Download Evaluation of Cardiac Output during pain crises in sickle cell disease

EVALUATION OF CARDIAC OUTPUT DURING PAIN CRISIS IN CHILDREN WITH SICKLE CELL DISEASE
Chikelue Oragwu MBBS
MS Clinical Investigation Sciences Candidate, CREST Program, University of Louisville, Louisville KY
INTRODUCTION
Sickle cell disease (SCD) is an autosomal recessive genetic red blood cell (RBC) disorder due to an inborn
amino acid substitution, characterized by the presence of hemoglobin S resulting in abnormally shaped
red blood cells. This structural abnormality impedes the ability of the red cells to move through narrow
vasculature, and hence the oxygen carrying ability of the blood stream. The ‘sickled’ cells break down
easily resulting in anemia. The types of sickle cell disease include sickle cell anemia [SS], hemoglobin SC
disease, and sickle beta thalassemia1. SCD is particularly common among people whose ancestors come
from Sub-Saharan Africa, South America, Cuba, Central America, Saudi Arabia, India, and Mediterranean
countries such as Turkey, Greece, and Italy. In the Unites States, it affects around 72,000 people, mostly
African Americans. The disease occurs in about 1 in every 500 African-American births and 1 in every
1000 to 1400 Hispanic-American births. About 2 million Americans, or 1 in 12 African Americans, carry
the sickle cell allele2.
Pain is the most common complaint in children. Pain crisis, a condition of extreme pain as a result of
vessel occlusion by the sickled cells, leading to ischemia, affects children and adult alike. Children suffer
periodically from severe pain in the extremities, abdomen or chest and often present to the emergency
department (ED) seeking medical attention. The pain crisis is postulated to be due to “sickling” of the
red blood cells of the affected organ or extremity resulting in poor blood flow and hypoxia. Dehydration,
hypothermia and hemoconcentration are known risk factors. The vaso-occlusion has been attributed to
complex interactions among the sickle cell red blood cells, leukocytes, endothelial cells, and plasma
proteins, and many other unknown factors.3,4 The treatment in the emergency department typically
consists of intravenous fluids and potent analgesics (oral or parenteral). Improvements in pain in sickle
cell disease have been attributed to improvements in circulation by fluid resuscitation in addition to
analgesics. The red cells regain their elasticity by reoxygenation perhaps from increased flow.
The cardiac output (CO) is the volume of blood the heart pumps into the circulation per minute. The
cardiac index (CI) is ratio of the cardiac output, measured in Liters per minute (Liters/min) to the total
body surface area (m2). The unit for the cardiac index is liters per minute per square meter (L/min/m2).
Cardiac Index is easier to compare between children of various ages due to its consideration of body
surface area. The sickle cell pain crisis is directly related to hemodynamic phenomena in vaso-occlusion.
The study of pain crisis and its management thus revolves around the pattern of the cardiac output and
blood flow. Non invasive techniques which measure cardiac output are increasingly being used in
various clinical settings. Impedance cardiography (ICG), a technique which can be utilized to measure
cardiac output noninvasively is based on the principle of electrical bioimpedance and Ohm’s law.
Modern ICG instruments transmit low energy; high frequency alternating-electrical-current through the
thorax. Another pair of detecting electrodes is located inside the current pathway display the impedance
changes. The frequency of the delivered current is between 20 to 200 KHz, with minimal energy that
cannot be sensed by the patient. Impedance cardiometry is safe and FDA approved.
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The electrical cardiometry (EC) is similar to the impedance cardiography; both use sensors on the thorax,
and they depend on the aortic volumetric changes to determine the stroke volume and cardiac output.
The electrical cardiometry, also referred to as electronic velocimetry, is more diverse and accurate in its
application while the ICG is mainly used with healthy adults.5
ICG is noninvasive, and has been shown to provide reliable hemodynamic data comparable to that
obtained by pulmonary artery catheterization and doppler echocardiography6-8 . One study showed that
in post operative heart patients with anatomical abnormalities however, the utility was limited. 9The
validity of ICG using the most current technology has been demonstrated both in adults and in
children.10 11 Impedance cardiography has also been used to measure cardiac output in various settings
including sepsis, hemorrhage, resuscitation and during surgery.12 Measuring cardiac output may be
helpful in evaluation of cardiovascular status during pain crisis in sickle cell disease.
To the best of our knowledge, the changes in cardiovascular status, specifically cardiac output in
children with sickle cell disease who are experiencing pain crisis has not been reported. A better
understanding of the changes in cardiovascular parameters and tissue oxygenation may yield clues for
improved management of children suffering from pain crisis in sickle cell disease.
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RESEARCH QUESTION / HYPOTHESIS
The study sought to answer the question:
Do children experiencing a pain crisis associated with sickle cell disease have similar
hemodynamic parameters (preload, afterload, heart rate, contractility, and cardiac output) as
measured by electronic velocimetry when compared to baseline?
How do these parameters ultimately affect the disposition (outcome)?
In this study, we hypothesized that cardiac output measurements by electronic velocimetry in pain crisis
associated with sickle cell disease may be similar to baseline measurements.
OBJECTIVE /SPECIFIC AIM
Our objective was to measure cardiac output (CO) noninvasively during the management of sickle cell
disease in children in pain crisis and compares these to measurements after its resolution. The study
planned to enroll 50 children between 3 and 18 years in age suffering from pain crisis due to sickle cell
disease.
We specifically aimed to obtain non invasive cardiac output values in sickle cell disease in pain crisis at
presentation and during management and compare the values with measurements obtained during the
subsequent follow up appointment after the pain crisis has resolved.
We feel that the knowledge gained may help us understand the status of cerebral venous oxygenation
and cardiovascular status during treatment of children in pain crisis, understand pathophysiology of pain
crisis and improve management of children with sickle cell disease associated pain crisis.
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METHODS
Eligibility Criteria
Eligibile children were aged 3 to 18 years seen at Kosair Children’s Hospital Emergency Department (ED)
or Wards in Louisville, KY for sickle cell disease in pain crisis. The exclusion criteria were subjects with
preexisting neurological conditions such as cerebral palsy or developmental delay, cyanotic heart
disease, intubated children, individuals at risk for an adverse skin reaction due to dermatological
hypersensitivity. Thus far, 39 subjects were enrolled in this study.
Study Procedure
The study was conducted at Kosair Childrens Hospital’s Emergency Department (ED) and at the
outpatient sickle cell clinics. Once the diagnosis of pain crisis was made by the attending hematologist or
emergency physician who is providing medical care, and it was determined that the subject meets the
eligibility criteria, the subject or the subject’s legal guardian for children under 18, is guided through the
consenting process. The characteristics of subject including age, weight, previous weight, height ,
duration and degree of pain, home medications, baseline mental status, vital signs, perfusion status
(capillary refill, skin turgor), fever, chest pain, presence/absence of difficulty breathing, and pulse
oximetry were obtained. All components of ED standard of care and hospital evaluation and therapy
(laboratory data, medications, intravenous fluids, and red blood cell transfusions) were recorded using a
standardized data collection form. Only the cardiac output measurement was obtained outside the
standard of care for these subjects. These readings were obtained every 15 minutes for 2 hours after
initiation of intravenous fluids and/or analgesics. Age-appropriate pain scores on a scale of 1-10 were
also obtained every 15 minutes for the 2 hour duration and the Wong-Baker Faces pain rating scale was
used for children who cannot verbalize pain. The follow up measurements were obtained during the
subsequent clinic visit after discharge from the hospital.
Data Collection and Analysis
We accrued subjects and collecting data for the past 24 months. The numeric clinical measurements
and demographic data included age, weight, height, electrical velocimetry measurements , pulse
oximetry, pain scores, and time to resolution were analyzed using mean, median, standard deviation,
interquartile range, maximum, and minimum. For time-varying measurements made at 15 minute
intervals, the summary statistics were calculated at baseline, at every 15 minute interval, and at
resolution of the condition. We used multivariate Regression and recursive analysis on initial
Information to Predict Disposition. Longitudinal mixed model analysis will be used to assess time trends
for the time-varying measurements. Categorical demographic data including gender and ethnicity will be
summarized using frequency and count tables. Frequency and count tables were also be used to
summarize the dichotomized variables above (dichotomized NIRS regional Oxygen saturation (rSO2),
Page 4 of 17
pulse oximetry SpO2, and ICG). The number of events and the timing of each event for each patient were
noted. The rate of detection of adverse events if any and the timing of events for each of these
instruments (NIRS, Pulse oximetry) were calculated and measured by repeated measures ANOVA. If
results were not normal in distribution, non-parametric methods would be utilized.
Page 5 of 17
RESULTS
A total of 39 subjects have been recruited for the study. There were slightly more males recruited so far
[56.4%] compared with the females [43.6%]. The number of subjects was almost equal with reference to
the disposition after the intervention in the emergency room-51.3% admitted, and 48.7% discharged.
The mean age (months) is 155.51, with a median of 159.00 and standard deviation (sd) of 54.765
(Range: 44 – 263 months). The mean weight in kilogram (Kg) was 52.2, median of 56 and sd of 23.3911
(Range: 15.4 - 111.3 Kg). The mean Initial Systolic BP in mmHg 121.66 while the median was 120.00 and
sd of 15.859 (Range: 94-161 mmHg). There were seven missing values for the systolic BP. The mean
heart rate (HR) in beats per minute 89.87 while the median was 94.00 and sd of 18.117 (Range: 50-128
beats/min); there was also a missing value for the HR. The details are presented in Tables 1 – 3.
Table 1: Gender
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
Female
17
43.6
43.6
43.6
Male
22
56.4
56.4
100.0
Total
39
100.0
100.0
Table 2: Disposition
Frequency
Percent
Valid Percent
Cumulative
Percent
Valid
ADMITTED
20
51.3
51.3
51.3
DISCHARGED
19
48.7
48.7
100.0
Total
39
100.0
100.0
Page 6 of 17
Table 3: Initial Clinical Information
Age (months)
Valid
Weight in Kg
Initial SysBP
Initial HR
39
39
32
38
0
0
7
1
Mean
155.51
52.200
121.66
89.87
Median
159.00
56.000
120.00
94.00
Std. Deviation
54.765
23.3911
15.859
18.117
Minimum
44
15.4
94
50
Maximum
263
111.3
161
128
25
128.00
35.100
113.25
74.75
50
159.00
56.000
120.00
94.00
75
193.00
64.600
129.75
101.00
N
Missing
Percentiles
The results are expressed in mean +/- sd, median or range. The laboratory values on presentation
include: hemoglobin (g/dl) with mean +/- sd values of 9.29 +/-1.9558 (range: 34-12.1g/dl), hematocrit
(%) with mean +/- sd values 26.45+/-5.6247 (range:9.1-34.8%), reticulocyte count (%) with mean +/- sd
values 8.084 +/- 5.3266 (range:0.2-23.0%), and white blood cell (WBC) count with mean +/- sd values
11,904.47+/-4,417.256 (range: 5,570-23,790 ).
Table 4: Initial Laboratory Values
Hemoglobin
Valid
Hematocrit
Retic Count
WBC
Neutrophils
Lymphocytes
Monocytes
38
38
38
38
38
38
38
1
1
1
1
1
1
1
Mean
9.290
26.450
8.084
11904.47
58.74
27.05
10.74
Median
9.050
26.000
8.150
11430.00
58.50
25.00
11.00
1.9558
5.6247
5.3266
4417.256
15.144
13.111
4.985
Minimum
3.4
9.1
.2
5570
29
3
0
Maximum
12.1
34.8
23.0
23790
96
62
24
25
7.900
23.000
3.425
8250.00
47.00
18.00
8.00
50
9.050
26.000
8.150
11430.00
58.50
25.00
11.00
75
11.125
32.100
11.250
14855.00
70.50
33.50
13.00
N
Missing
Std. Deviation
Percentiles
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The cardiac output (CO) and cardiac index (CI) were analyzed in 3 phases-initial, minimum and
maximum, and the time to achieve max value in minutes is also analyzed. Initial CO had the mean +/- sd
value of 5.26+/-2.4837 (range: 1.6-16.8L/min), minimum CO had the mean +/- sd value of 4.465+/2.3745 (range: 1.7-16.8L/min), and maximum CO had the mean +/- sd value of 6.185+/- 2.9074 (range:
2.5-21.1L/min). The time to the achieve peak CO in minutes had the mean +/- sd value of 57.08 +/42.696 (range: 0-120 minutes).
Initial CI has the mean +/- sd value of 3.721+/-1.3744 (range: 2.1-10.2L/min/m2), minimum CI had the
mean +/- sd value of 3.128+/-1.2724 (range: 1.9-9.6L/min/m2), and maximum CI had the mean +/- sd
value of 4.367+/- 1.6945 (range: 2.0-12.8L/min/m2). The time to the achieve peak CI in minutes had the
mean +/- sd value of 57.46 +/- 41.097 (range: 0-120 minutes).
The median duration to reach the peak values in both CO and CI were 50 minutes.
Table 5: ICON Cardiometry Results
ICG (C.O.)
ICG (C.O.)
ICG (C.O.)
Time to CO Max
ICG (C.I.)
ICG (C.I.)
ICG (C.I.)
Time to CI
Initial
Min
Max
(minutes)
Initial
Min
Max
Max
(minutes)
Valid
39
39
39
39
39
39
39
39
0
0
0
0
0
0
0
0
Mean
5.260
4.465
6.185
57.08
3.721
3.128
4.367
57.46
Median
4.800
4.100
5.800
50.00
3.500
3.000
4.000
50.00
2.4837
2.3745
2.9074
42.696
1.3744
1.2724
1.6945
41.097
Minimum
1.6
1.7
2.5
0
2.1
1.9
2.0
0
Maximum
16.8
16.8
21.1
120
10.2
9.6
12.8
120
25
3.800
3.100
4.800
15.00
2.600
2.200
3.600
15.00
50
4.800
4.100
5.800
50.00
3.500
3.000
4.000
50.00
75
6.400
4.900
7.200
104.00
4.300
3.800
5.000
104.00
N
Missing
Std. Deviation
Percentiles
The cerebral (C) NIRS and splanchnic (R) NIRS as measured by the NIRS were also analyzed in 3 phases
and the time to reach the maximum value was gotten.
Initial C had the mean +/- sd value of 49.47+/-8.443 (range: 32-65), minimum C had the mean +/- sd
value of 45.05+/-6.96 (range: 31-58), and maximum C has the mean +/- sd value of 54.5+/- 7.682 (range:
37-70). The time to the achieve peak C in minutes had the mean +/- sd value of 47.38 +/- 45.03 (range:
0-120 minutes).
Initial R had the mean +/- sd value of 59.9+/-11.295 (range: 28-85), minimum R has the mean +/- sd
value of 56.56+/- 10.857 (range: 28-82), and maximum R has the mean +/- sd value of 68.64 +/- 10.941
(range: 38-91). The time to the achieve peak R in minutes has the mean +/- sd value of 72.41 +/- 33.849
(range: 0-123 minutes).
Page 8 of 17
Table 6: NIRS Results
NIRS (C )
NIRS (C )
NIRS (C )
Time to NIRS C
NIRS (R )
NIRS (R )
NIRS (R )
Time to
Initial
Min
Max
Max (minutes)
Initial
Min
Max
NIRS R
Max
(minutes)
Valid
38
37
38
37
39
39
39
39
1
2
1
2
0
0
0
0
Mean
49.47
45.05
54.50
47.38
59.90
56.56
68.64
72.41
Median
51.00
45.00
55.00
30.00
60.00
55.00
70.00
75.00
Std. Deviation
8.443
6.960
7.682
45.030
11.295
10.857
10.941
33.849
Minimum
32
31
37
0
28
28
38
0
Maximum
65
58
70
120
85
82
91
123
25
42.00
40.00
47.75
10.00
52.00
51.00
64.00
45.00
50
51.00
45.00
55.00
30.00
60.00
55.00
70.00
75.00
75
55.50
51.00
59.50
97.50
68.00
64.00
76.00
100.00
N
Missing
Percentiles
The delta (maximum minus initial) in observed values for the CI, Cerebral NIRS and Splanchnic NIRS were
as follows; CI-mean+/-sd are 0.9244+/-0.96896, cerebral NIRS-mean+/-sd are 5.0263+/-4.40814 and the
splanchnic NIRS-mean +/-sd are 8.7436 +/- 5.14893.
Page 9 of 17
Maximum Observed Increase in Measurements
Table 7: Delta in Observed Values (Maximum minus Initial)
Delta Cardiac
Delta Cerebral
Index
NIRS
Valid
Delta Splanchnic NIRS
39
38
39
0
1
0
Mean
.9244
5.0263
8.7436
Median
.7000
4.0000
9.0000
.96898
4.40814
5.14893
Minimum
.00
.00
.00
Maximum
4.30
14.00
20.00
25
.3000
1.0000
6.0000
50
.7000
4.0000
9.0000
75
1.3000
9.2500
11.0000
N
Missing
Std. Deviation
Percentiles
Correlations of Initial Measurements
Table 8: Correlations
ICG (C.I.) Initial
NIRS (C ) Initial
NIRS (R ) Initial
1
-.140
-.129
.402
.432
39
38
39
-.140
1
.592**
Pearson Correlation
ICG (C.I.) Initial
Sig. (2-tailed)
N
Pearson Correlation
NIRS (C ) Initial
Sig. (2-tailed)
N
Pearson Correlation
NIRS (R ) Initial
Sig. (2-tailed)
N
.402
.000
38
38
38
-.129
.592**
1
.432
.000
39
38
39
**. Correlation is significant at the 0.01 level (2-tailed).
Page 10 of 17
Correlations of Maximum Measurements
Table 9: Correlations
ICG (C.I.) Max
-.388*
.020
.015
39
38
39
-.376*
1
.659**
1
Sig. (2-tailed)
N
Pearson Correlation
NIRS (C ) Max
Sig. (2-tailed)
.020
N
Pearson Correlation
NIRS (R ) Max
NIRS (R ) Max
-.376*
Pearson Correlation
ICG (C.I.) Max
NIRS (C ) Max
.000
38
38
38
-.388*
.659**
1
.015
.000
39
38
Sig. (2-tailed)
N
39
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations of Minimum Measurements
Table 10: Correlations
ICG (C.I.) Min
Pearson Correlation
ICG (C.I.) Min
1
-.177
.604
.282
39
37
39
-.088
1
.640**
N
NIRS (C ) Min
Sig. (2-tailed)
.604
N
Pearson Correlation
NIRS (R ) Min
Sig. (2-tailed)
NIRS (R ) Min
-.088
Sig. (2-tailed)
Pearson Correlation
NIRS (C ) Min
.000
37
37
37
-.177
.640**
1
.282
.000
39
37
N
39
**. Correlation is significant at the 0.01 level (2-tailed).
Page 11 of 17
Multivariate Regression using Initial Information to Predict Disposition
Logistic regressions for the binary result of Admitted/Discharged with backward stepwise conditional
method:
Table 11: Case Processing Summary
Unweighted Casesa
N
Included in Analysis
Selected Cases
Missing Cases
Total
Unselected Cases
Total
Percent
30
76.9
9
23.1
39
100.0
0
.0
39
100.0
a. If weight is in effect, see classification table for the total number of cases.
Table 12a: Dependent Variable Encoding
Original Value
Internal Value
Discharged
0
Admitted
1
Table 12b: Categorical Variables Codings
Frequency
Parameter coding
(1)
Female
14
1.000
Male
16
.000
Gender
Page 12 of 17
Table 13: Regression Results – Start and Finish - Variables in the Equation
B
S.E.
Wald
df
Sig.
Exp(B)
95% C.I.for EXP(B)
Lower
Gender(1)
Upper
-2.093
1.636
1.638
1
.201
.123
.005
3.041
.011
.020
.289
1
.591
1.011
.972
1.050
-.054
.047
1.284
1
.257
.948
.864
1.040
InitialSysBP
.210
.100
4.371
1
.037
1.233
1.013
1.501
InitialHR
.006
.046
.015
1
.902
1.006
.918
1.101
Start
Initial_CI
-.185
.832
.049
1
.824
.831
.163
4.243
Step 1a
NIRSCInitial
.116
.097
1.410
1
.235
1.123
.927
1.359
NIRSRInitial
.031
.082
.139
1
.709
1.031
.877
1.212
Hgb
-.767
.523
2.147
1
.143
.465
.167
1.295
Retic
.324
.169
3.657
1
.056
1.383
.992
1.927
wbctotal
.000
.000
1.524
1
.217
1.000
1.000
1.001
-30.531
14.607
4.369
1
.037
.000
.117
.047
6.194
1
.013
1.124
1.025
1.232
Hgb
-.509
.369
1.909
1
.167
.601
.292
1.238
Retic
.268
.136
3.894
1
.048
1.307
1.002
1.705
-11.973
5.948
4.052
1
.044
.000
Age_months
Weight
Constant
InitialSysBP
Finish
Step 9a
Constant
a.
Variable(s) entered on step 1: Gender, Age_months, Weight, InitialSysBP, InitialHR, Initial_CI, NIRSCInitial, NIRSRInitial, Hgb,
Retic, wbctotal.
The above table of results shows that when initial information is considered, the outcome (admit vs.
discharge) is predicted best by only 2 independent variables: Initial SBP (higher is more likely to be
admitted); and Reticulocytes (higher is more likely to be admitted). The “Exp(B)” column is the odds
ratio for each variable. Note that the significance (p-value) for hemoglobin is not significant. All other
initial information in this model was not predictive in this (small: N=30) dataset.
Page 13 of 17
Recursive Partitioning using Initial Information to Predict Disposition
Figure 1.
This tree is the best predictor of the
disposition given all data available,
including maximum and minimum
values for Cardiometry and NIRS.
Blue is discharged, red is admitted.
The tree has the following 2x2
table:
Predicted
Class = DISCHARGED
Class
Cases %
ADMITTED
20 51.3
DISCHARGED 19 48.7
N = 39
RETIC <=
Actually
Actually
Admitted
Discharged
18
4
2
15
8.1
RETIC >
Class = DISCHARGED
Class
Cases %
ADMITTED
5 25.0
DISCHARGED 15 75.0
N = 20
Class = ADMITTED
Class
Cases %
ADMITTED
15 78.9
DISCHARGED
4 21.1
N = 19
Admitted
Predicted
Discharged
For predicting admission using all
predictor data available, the test
characteristics of this tree are:
Sensitivity 90% (95% CI: 67-98%)
Specificity 79% (54-93%)
PPV
82% (59-94%)
NPV
88% (62-98%)
LR+
4.3 (1.8-10.3)
LR0.13 (0.03-0.48)
8.1
INITIALSYSBP <= 142.5
INITIALSYSBP > 142.5
Class = DISCHARGED
Class
Cases %
ADMITTED
2 11.8
DISCHARGED 15 88.2
N = 17
Class = ADMITTED
Class
Cases %
ADMITTED
3
100.0
DISCHARGED 0
0.0
N=3
Note that none of the NIRS or Cardiometry data appear useful in predicting patient disposition.
Also note that the confidence intervals are relatively large due to the dataset N=39 being relatively
small.
Page 14 of 17
DISCUSSION
This was a prospective observational study, that was feasible, non invasive, well accepted and it did not
interfere with the standard of care of the subjects. Our purpose was to observe circulatory changes
during fluid resuscitation. The utility of the standardized management was not studied.
From our results we observed that the mean time taken to reach the maximum cardiac output (CO) and
cardiac index (CI) values are about the same (CO= 57.06 mins with sd=42.696, CI=57.46 mins with
sd=41.097). Both the CO and CI had 50 mins as the median duration to reach the maximum values for
the respective indices. This is understandable as the CI is a function of the CO by definition.
We observed that the outcomes: admission and discharge were best predicted by only 2 independent
variables: Initial Systolic Blood Pressure (SBP) and the reticulocyte count. The initial SBP (higher is more
likely to be admitted); and Reticulocytes (higher is more likely to be admitted) were significant in our
anaylsis (Initial SBP: p=0.013, Reticulocytes: p=0.048).
We feel that the following may explain some of the observations. Acute Pain is known to cause an
increase in systolic blood pressure (SBP) through sympathetic activity 13, and the sickle cell pain, known
as pain crisis is caused by vaso-occlusion. This vaso-occlusion usually leads to anemia. Increased sickling
is associated with the loss of RBC’s which leads to increased production of red blood cells from the
marrow resulting in increase in precursors of red blood cells ( reticulocytes)
The p-value for hemoglobin (p=0.167) is not significant. The NIRS and Cardiometry data were not useful
in predicting the outcome (admission, discharge) as their p-values were high (p>0.05). The confidence
interval was wide due to the small sample analyzed. All other initial pieces of information in this model
were not predictive in this (small: N=30) dataset.
Adverse Events
There were no observed nor reported adverse events with our study.
LIMITATIONS:
One of the aims of the study was to compare the results obtained with the figures at baseline. The
baseline was to be obtained at the point of follow up but most of the subjects have not been compliant.
Secondly, the required personnel were not always available to recruit the subjects at the outpatient
clinic. Hence there was no comparison.
The number of subjects recruited was still small (N=39) over a period of 2 years. This makes
generalization of our finding difficult.
For the above, we hope to correct them as the recruitment is still ongoing. We also hope to expand on
the data we are collecting to include more interventions in the ER to find possible associations on
analysis.
This was a pilot study, and there were not too many references to compare with.
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CONCLUSION
The initial systolic blood pressure and reticulocyte count were significant for determining the outcome;
thus the higher the initial SBP and reticulocyte count, the higher the chance of being admitted.
The Cardiac output, cardiac index and the NIRS (cerebral and splanchnic) values were not significant in
determining the outcome based on this model.
These obtained values were not compared with the baseline due to unavailability of data. The subjects
did poorly with follow up at the outpatient clinic, and the absence of personnel hampered the collection
of data when the subjects were present.
ACKNOWLEDGEMENT
I would specially like to acknowledge my mentor and PI for this study, Dr Pradeep Padmanabhan who
has been very helpful all through this research project, a good guide and a helper with editing this
paper. My sincere thanks go to Dr Keith Cross of Kosairs Hospital, Louisville KY who helped with the data
analysis. I would like to thank Dr Muldoon who helped with the editing of this paper and gave a lot of
support in the course of this work.
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