20
22
DOB 5µg/kg/min
abdomen, head or neck with an estimated >2000-ml blood
loss or evidence of circulatory derangements such as
hypotension, tachycardia, oliguria, poor tissue perfusion
and altered mental status.27,28 Invasively monitored
haemodynamic data were compared with noninvasively
monitored data of a series of trauma patients with the same
indications for monitoring. The noninvasive monitoring
was performed in 1,026 high-risk patients beginning
shortly after their ED admission20–25 (Table 1). Invasive
PAC monitoring was also instituted after the patient
arrived in the ICU or the OR to validate the noninvasive
cardiac output measurements.
Conventional invasive PA-catheter haemodynamic
monitoring
Invasive PA (pulmonary artery) catheterisation with
Swan-Ganz(R) thermodilution catheters, combined with
measurements of mean arterial pressure (MAP), heart rate
(HR), central venous pressure (CVP), arterial and mixed
venous blood gases/pH, were undertaken shortly after the
patient entered the operating room (OR) for emergency
surgery. These conventional pressure, HR, CVP and blood
gas measurements were made immediately after thermodilution CI measurements that were performed preoperatively, and repeated at intervals throughout the
operation and subsequent hospital course.
Cardiac index (CI)
A thoracic bioelectric impedance device (Noninvasive
Medical Technologies, LLC, Las Vegas, NV, USA)30,31 or
Physio-Flow (Manatec Biomedical, Paris, France)32 were
applied as soon as possible after arrival in the ED. Both of
these instruments gave satisfactory results compared with
the PAC thermodilution method. The appropriate noninvasive disposable prewired hydrogel electrodes were
positioned on the skin, and EKG leads were placed on the
precordium and shoulders.21–23,32 Previous studies have
documented satisfactory correlations between thermodilution and bioimpedance cardiac output values for trauma patients in the ED, OR and ICU conditions.22,23,32
Pulmonary artery catheters were placed usually in the OR
or ICU when indicated by clinical criteria and the need to
validate noninvasive measurements. Limitations of the
impedance method include faulty electrode placement,
motion artifacts, restlessness, shivering, pulmonary oedema, pleural effusion, valvular heart disease, dysrhythmias
and electrical leaks from other instruments using the same
circuit. These are apparent from the impedance waveform,
the consistency of continuous displays and by previously
described criteria: baseline impedance, Zo, >15 ohms and
peak impedance signal, dZ/dtmax, >0.3 ohms.21
Pulse oximetry
Continuous on-line noninvasive haemodynamic
monitoring of trauma patients
Haemodynamic values were evaluated by continuous display of noninvasive monitoring of cardiac, respiratory and
tissue perfusion functions. Figure 1 illustrates an example
of a patient who sustained gunshot wounds to his
abdomen and flank, with lacerations of the liver, pancreas,
diaphragm, left kidney and upper extremity and estimated blood loss of 2,500 mL. This was replaced with rapid
transfusion of seven units of packed red cells and four units
of fresh frozen plasma with 8,000 mL of lactated Ringer’s
2
Routine pulse oximetry (Nellcor, Pleasanton, CA, USA) was
used to assess continuously arterial oxygen saturation
(SapO2). Values were observed and recorded at the time of
the cardiac index measurements.21 Sudden changes in these
values were also noted and confirmed by in-vitro arterial
oxygen saturation obtained by standard blood gas analysis.
Transcutaneous oxygen tension
Transcutaneous oxygen tension (PtcO2) measurements
use the Clark polarographic oxygen electrode routinely
used in standard blood gas measurements.33–36 They were
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Table 1. Clinical features
Invasive
NonInvasive
Survivors
(N = 178)
Nonsurvivors
(N = 89)
Survivors
(N = 816)
Nonsurvivors
(N = 210)
32 ± 15
34 ± 21
36 ± 17
42 ± 21**
Male (N)
149
73
662
157
Female (N)
29
16
154
53
Fall
1
2
55
16
Gunshot wound
112
37
250
58
Blunt trauma
44
42
430
126
Stab wound
21
7
81
10
Head injury
14
17
224
141
Chest
74
25
272
32
Abdomen
115
43
315
41
Spinal cord injury
6
6
22
0
Fractures
25
11
160
17
Extremity
29
12
170
15
Estimated blood loss (mL)
1942 ± 2292
3417 ± 3776**
2329 ± 2526
4147 ± 5282**
Injury severity score
20.9 ± 0.3
31.1 ± 0.8**
19.1 ± 11.8
30.5 ± 14.4**
Glascow Coma Score
12.9 ± 0.3
8.1 ± 0.8**
13.7 ± 2.8
6.5 ± 4.7**
Age in years (mean ± SD)
Gender
Mechanism of injury (N):
Bodily injury (N):
*NS = not significant
indexed to the fractional inspired oxygen concentration
(FiO2) to give the PtcO2/FiO2 ratio, because changes of the
inspired oxygen produce marked PtcO2 changes. The
PtcO2/FiO2 was continuously measured in a representative
area of the skin surface heated to 440C to increase diffusion
of oxygen across the stratum corneum and to avoid vasoconstriction in the local area of the skin being measured.35,36
Database for monitoring emergency patients
Using both invasive and noninvasive monitoring, a
database for acutely ill or injured emergency patients was
developed to describe primary illnesses or injuries; selected covariates and sequential haemodynamic patterns by
invasive and noninvasive monitoring methods; and outcomes, including survival or death, organ failures, other
complications, hospital days and ICU days. The database
also included: age, gender, diagnostic categories such as
truncal, peripheral and head injuries, brain death, the
presence of sepsis or systemic immune response system
(SIRS), APACHE scores, Glasgow coma scores (GCS) and
injury severity score (ISS). The time and place of monitoring, time of operations, times of ICU admission and
discharge, time of hospital discharge or death, and other
events were recorded in time elapsed after admission.23
Mathematical search and display programme
The information system proposed is a search and display
programme that uses a large database of emergency
patients to identify similar patients with identical diagnosis, covariates and very close haemodynamic patterns.
It is based on a stochastic (probability) analysis and control programme developed and tested by Bayard et al.22,23
The programme uses data from subsets of patients,
defined as ‘nearest neighbours,’ who had identical diagnoses and covariates and very similar haemodynamic
patterns. These nearest neighbours are used as surrogates
to compute in real time the survival probability (SP) of
newly admitted study patients. A patient’s SP for a given
state x is denoted by S(x), which is calculated by first
extracting 40 or more ‘nearest neighbour’ states of
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Cl
5.0
4.5
4.0
3.5
3.0
HR
NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
140
120
100
80
60
tives and their integrals. Assume that there are L different
types of measurements taken on a given patient (e.g. cardiac
index, blood pressure, pulse oximetry and transcutaneous
O2 and CO2 tensions). Specifically, for each measurement
type, denoted as γ1, define the state vector as a concatenation of the value γ1 itself, its first and second derivatives
γ'1, γ”1 , and its first integral. The state vector, x(t) at time
t, is described in terms of the various haemodynamic measurements, their derivatives and their integrals. Assume
that there are L different types of measurements taken on
a given patient (e.g. cardiac index, blood pressure, pulse
oximetry, and transcutaneous O2 and CO2 tensions).
Specifically, for each measurement type, denoted as γ1,
define the state vector as a concatenation of the value γ1
itself, its first and second derivatives γ'1, γ”1 , and its first
integral ∫γ1dt, as follows:
MAP
100
80
60
40
SapO2
100
i.e., for L different measurement types there will be 4L
states. In practice, the derivatives and integrals are approximated by finite differences and sums of the time-ordered
data of the database. Specifically, we will calculate the
approximations:22
95
90
Survival probability
PtcO2/FIO2
85
300
200
100
0
100
80
Control input definition
60
The ‘control input’ (or mode of therapy) will be chosen from
a finite set of control inputs that can be applied to the system.
40
4
8
16
Time (hours)
24
48
System dynamics
Survivors
Nonsurvivors
Figure 2. Survivors’ (solid line) and
nonsurvivors’ (dashed line)
temporal patterns are shown.
Mean values +SEM are shown for
cardiac index (CI), heart rate (HR),
mean arterial pressure (MAP),
pulse oximetry (SapO2),
transcutaneous oxygen tension
indexed to the fractional inspired
oxygen concentration (PtcO2/FiO2),
and survival probability (SP). All
values are keyed to the time of
admission to the ED. Note the
survivors’ cardiac index, MAP,
SapO2, PtcO2/FiO2, and SP values
were generally higher than those of
the nonsurvivors. The mean
survivors’ SP values were
significantly higher than the mean
nonsurvivors’ SP values in this
observation period.
4
patients with the same diagnosis, the same or very similar covariates, and haemodynamic patterns that are closest to those of the study patient. The SP is calculated as
the fraction of nearest neighbours that survived. The SP
is an outcome predictor as well as a measure of the severity of illness.22,23
The therapeutic decision support programme uses the
study patient’s nearest neighbours’ responses to various
therapies that had been given them. The likely responses
of the study patient are suggested by the nearest neighbours’ responses which are known by the database.23 The
characteristics of this approach include: the patient’s
course is described by vectors in three dimensions as a trajectory; the first derivative of the initial vector projects the
patient’s course if there are no inherent changes or external influences; the second derivative tracks changes in the
patient’s course from either internal compensations, further deterioration, spontaneous improvement, or external influences such as changes in therapy; the integral sums
up the total influences. The programme was motivated by
methods of machine learning,37,38 and methods of dynamic programming for stochastic control.39,40
The state vector, x(t) at time t is described in terms of
the various haemodynamic measurements, their deriva-
It is convenient to think of the propagation of the patient’s
state at time , to his state at time as obeying the following
nonlinear dynamical system with process noise and
parameters p, i.e.:
For simplicity, p is discrete, and is assumed to be drawn
from a finite set formed by enumerating all useful combinations of clinical covariates,
Both the clinical covariates and process noise help to
explain the variability of patient responses seen in the
database. The covariates help to distinguish gross differences in responses of patients with major differences
in the nature of their disorders and complications.
Process noise helps to explain small differences between
patients with the same covariates but different responses to the same therapy. It is a measure of unmodelled
dynamics, or intra-individual variability, due to other
sources of variability in the system.22
Statistical analyses
Data sets obtained under comparable temporal conditions were evaluated using the two-sample Student’s
unpaired t-test and the Mann-Whitney test. When there
were multiple measurements from the same patient,
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NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
they were averaged to provide one number for each
patient, for each time interval. For categorical variables,
differences between survivors’ and nonsurvivors´´ data
were evaluated by the two-tailed Fisher’s Exact test and
Chi-square tests. The GraphPad Prism and SPSS software were used for statistical computations. Differences
were considered significant if p <0.05.
ROC curve
1.00
Sensitivity
.75
RESULTS
Comparison of invasive and noninvasive haemodynamic monitoring of trauma patients
Table 2 lists mean (+SD) survivors’ and nonsurvivors’ values by invasive monitoring for CI, HR, MAP, arterial
haemoglobin saturation (SapO2), central venous pressure
(CVP), mixed venous oxygen saturation (SvO2), DO2 and
VO2. Noninvasive haemodynamically monitoring included
CI, MAP, HR, SapO2, DO2, transcutaneous oxygen tension
indexed to the FiO2 (PtcO2/FiO2), and transcutaneous
carbon dioxide tension (PtcCO2). The CI, MAP, SapO2,
PtcO2/FiO2, SvO2, DO2, VO2 and Hct values of the survivors
were significantly higher than the corresponding values of
those who died during their current hospitalisation, while
the HR, CVP and PtcCO2 were higher in the nonsurvivors
during the first 24 hours after their ED admission. Figure 2
illustrates the time course of survivors’ and nonsurvivors’
haemodynamic patterns averaged over intervals during the
first 48-hour period after ED admission.
CI was simultaneously measured by both PAC thermodilution and bioimpedance on 907 occasions in 213 trauma
patients. The regression coefficient, r, was 0.92; r2 was 0.83,
and p <0.001. The bias and precision was —0.07 +0.47
L/min/m2. These data were similar to previously reported
studies.21
Head injured patients
Table 3 summarises the haemodynamic and oxygen transport values of 224 survivors and 67 nonsurvivors of head
injury, compared with values of normal, healthy, resting
volunteer subjects.21 Data of brain dead patients were analysed separately and presented in Tables 3 and 4. The survivors’ patterns had significantly higher CI, MAP, SapO2
and PtcO2/FiO2 values than did the nonsurvivors. The
nonsurvivors had higher HR and PtcCO2 values.
Brain dead patients
Table 4 lists haemodynamic and oxygen transport values
(mean +SD) of the 74 brain dead patients for three periods: the baseline period at, or just before, the time of brain
death diagnosis; the main period of brain death after its
diagnosis; the terminal haemodynamic end-stage just
before circulatory collapse and arrest. The patterns are
more clearly evident in the flow and tissue perfusion
parameters, than in blood pressure and heart rate. The significant findings during the major time of brain death were
pronounced increases in CI, PtcO2/FiO2 and DO2 values.
Initially, there was hypotension that usually responded
to fluid and supportive therapy and there were episodic
elevations of CI and PtcO2/FiO2 values (Table 4).
Calculation of survival probability (SP) compared
with actual hospital outcome
The survival probabilities calculated for the time after the
initial ED resuscitation were compared with the actual
hospital outcome. The mean SP of survivors during the
.50
Source of the Curve
PtcO2/FIO2
SAPO2
MAP
Cl
SP
.25
0.00
0.00
.25
.50
.75
1.00
Figure 3. Receiver operating
characteristic (ROC) curves
calculated for data collected in
trauma patients over the first 24hour period after ED admission.
The area under the curves
represents the sensitivity and
specificity for each variable: 1.00
represents 100% correct, 0.50
represents no correlation. These
areas under the ROC curve were
0.85 for SP, 0.81 for PtcO2/FiO2,
0.64 for SapO2, 0.61 for MAP, and
0.59 for CI.
1 - Specificity
Diagonal segments are produced by ties.
first 24 hours was 89 +0.1%. The SP for nonsurvivors was
75 +0.2%. Using 82%, which is half the difference between
survivors and nonsurvivors, as the cut-point for SP, there
were 14.2 % misclassifications (Table 5).
Receiver operating characteristic curves
Figure 3 shows receiver operating characteristic (ROC)
curves showing the survival probability together with
the other haemodynamic values. The size of the area
under the curves represents the sensitivity and specificity for each variable: 1.00 represents 100% correct,
0.50 represents no correlation. These areas were 0.85 for
SP, 0.81 for PtcO2/FiO2, 0.64 for SapO2, 0.61 for MAP,
and 0.59 for CI.
Therapeutic decision support system
The stochastic analysis of haemodynamic patterns may
also be used for therapeutic decision-making based on
data observed to occur in similar patients, i.e., the nearest
neighbours, recorded in the database. Table 6 summarises
the effects of various therapies where measurements were
taken before and after the therapy.
Comparison of various predictors by misclassification
rates
Table 7 shows misclassification rates of single initial or
lowest values of HR, MAP and CI values, APACHE II
score, discriminant analysis and the present survival
probability analysis. The APACHE II values were calculated on a daily basis, discriminant analysis covered the
early resuscitation period, and the probability analysis
was calculated for each set of values beginning shortly
after ED admission. Limitations of single values were
compared with the results of computerised noninvasive
variables measured over time.23
DISCUSSION
The haemodynamic patterns derived from invasive and
noninvasive monitoring of trauma patients were very similar in terms of CI, MAP, HR, SapO2, and qualitatively
comparable in terms of tissue perfusion/oxygenation (DO2,
and VO2 vs. PtcO2/FiO2, and PtcCO2). The advantage of
invasive monitoring is that over 30 years of widespread
usage has made it the standard of care in complex circulatory problems. The disadvantages are the cost, and the
invasive nature that limits its use to the ICU or OR, where
delayed ICU admission leads to delayed therapy. The
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NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
Table 2. Haemodynamic values in trauma patients by invasive and noninvasive monitoring
Invasive
Variable unit
NonInvasive
Survivors
(N = 178)
Nonsurvivors
(N = 89)
Survivors
(N = 592)
Nonsurvivors
(N = 69)
Mean ± SEM
Mean ± SEM
Mean ± SEM
Mean ± SEM
CI, L/min/m2
4.31 ± 0.06
3.72 ± 0.07**
4.10 ± 0.04
3.67 ± 0.10**
MAP, mmHg
98 ± 1
90 ± 1**
89 ± 1
78 ± 2**
HR, beat/min
109 ± 1
105 ± 1*
103 ± 1
114 ± 2**
SapO2, %
99 ± 0.4
95 ± 1**
98 ± 0.0
96 ± 0.2**
CVP, cmH2O
11.5 ± 0.3
12.5 ± 0.3*
-----
-----
SvO2, %
74 ± 0.4
72 ± 0.6**
-----
-----
DO2, mL/min/m
640 ± 9
522 ± 9**
617 ± 10
487 ± 21**
VO2, L/min/m
158 ± 2
130 ± 3**
-----
-----
PtcCO2, torr
-----
-----
47 ± 1
62 ± 7**
PtcO2/FiO2
-----
-----
230 ± 5
107 ± 9**
Hct, %
33 ± 0.2
33 ± 0.33
4±1
30 ± 1
89 ± 0.1
75 ± 0.2**
SP, %
CI = cardiac index; MAP = mean arterial pressure; HR = heart rate; SapO2 = arterial haemoglobin saturation by pulse oximetry; PtcCO2 = transcutaneous CO2 tension; PtcO2/FiO2 = transcutaneous O2 tension indexed to FiO2; SP =
survival probability
* = p<0.05; ** = p<0.01 comparing survivors with their corresponding nonsurvivors using unpaired Student’s t-test
Table 3. Haemodynamic values of head injured and brain dead patients
Variable unit (Mean ± SD)
Survivors (N = 224)
Nonsurvivors (N = 67)
Brain Dead (N = 74)
CI, L/min/m2
3.96 ± 1.12
3.65 ± 1.05*
4.25 ± 1.13
MAP, mmHg
89 ± 13
80 ± 20**
85 ± 20
HR, beat/min
102 ± 18
109 ± 21**
112 ± 19
SapO2, %
99 ± 2
97 ± 6**
98 ± 5
PtcCO2, torr
43 ± 14
48 ± 17*
41 ± 15
PtcO2/FiO2
229 ± 123
125 ± 100**
219 ± 152
CI = cardiac index; MAP = mean arterial pressure; HR = heart rate; SapO2 = arterial hemoglobin saturation by pulse oximetry; PtcCO2 = transcutaneous carbon dioxide tension; PtcO2/FiO2 = transcutaneous oxygen tension indexed to FiO2.
*p value <0.05 for survivors vs. nonsurvivors of head injury
**<0.01 for survivors vs. nonsurvivors of head injury
***<0.05 for brain dead vs. nonsurvivors of head injury
advantages of noninvasive monitoring are that it is continuous, on-line with real-time displays of arrays of
haemodynamic data. In each clinical category studied, the
survivors’ mean CI, MAP, SapO2 and PtcO2/FiO2 values
were significantly greater than those of the nonsurvivors,
while the nonsurvivors’ HR and PtcCO2 were greater than
those of the survivors. Compared with the PAC, noninvasive monitoring is safer, simpler, easier, quicker, cheaper, reasonably accurate and available anywhere in the
hospital.
Each of the component devices may be replaced by newer
or better methods that become available. For example,
6
the initial impedance cardiac output device (NCCOM3)
used in 198820 was replaced by the IQ device in 1992, and
now may be used with the PhysioFlow (Manatec
Biomedical, Paris, France), which gives comparable values
to the PAC thermodilution method.19
These survivor and nonsurvivor haemodynamic patterns
suggest that emergencies produce increased adrenalmedullary stress responses.28,36 In severely ill or dying
patients, intense vasomotion produces unevenly distributed metarteriolar flow with poor local tissue perfusion
and tissue hypoxia as evidenced by decreasing PtcO2/FiO2
and increasing PtcCO2. Progressive increases of localised
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INTERNATIONAL JOURNAL OF INTENSIVE CARE | SPRING 2007
NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
Table 4. Mean values +SD for monitored variables: baseline before or at the time of diagnosis of brain death, during brain death, and at the terminal
haemodynamic stage
Variable unit
Number of patients
Monitoring time
Baseline brain death
(N = 21)
1.3 ± 1.7 hours
During brain death
(N = 51)
4.9 ± 2.4 hours
Terminal stage
(N = 9)
1.3 ± 1.6 hours
CI, L/min/m2
3.19 ± 1.26
4.43 ± 1.33
1.76 ± 1.10
HR, beat/min
119 ± 29
113 ± 23
126 ± 28
MAP, mmHg
71 ± 23
89 ± 22
61 ± 16
SapO2, %
98 ± 3
99 ± 3
86 ± 21
PtcCO2, torr
54 ± 24
40 ± 15
59 ± 33
PtcO2/FiO2
103 ± 86
238 ± 186
70 ± 53
DO2, mL/min/m2
2399 ± 152
738 ± 185
283 ± 108
* p value <0.05 for baseline vs. during brain death
** p value <0.05 for during brain death vs. terminal stage
Table 5. Summary of classifications of emergency trauma patients exclusive of severe head injury and brain death (N = 661)
Predicted to die
N
(Row%)
Predicted to live
N
(Row%)
Total
N
(Col%)
Died
32
46.4%
37
53.6%
69
10.4%
Lived
57
9.6%
535
90.4%
592
89.6%
Total (%)
89
10.3%
572
89.7%
661
100.0%
Misclassification: 94/661 = 14.2%
tissue perfusion lead to global tissue hypoxia, shock, organ
failures and mortality.28,29
The conventional approach to databases is to collect
more and more data in order to consider as many contingencies as possible. Enlarging the series may improve
the accuracy of the mean values, but it also adds to the
variability. The proposed mathematical approach searches
the database to identify data of ‘nearest neighbours.’ The
statistical concept of nearest neighbours allows comparison
of the newly admitted study patient’s clinical-haemodynamic state with those of a subset of surrogate patients
from the database. This is a similar process to that pursued
by experienced clinicians who, on seeing an unusual
patient, reach back in their memory to recall a similar
patient who responded well to a specific therapy. The
proposed information system searches the database in an
analogous manner to find a number of very similar
patients and to evaluate the effectiveness of the therapy
given to each of them. In essence, this information system
emulates the processes of good clinical judgment by using
a computerised programme that obviates memory lapses.
The survival predictions were validated by comparison
with the patient’s actual outcome at hospital discharge.
The accuracy and reliability of this approach depends
on the size and comparability of the database needed to
provide an adequate group of nearest neighbours. The
present database contains 1,250 high-risk surgical and
trauma patients with over 45,000 time-lines, each of which
represents a patient’s clinical-haemodynamic state. This
provides adequate numbers for selection of nearest neighbours. The average difference between the study patients’
CI and its nearest neighbours’ CI was <0.3 of their SD.
The decision support system was developed to show the
relative effectiveness of therapy used in each of the nearest
neighbours in terms of improvement in haemodynamic
values and survival probabilities. The decision support
system allows the clinician at the bedside to make his/her
choice based on knowledge of each therapy’s probability
of improving outcome in a specific patient’s unique physiological state. The predicted effects of each therapeutic
intervention given to the newly admitted study patient
may be compared with the actual haemodynamic and SP
changes.
These preliminary results illustrate the use of an information system based on noninvasive data to support therapeutic decisions at the bedside. They are not meant to be
generalised for evaluation of the relative effectiveness of
each therapy, since these were not results of controlled
studies and the patients in these early resuscitation periods were not in ‘steady’ states, but were likely to have had
continuin blood and fluid losses at undetermined rates.
CONCLUSIONS
When time is crucial, it is necessary to treat circulatory
derangements of emergency patients as early as possible,
because their correction by early vigorous therapy improves
the likelihood of survival. The proposed stochastic analysis
and decision support programme provided mathematical
tools designed to facilitate initial physiological assessment,
the probability of survival and the likely responses to
various therapeutic choices. Reasons for favourable or
unfavourable outcome predictions are apparent from the
haemodynamic patterns. The therapeutic decisions based
on the known responses of ‘nearest neighbour’ surrogates
SPRING 2007 | INTERNATIONAL JOURNAL OF INTENSIVE CARE
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NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
Table 6. Effects of fluid therapy in trauma patients’ resuscitation
Survivors (N = 209)
RBC
SP
Before
0.84 + 0.13
Nonsurvivors (N = 88)
After
p-value
Before
After
p-value
0.89 + 0.12
<0.001
0.65 + 0.21
0.69 + 0.22
NS
CI
3.91 + 1.17
4.54 + 1.32
<0.001
3.48 + 1.13
3.99 + 1.16
<0.05
HR
106 + 19
106 + 17
NS
119 + 24
119 + 23
NS
MAP
83 + 18
90 + 17
<0.05
75 + 23
81 + 25
NS
SapO2
99 + 2
100 + 1
<0.05
96 + 9
96 + 9
NS
PtcCO2
44 + 13
43 + 13
NS
56 + 36
59 + 40
NS
PtcO2/FIO2
227 + 130
286 + 129
<0.001
117 + 146
125 + 161
NS
Survivors (N = 128)
Nonsurvivors (N = 55)
Crystalloids
Before
After
p-value
Before
After
p-value
SP
0.80 + 0.18
0.82 + 0.17
NS
0.70 + 0.18
0.68 + 0.18
NS
CI
3.79 + 1.12
3.78 + 1.13
NS
3.60 + 1.17
3.67 + 1.37
NS
HR
112 + 23
110 + 22
NS
123 + 25
121+ 24
NS
MAP
84 + 20
88 + 17
NS
81 + 23
81 + 21
NS
SapO2
99 + 3
99 + 3
NS
98 + 5
97 + 8
NS
PtcCO2
45 + 16
44 + 15
NS
45 + 18
45 + 19
NS
PtcO2/FIO2
179 + 112
214 + 123
<0.05
157 + 147
167 + 150
NS
Survivors (N = 150)
Nonsurvivors (N = 68)
Colloids
Before
After
p-value
Before
After
p-value
SP
0.82 + 0.17
0.85 + 0.16
NS
0.69 + 0.18
0.68 + 0.19
NS
CI
3.92 + 1.19
4.28 + 1.21
<0.01
3.69 + 1.36
3.72 + 1.28
NS
HR
110 + 21
107 + 20
NS
121 + 231
20 + 24
NS
MAP
85 + 19
87 + 17
NS
80 + 22
79 + 22
NS
SapO2
99 + 39
9+2
NS
98 + 4
97 + 6
NS
PtcCO2
44 + 15
43 + 15
NS
48 + 27
51 + 30
NS
PtcO2/FIO2
195 + 115
246 + 128
<0.001
129 + 134
128 + 140
NS
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INTERNATIONAL JOURNAL OF INTENSIVE CARE | SPRING 2007
NONINVASIVE HAEMODYNAMIC MONITORING OF ACUTE EMERGENCIES
Table 7. Misclassifications in outcome prediction by various analyses
Method
Criteria
Misclassification Rate
Initial heart rate
S <95, NS >96 beat/min
(70/159) 45%
Initial MAP
S >85, NS <70
mmHg
(76/159) 47%
Lowest MAP
S >50, NS <50
mmHg
(83/159) 52%
Initial CI
S >3.8, NS <3.8 L/min/m2
(72/159) 43%
APACHE II
S <27, NS >27
(30/97) 31%
Discriminate analysis
Multiple25
(23/151) 15.2%
Survival probability, present study
S>82%, NS <82%
(94/661) 14.2%
provide the bedside attendants with an array of therapies
together with their likely effects on the study patient. The
programme was designed to provide objective, verifiable
methods to predict outcome and support therapeutic
decisions in critically ill emergency patients.
19.
20.
21.
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CORRESPONDENCE TO:
William Shoemaker, MD
LAC+USC Medical Center
Room 9900
University of Southern California
1200 North State Street, Los Angeles CA 90033, USA
E-mail: [email protected]
9