The prognostic value of arterial blood gas
parameters in ST-elevation myocardial infarction
patients who underwent percutaneous coronary
intervention
Name: Jake Prins
Student number: 1796062
Supervisor: Prof. dr. P. van der Harst
Department of Cardiology, University medical center Groningen (UMCG)
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Table of Contents Summary .................................................................................................................................... 3
Samenvatting .............................................................................................................................. 3
List of abbreviations ................................................................................................................... 4
Introduction ................................................................................................................................ 5
Background ............................................................................................................................ 5
Definition and pathophysiology of an AMI ........................................................................... 5
Diagnosis and treatment of an AMI ....................................................................................... 6
Epidemiology of AMI ............................................................................................................ 8
Arterial blood gas analysis ..................................................................................................... 8
Aim ......................................................................................................................................... 9
Relevance ............................................................................................................................... 9
Material and Methods ............................................................................................................... 10
Study population .................................................................................................................. 10
Data collection and ABG analysis ....................................................................................... 10
Clinical classifications .......................................................................................................... 11
Primary outcome .................................................................................................................. 11
Statistical analysis ................................................................................................................ 11
Results ...................................................................................................................................... 12
Study population .................................................................................................................. 12
Baseline characteristics of the derivation set ....................................................................... 13
Univariate analysis ............................................................................................................... 14
Prediction model .................................................................................................................. 16
Risk-score development ....................................................................................................... 17
Internal validation of the risk score ...................................................................................... 18
Discussion ................................................................................................................................ 22
Limitations ........................................................................................................................... 23
Conclusion ................................................................................................................................ 23
Acknowledgements .................................................................................................................. 23
References ................................................................................................................................ 24
3
Summary
The prognostic value of arterial blood gas (ABG) parameters in patients presenting with an
ST-elevation myocardial infarction (STEMI) who were treated with percutaneous coronary
intervention (PCI) has not been established. Therefore, the primary aim of this study was to
determine if ABG parameters are predictors of long-term clinical outcome in this population.
A secondary aim was to develop a practical risk score based on the prediction model.
This is a retrospective study of 678 STEMI patients who received PCI at the University
Medical Center Groningen (UMCG) between 2008 and 2010. The cohort was split into a
derivation set (452 patients) to derive the prediction model and a validation set (226 patients)
to validate the risk score. Data was obtained from the hospital STEMI registry. The primary
endpoint was all-cause 1-year mortality. For the risk score, each independent predictor was
assigned weighted points proportional to their ß-coefficient. Patients were divided into low-
and high-risk groups based on their individual risk scores.
The main ABG parameters were not associated with 1-year all-cause mortality. After
multivariate regression analysis, hemoglobin was the only ABG parameter which
demonstrated significant prognostic value. The final prediction model consisted of age, heart
rate, hemoglobin, cardiogenic shock (CS) and peak troponin T. After dichotomizing the
predictors, only age, anemia and CS remained significant and were used for the risk score.
The c-statistic of the risk score for 1-year all-cause mortality was 0.85 in the derivation set
and 0.89 in the validation set. The 1-year mortality rates in the low risk groups were 2.7% and
1.5% and in the high risk groups 31% and 40% in the derivation and validation sets,
respectively. The findings suggest that the main ABG parameters offer limited prognostic
value in STEMI patients who received PCI. The developed practical risk score accurately
predicts long-term clinical outcome.
Samenvatting
De prognositische waarde van arterieel bloed gas (ABG) parameters in patiënten met een ST-
segment elevatie myocard infarct (STEMI) die zijn behandeld met PCI is nog niet bekend. De
primaire doelstelling van dit onderzoek is derhalve om te analyseren of ABG parameters
voorpsellers zijn van lange termijn mortaliteit in deze populatie. Een secundair doel was om
een praktische risicoscore te ontwikkelen gebaseerd op het voorspellend model.
Dit is een retrospectief onderzoek van 678 STEMI patiënten die zijn behandeld met PCI in het
Universitair Medisch Centrum Groningen (UMCG) tussen 2008 en 2010. De cohort werd
onderverdeeld in een derivatieset (452 patiënten) en een validatieset (226 patiënten). Data
werd verkregen uit het STEMI register van het ziekenhuis. De primaire uitkomstmaat was 1-
jaars mortaliteit. Voor de risicoscore kreeg elke individuele voorspeller gewogen punten
toegwezen proportioneel aan hun ß-coëfficiënt. Patiënten werden ingedeeld in een lage of
hoge risicogroep baserend op hun individuele risicoscore.
De voornaamste ABG parameters waren niet geassocieerd met 1-jaars mortaliteit. Na
multivariate regressieanalyse was hemoglobine de enige ABG parameter die significante
prognostische waarde toonde. Het uiteindelijk voorspellend model bestond uit leeftijd,
hartfrequentie, hemoglobine, cardiogene shock (CS) en de piekwaarde van troponine T. Na
het binair maken van de voorspellers, bleven alleen leeftijd, anemie en CS significant en
werden vervolgens gebruikt voor de risicoscore. De c-statistiek van de risicoscore voor 1-
jaars mortaliteit was 0.85 in de derivatieset en 0.89 in de validatieset. De 1-jaars mortaliteit in
de lage risicogroep was 2.7% en 1.5% en in de hoge risicogroep 31% en 40% in de derivatie-
en validatieset respectievelijk. De gevonden resultaten suggereren dat de voornaamste ABG
parameters weinig prognostische waarde bieden in STEMI patiënten welke zijn behandeld
met PCI. De ontwikkelde simpele risicoscore voospeld lange termijn klinische uitkomst
nauwkeurig.
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List of abbreviations
Abbreviation Definition
ABG Arterial blood gas
ACS Acute coronary syndrome
AMI Acute myocardial infarction
AUC Area under the curve
Bpm Beats per minute
CABG Coronary artery bypass graft
CHD Coronary heart disease
CI Confidence interval
CK Creatine kinase
CK-MB Creatine kinase – myocardial band
COHb Carboxyhemoglobin
CRP C-reactive protein
CS Cardiogenic shock
cTn Cardiac troponin
CVD Cardiovascular disease
CX Circumflex artery
ECG Electrocardiogram
GRACE The Global Registry of Acute Coronary Events
Hb Hemoglobin
HDL High-density lipoprotein
IABP Intra-aortic balloon pump
IHD Ischemic heart disease
LAD Left anterior descending artery
LBBB Left bundle branch block
LDL Low-density lipoprotein LMS Left main stem
MBG Myocardial blush grade
MetHb Methemoglobin
NSTEMI Non-ST-elevation myocardial infarction
NT-proBNP N-terminal pro-brain natriuretic peptide
OHCA Out-of-hospital-cardiac-arrest
OR Odds ratio
PCI Percutaneous coronary intervention
RCA Right coronary artery
ROC Receiver operating curve
STEMI ST-elevation myocardial infarction
TIMI Thrombolysis in myocardial infarction
UMCG University Medical Center Groningen
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Introduction
Background
Cardiovascular disease (CVD) remains a significant burden to society, accounting for more
than 4 million deaths within Europe which amounts to 45% of all deaths (1). The term CVD
covers a broad range of diseases, of which cerebrovascular disease and coronary heart disease
(CHD) together, are responsible for almost 3 million deaths (1). Acute coronary syndrome
(ACS) is an umbrella term which encompasses unstable angina, non-ST-elevation myocardial
infarction (NSTEMI) and ST-myocardial infarction (STEMI) which are all forms of CHD (2).
The Global Registry of Acute Coronary Events (GRACE) is a multinational registry, which to
date entails one of the most comprehensive epidemiological data collection regarding ACS
(3). In the more recent expanded GRACE, consisting of almost 32,000 patients hospitalized
with ACS, the prevalence of unstable angina, STEMI and NSTEMI was 26%, 31% and 32%,
respectively (3). NSTEMI and STEMI are further classified as an acute myocardial infarction
(AMI), more commonly known as a heart attack (2).
Definition and pathophysiology of an AMI
An ACS occurs due to the interruption of blood flow, and therefore oxygen supply, to a
certain part of the myocardium, the muscle tissue of the heart. During an AMI, the blood flow
is diminished to such an extent that an imbalance in the oxygen supply and demand occurs,
leading to myocardial ischemia (4). Prolonged myocardial ischemia consequently leads to
myocardial necrosis which is an essential criterion for the definition of an AMI (4). This is
where the distinction is made between an AMI and unstable angina. During unstable angina,
the ischemia is not severe enough to result in cellular necrosis (2).
Five different types of AMI can be distinguished based mainly on differences in
pathophysiology (4). Type 1 MI is predominantly responsible for most cases of AMI. It is
characterized by the rupture of a previously stable atherosclerotic plaque in one or more of the
coronary arteries. This rupture then
stimulates the clotting cascade leading to
the formation of a blood clot, known as a
thrombus (Figure 1) (2). The intraluminal
thrombus occludes the coronary artery
resulting in decreased myocardial
perfusion and consequently decreased
oxygen supply to the cardiac muscle cells
known as cardiomyocytes. Being
deprived of oxygen, a switch from aerobic
metabolism to anaerobic in the
cardiomyocytes will ensue and, as a
consequence, hydrogen ions and lactate
will gradually accumulate (6). Acidosis
progressively develops which is
responsible for the eventual myocardial
cell death (necrosis) that follows (6). The
time frame in which this cascade of events occurs is as short as 20 minutes. After this period,
the downstream heart tissue becomes necrotic and will not regenerate (4,6). Ventricular
Figure 1. Intraluminal thrombus (5)
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dysfunction is a common phenomenon following an AMI as a result of the myocardial
damage. As a consequence, the inability of the cardiac ventricles to function properly
frequently leads to the development of heart failure, which occurs in approximately 25% of
the cases after an AMI (7,8). Table 1. Major risk factors for CHD
Several risk factors have been identified that
predispose or contribute to developing CHD
and, consequently, AMI. These can either
be classified as non-modifiable or as
modifiable risk factors (Table 1) (6). Many
of these factors are intertwined meaning that
when several risk factors coexist, it would
substantially increase the risk of developing
CHD. Therefore, managing and preventing
modifiable risk factors would significantly
decrease a person’s risk of eventually
developing an AMI.
Diagnosis and treatment of an AMI
In order to make the diagnosis of an acute
MI, certain criteria have to be satisfied. A
necessary criterion is the observation of an
increase and/or decrease of a cardiac
biomarker, with one or more of the values
above the 99th
percentile of the upper
reference limit of the reference assay (4). A
definite diagnosis can only be made when
the aforementioned criterion is observed in combination with at least one of the following (4):
Symptoms of ischemia
Significant ST-segment-T wave abnormalities (newly discovered or presumed new)
or left bundle branch block (LBBB)
ECG evidence of pathological Q wave development
Evidence of damaged myocardium or abnormal regional wall motion from imaging
techniques
Angiographically or by autopsy detected intraluminal thrombus
Cardiac biomarkers have become increasingly important in the diagnosis of an AMI. They are
released as a result of myocardial necrosis and increased levels can be detected in the blood of
a patient. The two most commonly utilized biomarkers are cardiac proteins troponin I and T
(cTn), and the isoenzyme creatine kinase-myocardial band (CK-MB) (2). Due to its sensitivity
and specificity for cardiomyocyte injury, cTn (especially high-sensitivity) is preferred over
other biomarkers (2). Since levels in the blood only rise several hours after the MI, initiating
treatment for a suspected MI should not be delayed due to awaiting the test results for cardiac
biomarkers. Besides their applicability in diagnosing an AMI, they also have important
prognostic value in regards to short-and long-term mortality (2).
The classic clinical symptom associated with myocardial ischemia is acute chest pain (angina)
persisting for at least 20 minutes, which may radiate to the neck, left arm or the jaw (7).
Non-modifiable Modifiable
Increasing age
Male sex
Certain races
and ethnicities
Family
history of
heart disease
Hypertension
Smoking tobacco
Blood cholesterol
profile:
- Elevated low-
density-
lipoprotein
(LDL)
cholesterol
- Low levels of
high-density-
lipoprotein
(HDL)
- Elevated
triglycerides
- Elevated total
cholesterol
Physical inactivity
Obesity
Diabetes mellitus
Alcohol intake
Diet and nutrition
Stress
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Chest pain
Normal
ECG
ST-depression or
T-wave inversion
ST-elevation
STEMI NSTEMI Unstable angina
Elevated Biomarkers Elevated Biomarkers Normal Biomarkers
Atypical symptoms, which may accompany the angina or present on its own, include nausea,
dyspnea, syncope, sweating, fatigue or palpitations (7). An estimated 30% of STEMI patients
experience atypical symptoms resulting in delayed or even missed diagnoses and treatment
(7).
An indispensable diagnostic tool in the assessment of a patient with a suspected ACS is a 12-
lead electrocardiogram (ECG). Prompt interpretation (within 10 minutes) of the ECG findings
by a qualified physician is the recommended target for all patients presenting with clinical
symptoms of ischemia (2). The ECG findings assist physicians to distinguish between the
three types of ACS, as well as to identify the culprit artery which is occluded. Unstable angina
and NSTEMI either show a normal ECG, ST-segment depression or inverted T waves (Figure
2) (2). In contrast to NSTEMI, cardiac biomarkers are not elevated in unstable angina which
helps in making the distinction between the two (4). During a STEMI on the other hand, ST-
segment elevation on two consecutive leads can be observed as well as T-wave inversion
(Figure 2) (2).
Figure 2. Diagnosing ACS (9)
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The overall aim of treatment is to restore blood flow to the affected area as soon as possible.
Initially, pharmacological therapy with anti-ischemic, analgesic and anti-thrombotic
medication should be initiated in patients with unstable angina and NSTEMI (2). This should
be followed by coronary angiography and, if indicated, by reperfusion therapy via
percutaneous coronary intervention (PCI) (2). Patients suspected of a STEMI, whose
symptoms presented no longer than 12 hours ago or presenting with ongoing ischemia, should
directly undergo PCI due to an increased risk of mortality (7). However, performing PCI in
stable patients, with an onset of symptoms longer than 12 hours ago, has not proven to be
beneficial (7). Patients ineligible to be treated with PCI may require a coronary artery bypass
graft (CABG) (7).
Epidemiology of AMI
Ischemic heart disease (IHD), mostly driven by AMI, accounts for the majority of deaths and
is the leading cause of premature death in Europe. Each year, roughly 19% and 20% of all
deaths among men and women respectively are attributable to IHD (10). The incidence of
STEMI has seen a steady decline over the past two decades, whereas that of NSTEMI has
slightly increased (7). Due to therapeutic advancements in the management of ACS, mortality
following a STEMI has also seen a gradual decline in recent years (7). Nevertheless, the 6-
month mortality rate for STEMI patients lies around 12%, with the majority of deaths
occurring in high-risk patients (7). The in-hospital mortality rate of STEMI patients ranges
from 6-14% in European countries. Short-term mortality of NSTEMI patients is lower
compared to STEMI patients but equalizes for long-term (1-year) mortality (2).
Arterial blood gas analysis
Arterial blood gas (ABG) analyses are routine point-of-care tests used in intensive care and
emergency settings in order to quickly monitor the acid-base balance as well as electrolyte
values of a patient (11). Acid-base and electrolyte disturbances can cause many complications
during a vulnerable state such as an AMI (7). Therefore, timely diagnosis and management of
abnormalities can often mean the difference between life and death in an emergency setting.
ABG parameters can be measured reliably within minutes of arrival at the emergency
department making it a valuable diagnostic tool for assessing a patient’s status. The five most
commonly measured parameters in ABG analysis are pH, bicarbonate (HCO3-), oxygen
saturation (sO2), and partial pressure of oxygen (pO2) and carbon dioxide (pCO2). Additional
parameters include hematocrit, hemoglobin, oxyhemoglobin, methemoglobin (MetHb),
carboxyhemoglobin (COHb), electrolytes (particularly sodium and potassium) and lactate
(11).
In a nationwide prospective cohort study, Park et al. demonstrated the prognostic value of
ABG analysis by finding that acidosis was a strong predictor of 12-month mortality in high-
risk acute heart failure patients (12). Similarly, Burri et al. reported a lower pH to be an
independent predictor of mortality after 12 months in patients with acute dyspnea, which is a
common symptom of acute heart failure (13). Increased arterial lactate levels on admission in
STEMI patients have previously been associated with adverse clinical outcome and a
generally worse response to PCI (14,15).
ABG analysis has proven useful in predicting clinical outcome in several clinical settings and
may have considerable potential in the risk stratification and therapy guidance of AMI
patients. However, few studies have been conducted to examine the prognostic value of ABG
parameters in the setting of an AMI.
9
Aim
The primary aim of this study was to determine ABG predictors of long-term clinical outcome
as well as to develop an easily applicable risk score to stratify STEMI patients who underwent
primary PCI.
Relevance
Prompt risk stratification on admission and facilitating appropriate interventions is essential in
order to reduce the mortality rate within the AMI population. Clinical prediction models and
accompanying risk scores are practical tools to distinguish patients based on their risk of an
adverse outcome and to aid therapeutic decision making. In order to facilitate triage of
patients, risk scores should ideally be accurate at predicting clinical outcome and simple
enough to apply at the bedside.
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Material and Methods
Study population
This is a retrospective cohort study where all patients hospitalized with a STEMI who
underwent primary PCI, between March 2008 and April 2010 at the University Medical
Center Groningen (UMCG), were eligible for inclusion. The inclusion and exclusion criteria
can be found in table 2. Informed consent was not a requirement for the ethics committee as
this involved a retrospective analysis.
A split-sample method was used to randomly divide the population into a derivation set (2/3)
and a validation set (1/3). The derivation set was used to derive the prediction model and the
subsequent risk score while the validation set was used for internal validation of the risk
score.
Table 2. Inclusion and exclusion criteria
Inclusion criteria Exclusion criteria
- Admitted to the
UMCG via the
STEMI protocol
between 17th March
2008 and 26th
- Received PCI
- Age below 18 years
- Missing ABG data
- Missing follow-up data
- Venous blood sample
- Patients with an out-of-
hospital-cardiac-arrest
(OHCA)
Data collection and ABG analysis
Data was obtained from the hospital STEMI registry in which all STEMI patients were
prospectively enrolled and data was electronically collected from 2004 onwards. All patients
were treated according to the then valid guidelines for the management of AMI patients
presenting with ST-segment elevation. The registry included information on demographics
and baseline characteristics, risk factors for CVD, medical history, data on performed
interventions, and laboratory test results. Information on mortality was obtained from hospital
medical files. Blood samples for ABG analysis were taken on admission prior to PCI in the
cardiac catheterization laboratory. ABG analysis was standard procedure for every patient
hospitalized with a STEMI between 2008-2010 at the UMCG. Measurements included PaO2,
PaCO2, sO2, pH, HCO3-, COHb, potassium (K), lactate, MetHb and Hb.
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Clinical classifications
Cardiogenic shock (CS) was defined as systolic blood pressure on admission of < 90 mmHg
or the use of an intra-aortic balloon pump (IABP) (7,16). An IABP delivers hemodynamic
support by mechanically pumping blood and is indicated during CS at the catheterization
laboratory.
Anemia was defined according to the criteria set out by the World Health Organization, which
are as follows: hemoglobin (Hb) value of < 12 g/dL for females and < 13 g/dL for males (17).
Biomarkers were assessed at several moments during hospitalization. Peak values for troponin
T and N-terminal pro-brain natriuretic peptide (NT-proBNP) were determined between day 0
(admission) and day 6 of hospitalization. For creatine kinase (CK) and CK-MB, peak values
were determined within the first 24 hours of hospitalization.
Primary outcome
The principal clinical endpoint of this study was all-cause 1-year mortality. The predictive
value of ABG parameters was evaluated based on this endpoint.
Statistical analysis
Data is presented as mean ± standard deviation for continuous variables if normally
distributed or median with interquartile ranges for skewed distributions. The unpaired T-test
and the Mann-Whitney U test were used to determine differences between means and medians
respectively. Dichotomous variables were analyzed using the Pearson’s chi-square test.
Logistic regression analysis was used to identify individual predictors of the defined endpoint.
All variables with p≤ 0.10 in the univariate analysis were considered potential predictors of
all-cause mortality and entered the multivariable stage. Candidate variables were checked for
correlation. Stepwise backward elimination, in which sequential deletion of the least
significant variable leads to a model with only significant predictors remaining, was applied
to construct a final multivariate prediction model adjusted for age and sex. Independent
predictors resulting from the multivariate analysis are presented with odds ratios (OR) with
their 95% confidence intervals (CI). To develop the ensuing risk score, the identified
independent predictors were dichotomized and assigned weighted points based on their β
coefficients. The cut-off value with the maximum sum of sensitivity and specificity was used
unless specified otherwise. Weighted points were calculated by dividing the β-coefficients by
the lowest β value in the multivariate model and rounding to the nearest integer. Individual
risk scores were then calculated by adding the points per risk factor per patient and the
derivation cohort was divided into two groups: low and high risk of death. The log-rank test
was used to determine if there is a significant difference in survival between the two risk
groups. Kaplan-Meier survival curves were created to portray the risk of death per group. The
discriminative ability of the model as well as the risk score was assessed by calculating the
area under (AUC) the receiver operating characteristic (ROC) curves (C-statistic). P-values
<0.05 were considered statistically significant for all analyses. All statistical tests were
performed using the STATA software version 14.0 (StataCorp, College Station, Texas, USA).
12
Results
Study population
Overall, 969 STEMI patients were admitted to the UMCG in the period of March 2008 - April
2010 and eligible for inclusion to the study (Figure 3). Of these, 250 patients were excluded
due to missing ABG data, 24 patients were excluded due to the occurrence of an OHCA, 11
were excluded due to only having a venous blood sample and a further 6 were excluded due to
having a negative COHb value (Figure 3). As a result, 678 patients met the inclusion criteria
and were included in the final analysis. The derivation set consisted of 452 patients and the
validation set of 226 patients.
969 STEMI
patients who
underwent PCI
between March
2008 and April
2010
719 patients with
available ABG
data
678 patients
remaining for
final analysis
250 patients
excluded due to
missing ABG data
Excluded:
- 24 OHCA
- 11 venous blood
sample
- 6 negative
COHb value
452 patients in
derivation set
226 patients in
validation set
Figure 3. Flow chart of the study population
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Baseline characteristics of the derivation set
The baseline characteristics of the survivors compared to the non-survivors as well as all
patients in the derivation set are shown in table 3. The average age of the whole population
was 64.5 ±10 years and the majority of patients were male (75.4%). Follow-up data was
available for all 452 patients. One year after study enrollment, a total of 28 (6.2%) patients
died in the derivation set. Non-survivors were on average older compared to survivors
(78.5±8 vs. 63±10). Non-survivors also had a higher prevalence of prior MI and a lower
prevalence of a positive family history of CVD. Furthermore, they had a slightly lower body
weight, a lower systolic blood pressure and a faster heart rate on admission. Additionally they
presented with a worse Myocardial Blush Grade (MBG), had a longer total ischemic time,
more frequently received balloon pre-dilatation, had a worse Thrombolysis in Myocardial
Infarction (TIMI) flow after PCI and experienced cardiogenic shock more frequently. Plasma
blood levels of CRP, creatinine, HbA1c and NT-proBNP were higher in non-survivors
compared to survivors. The ABG values of pO2, pCO2, sO2, HCO3- and Hb were lower
whereas potassium levels were significantly higher in non-survivors compared to survivors.
Table 3. Baseline characteristics of derivation set
Variable All patients Survivors Non-survivors P-
value
Number of patients 452 424 28
Demographics
Age (years) 64.5 ±10 63±10 78.5 ±8 <0.001
Gender 0.34
Male 341 (75.4%) 322 (75.9%) 19 (67.9%)
Female 111 (24.6%) 102 (24.1%) 9 (32.1%)
Cardiovascular risk factors
Hypertension 181 (40.6%) 168 (40.0%) 13 (50.0%) 0.31
Diabetes mellitus 56 (12.4%) 51 (12.0%) 5 (17.9%) 0.36
Hypercholesterolemia 116 (28.5%) 108 (28.1%) 8 (34.8%) 0.49
BMI (kg/m2) 26.7 (24.3; 29.4) 26.8 (24.5; 29.4) 25.7 (22.9; 28.4) 0.11
Smoking 227 (51.0%) 214 (51.2%) 13 (48.1%) 0.76
Family history 190 (43.8%) 187 (45.8%) 3 (11.5%) <0.001
Medical history
MI 48 (10.7%) 41 (9.8%) 7 (25.0%) 0.012
PCI 32 (7.1%) 28 (6.7%) 4 (14.3%) 0.13
CABG 10 (2.2%) 9 (2.1%) 1 (3.6%) 0.62
Physical examination
Height (cm) 176 (170; 181) 176 (170; 181) 175 (165; 178) 0.12
Weight (kg) 83 (73; 94) 83 (73; 95) 80 (70; 85) 0.044
Systolic blood pressure
(mmHg)
127 (110; 145) 128 (110; 145) 112 (94; 140.5) 0.034
Diastolic blood pressure
(mmHg)
75 (65; 84) 75 (65; 84) 69.5 (50; 80) 0.054
Heart rate (bpm) 76 (65; 88) 76 (64; 88) 84.5 (74; 106) 0.016
Culprit vessel 0.13
RCA 171 (37.8%) 165 (38.9%) 6 (21.4%)
LAD 203 (44.9%) 188 (44.3%) 15 (53.6%)
CX 65 (14.4%) 60 (14.2%) 5 (17.9%)
CABG 4 (0.9%) 4 (0.9%) 0 (0.0%)
LMS 9 (2.0%) 7 (1.7%) 2 (7.1%)
14
Angiographic results
Vessel disease 0.58
1 189 (41.9%) 179 (42.3%) 10 (35.7%)
2 139 (30.8%) 131 (31.0%) 8 (28.6%)
3 123 (27.3%) 113 (26.7%) 10 (35.7%)
MGB <0.001
0/1 132 (30.4%) 113 (27.7%) 19 (73.1%)
2 161 (37.1%) 157 (38.5%) 4 (15.4%)
3 141 (32.5%) 138 (33.8%) 3 (11.5%)
Anterior MI 212 (46.9%) 195 (46.0%) 17 (60.7%) 0.13
PCI results
Ischemic time (min) 187.5 (125; 300) 182 (124; 290) 245 (160; 490) 0.015
Balloon pre-dilatation 173 (38.3%) 156 (36.8%) 17 (60.7%) 0.012
Balloon post-dilatation 56 (12.4%) 52 (12.3%) 4 (14.8%) 0.70
Thrombus aspiration 403 (89.2%) 381 (89.9%) 22 (78.6%) 0.063
TIMI pre 0.91
0/1 273 (60.4%) 255 (60.1%) 18 (64.3%)
2 105 (23.2%) 99 (23.3%) 6 (21.4%)
3 74 (16.4%) 70 (16.5%) 4 (14.3%)
TIMI post <0.001
0/1 9 (2.0%) 7 (1.7%) 2 (7.4%)
2 52 (11.6%) 43 (10.2%) 9 (33.3%)
3 388 (86.4%) 372 (88.2%) 16 (59.3%)
CS 59 (13.1%) 46 (10.8%) 13 (46.4%) <0.001
Laboratory results
Creatinine (mg/dL) 75.5 (64; 88) 75 (63; 88) 84.5 (75; 130.5) <0.001
CRP (mg/dL) 2 (2; 7) 2 (2; 6) 12.5 (2; 46.5) <0.001
HbA1c (%) 5.8 (5.6; 6.2) 5.8 (5.5; 6.1) 6.1 (5.8; 6.4) <0.001
Lactate (mg/dL) 1.5 (1.1, 2.1) 1.5 (1.1; 2.1) 1.45 (1.05; 2.3) 0.98
CK max (U/L) 1225 (508.5; 2606) 1225 (505.5; 2566) 1244 (556; 4199) 0.62
CK-MB max (U/L) 150 (68.5; 303.5) 150 (66.5; 302) 153 (90; 438) 0.47
NT-proBNP max (ng/mL) 286 (80; 1130) 244.5 (76; 968.5) 2386 (593; 8185) <0.001
Troponin T max (ng/mL) 2.93 (.94; 7.16) 2.83 (.94; 6.73) 4.06 (1.48; 12.25) 0.071
ABG results
pH 7.42 (7.39; 7.45) 7.42 (7.39; 7.45) 7.42 (7.37; 7.45) 0.40
pO2 (kPa) 12.9 (10.5; 16.5) 13 (10.7; 16.6) 11.4 (9.3; 12.8) 0.008
pCO2 (kPa) 4.75 (4.26; 5.23) 4.77 (4.29; 5.24) 4.41 (3.98; 4.97) 0.048
sO2 (%) 98 (97; 99) 98.3 (97; 99) 97.7 (95; 98) 0.005
HCO3- (mmol/L) 22.6 (21; 24.2) 22.6 (21.1; 24.2) 21.1 (19.7; 23.4) 0.006
COHb (%) 1.45 (1; 2.8) 1.45 (1; 2.8) 1.45 (1.05; 2) 0.37
Hb (g/dL) 14.02 (12.88; 14.98) 14.02 (13.13; 14.98) 12.32 (10.39; 13.45) <0.001
MetHb (%) 0.009 (0.008; 0.011) 0.009 (0.008; 0.01) 0.01 (0.008; 0.011) 0.40
Glucose (mmol/L) 8.7 (7.4; 10.5) 8.6 (7.4; 10.5) 9.15 (7.45; 11.1) 0.50
Potassium (mmol/L) 3.7 (3.5; 4) 3.7 (3.5; 3.9) 4 (3.8; 4.25) <0.001
Univariate analysis
Initially, all baseline parameters presented in table 3 were considered potential predictors of
mortality and were included in the univariate logistic regression analysis. The results are
shown in table 4. The ABG parameters pO2, HCO3-, Hb and potassium demonstrated a
significant association with an increased risk of 1-year mortality. In addition, age, a family
history of CVD, prior MI, body weight, systolic and diastolic blood pressure, heart rate,
MBG, culprit vessel, balloon pre-dilatation, TIMI flow post PCI, CS, CRP, CK max, CK-MB
15
max, NT-proBNP max and troponin T max also showed a significant association with 1-year
mortality.
Table 4. Univariate analysis results
Variable Coefficient 95% CI P-value
Demographics
Age 0.101 0.061; 0.141 <0.001
Gender
Female 0.402 -0.421; 1.226 0.338
Cardiovascular risk factors
Hypertension 0.405 -0.388; 1.198 0.316
Diabetes mellitus 0.464 -0.547; 1.474 0.368
Hypercholesterolemia 0.310 -0.577; 1.196 0.494
BMI -0.099 -0.213; 0.015 0.088
Smoking -0.122 -0.901; 0.657 0.759
Family history -1.870 -3.089; -0.651 0.003
Medical history
MI 1.125 0.211; 2.039 0.016
PCI 0.850 -0.276; 1.976 0.139
CABG 0.528 -1.574; 2.630 0.623
Physical examination
Height -0.041 -0.089; 0.006 0.088
Weight -0.033 -0.063; -0.003 0.033
Systolic blood pressure -0.022 -0.038; -0.006 0.006
Diastolic blood pressure -0.035 -0.062; -0.009 0.009
Heart rate 0.027 0.007; 0.046 0.007
Angiographic results
Multi-vessel disease 0.229 -0.233; 0.692 0.331
MBG -1.277 -1.932; -0.623 <0.001 Anterior MI 0.596 -0.186; 1.378 0.135
Culprit vessel 0.420 0.036; 0.804 0.032
PCI results
Ischemic time <0.000 <-0.000; <0.000 0.467
Balloon pre-dilatation 0.976 0.193; 1.760 0.015
Balloon post-dilatation 0.218 -0.882; 1.319 0.697
Thrombus aspiration -0.882 -1.838; 0.074 0.071
TIMI pre -0.115 -0.640; 0.409 0.268
TIMI post -1.205 -1.827; -0.582 <0.001
CS 1.963 1.160; 2.766 <0.001
Laboratory results
Creatinine 0.005 <-0.000; 0.010 0.053
CRP 0.019 0.011; 0.027 <0.001
HbA1c 0.230 -0.101; 0.561 0.173
Lactate 0.165 -0.110; 0.439 0.239
CK max <0.000 <0.000; <0.000 0.037
CK-MB max 0.002 <0.000; 0.003 0.007
NT-proBNP max <0.000 <0.000; <0.000 0.011
Troponin T max 0.082 0.041; 0.123 <0.001
ABG results
pH -4.437 -10.85; 1.974 0.175
pO2 -0.142 -0.255; -0.030 0.013
pCO2 -0.386 -0.853; 0.080 0.105
sO2 -5.181 -10.80; 0.439 0.071
16
HCO3- -0.196 -0.316; -0.076 0.001
COHb -0.306 -0.677; 0.066 0.107
Hb -0.746 -1.006; -0.487 <0.001 MetHb 18.50 -16.30; 53.30 0.297
Glucose 0.018 -0.113; 0.148 0.790
Potassium 0.216 0.114; 0.317 <0.001
Prediction model
All univariate variables with a p-value of ≤ 0.10 were checked for multicollinearity before
being included in the multivariate analysis. If a correlation existed, the variable which was
most significant in the univariate analysis was included in the multivariate analysis. BMI was
excluded due to correlation with body weight; CK-MB and CK were excluded due to a strong
correlation with troponin T; and systolic blood pressure was excluded due to correlation with
CS. The following 20 candidate predictors were included in the multivariate regression
analysis to derive the final model with independent predictors: total serum hemoglobin,
oxygen saturation, COHb, potassium, pCO2, pO2, HCO3-, sex, age, body weight and height,
diastolic blood pressure, heart rate, family history of CVD, prior MI, balloon predilatation,
thrombus aspiration, TIMI score post PCI, culprit vessel, MBG, CS, troponin T max, NT-
proBNP max, creatinine, and CRP. Since complete data for each patient is a necessity for
multivariate analysis, missing data for one or more variables limited the analysis to 423
patients (93.6%). After stepwise backward elimination, the final model, which was adjusted
for age and sex, comprised 5 independent predictors; age, heart rate, total serum Hb, CS and
troponin T (Table 5). The final model demonstrated a strong discriminative ability with a c-
statistic of 0.94 (Figure 4). Hb is the only ABG parameter which remained in the final model.
Predictors were then dichotomized for the sake of practical applicability of the ensuing risk
score.
Table 5. Final prediction model
Variable OR 95% CI P-value
Age 1.15 1.08 ; 1.23 <0.001
Heart rate 1.03 1.01 ; 1.06 0.008
Hb 0.42 0.29 ; 0.61 <0.001
Cardiogenic shock 4.73 1.37 ; 16.28 0.014
Troponin T max 1.13 1.05 ; 1.21 0.002
17
Figure 4. AUC for the prediction model
Risk-score development
The risk score was established by assigning each independent predictor weighted points. For
this, the β-coefficient of each variable was divided by the lowest β-coefficient (corresponding
to CS) and rounded to the nearest integer (Table 6). The sum of the points per risk factor was
calculated to derive individual scores. After dichotomizing the variables, only significant risk
factors were included in the risk score. Each risk factor corresponded to 1 point with a
maximum score of 3 points. Finally, patients were divided into risk groups according to their
survival estimates: low risk (0-1 points) and high risk (2-3 points) (Figure 5). This resulted in
87.8% being assigned to the high risk group and 12.2% to the low risk group. In the high risk
group, 31% of the patients died compared to 2.7% in the low risk group. The c-statistic of the
risk score was 0.85 (Figure 6).
Table 6. Risk score
Variable OR 95% CI P-value ß-coeff. Score
Age >75 years 5.69 2.34 ; 13.82 <0.001 0.4366 1
Heart rate >88 bpm 1.76 0.72 ; 4.32 0.218
Anemia 4.61 1.93 ; 11.04 0.001 0.3714 1
Cardiogenic shock 4.73 2.03 ; 12.94 0.001 0.3294 1
Troponin T max >3.5 (ng/L) 1.96 0.77 ; 4.94 0.156
0.0
00
.25
0.5
00
.75
1.0
0
Se
nsitiv
ity
0.00 0.25 0.50 0.75 1.001 - Specificity
Area under ROC curve = 0.9408
18
Figure 5. Survival curves of low- and high-risk groups in derivation set
Figure 6. AUC of the risk score in the derivation set
Internal validation of the risk score
The validation set consisted of 226 patients and their baseline characteristics are compared to
those of the derivation set in table 7. No significant differences were found between the
validation and derivation populations. The 1-year mortality rates were comparable between
the two populations; 6.2% in the derivation set and 6.6% in the validation set (P=0.824). In
the validation set, 86.7% were assigned to the low-risk group and 13.3% to the high-risk
group which is similar to the derivation set (P=0.682). Overall, the 1-year mortality rate was
1.5% in the low risk group compared to 40% in the high risk group (Figure 7). The risk score
was a strong predictor of 1-year mortality in the validation set with a c-statistic of 0.89
(Figure 8).
p < 0.001
0.6
50
.70
0.7
50
.80
0.8
50
.90
0.9
51
.00
55 41 40 39 0High risk397 389 388 387 0Low risk
Number at risk
0 100 200 300 400analysis time
Low risk High risk
Kaplan-Meier survival estimates
0.0
00
.25
0.5
00
.75
1.0
0
Se
nsi
tivity
0.00 0.25 0.50 0.75 1.001 - Specificity
Area under ROC curve = 0.8488
19
Table 7. Baseline characteristics between derivation and validation set
Variable Derivation set Validation set P-value
Number of patients 424 226
Demographics
Age (years) 64.5±10 65 ±10 0.67
Gender 0.49
Male 341 (75.4%) 165 (73.0%)
Female 111 (24.6%) 61 (27.0%)
Cardiovascular risk factors
Hypertension 181 (40.6%) 91 (41.7%) 0.78
Diabetes mellitus 56 (12.4) 24 (10.6%) 0.50
Hypercholesterolemia 116 (28.5%) 54 (26.9%) 0.67
BMI (kg/m2) 26.7 (24.3, 29.4) 26.2 (24.2, 28.4) 0.09
Smoking 227 (51.0%) 108 (48.2%) 0.49
Family history 190 (43.8%) 105 (48.6%) 0.24
Medical history
MI 48 (10.7%) 20 (8.9%) 0.46
PCI 32 (7.1%) 15 (6.6%) 0.81
CABG 10 (2.2%) 3 (1.3%) 0.42
Physical examination
Height (cm) 176 (170, 181) 177 (169.5, 182) 0.97
Weight (kg) 83 (73, 94) 81.5 (72, 90) 0.14
Systolic blood pressure (mmHg) 127 (110, 145) 127 (110, 145) 0.73
Diastolic blood pressure (mmHg) 75 (65, 84) 73 (65, 85) 0.94
Heart rate (bpm) 76 (65, 88) 77 (65, 92) 0.40
Culprit vessel 0.52
RCA 171 (37.8%) 91 (40.3%)
LAD 203 (44.9%) 96 (42.5%)
CX 65 (14.4%) 32 (14.2%)
CABG 4 (0.9%) 0 (0.0%)
LMS 9 (2.0%) 7 (3.1%)
Angiographic results
Vessel disease 0.68
1 189 (41.9%) 88 (39.3%)
2 139 (30.8%) 68 (30.4%)
3 123 (27.3%) 68 (30.4%)
MGB 0.77
0/1 132 (30.4%) 61 (27.9%)
2 161 (37.1%) 86 (39.3%)
3 141 (32.5%) 72 (32.9%)
Anterior MI 212 (46.9%) 103 (45.6%) 0.74
PCI results
Ischemic time (min) 187.5 (125, 300) 175 (118, 260.5) 0.22
Balloon pre-dilatation 173 (38.3%) 82 (36.3%) 0.61
Balloon post-dilatation 56 (12.4%) 25 (11.1%) 0.62
Thrombus aspiration 403 (89.2%) 200 (88.5%) 0.80
TIMI pre 0.91
0/1 273 (60.4%) 135 (59.7%) 0.44
2 105 (23.2%) 46 (20.4%)
3 74 (16.4%) 45 (19.9%)
TIMI post 0.97
0/1 9 (2.0%) 5 (2.2%)
2 52 (11.6%) 25 (11.1%)
3 388 (86.4%) 195 (86.7%)
20
CS 59 (13.1%) 26 (11.5%) 0.57
Laboratory results
Creatinine (mg/dL) 75.5 (64, 88) 78 (67, 92) 0.12
CRP (mg/dL) 2 (2, 7) 2 (2, 6) 0.29
HbA1c (%) 5.8 (5.6, 6.2) 5.8 (5.6, 6.1) 0.22
Lactate (mg/dL) 1.5 (1.1, 2.1) 1.5 (1.1, 2.1) 0.43
CK max (U/L) 1225 (508.5, 2606) 1432.5 (579, 2823) 0.38
CK-MB max (U/L) 150 (68.5, 303.5) 179.5 (72.5, 349.5) 0.21
NT-proBNP max (ng/mL) 286 (80, 1130) 263 (76, 1568 0.76
Troponin T max (ng/mL) 2.93 (.94, 7.16) 3.61 (1.04, 7.97) 0.17
ABG results
pH 7.42 (7.39, 7.45) 7.42 (7.39, 7.45) 0.83
pO2 (kPa) 12.9 (10.5, 16.5) 13 (10.4, 16.9) 0.93
pCO2 (kPa) 4.75 (4.26, 5.23) 4.68 (4.24, 5.13) 0.23
sO2 (%) 98 (97, 99) 98 (97, 99) 0.71
HCO3- (mmol/L) 22.6 (21, 24.2) 22.2 (20.7, 23.8) 0.06
COHb (%) 1.45 (1, 2.8) 1.4 (1, 2.6) 0.55
Total Hb (g/dL) 14.02 (12.88,14.98) 14.02 (12.88, 14.98) 0.45
MetHb (%) .009 (.008, .011) .009 (.008, .01) 0.20
Glucose (mmol/L) 8.7 (7.4, 10.5) 8.7 (7.4, 10.5) 0.89
Potassium (mmol/L) 3.7 (3.5, 4) 3.7 (3.5, 4) 0.99
Figure 7. Survival curves of low- and high-risk groups in validation set
0.5
00
.60
0.7
00
.80
0.9
01
.00
30 20 19 18 0High risk196 195 194 194 0Low risk
Number at risk
0 100 200 300 400analysis time
Low risk High risk
Kaplan-Meier survival estimates
21
Figure 8. AUC of the risk score in the validation set
0.0
00
.25
0.5
00
.75
1.0
0
Se
nsi
tivity
0.00 0.25 0.50 0.75 1.001 - Specificity
Area under ROC curve = 0.8979
22
Discussion
The present retrospective study implies that the main parameters of an ABG analysis are not
associated with long-term, all-cause mortality in STEMI patients who underwent PCI. The
only ABG parameter which was of prognostic value, after comprehensive correction for
multiple variables, was hemoglobin. In addition, age, heart rate, cardiogenic shock and
troponin T were significant independent predictors of long-term clinical outcome. These
results suggest that the five main components of an ABG analysis are of limited value for
early triage of STEMI patients on admission. However, admission hemoglobin levels are of
valuable importance for distinguishing high risk from low risk STEMI patients.
There is a high prevalence of anemia among AMI patients with an increasing trend in the
elderly (18). This study shows that decreased Hb levels are associated with an increased risk
of 1-year mortality in STEMI patients who received PCI. This is in accordance with a
previous study by Sabatine et al., which analyzed a large cohort in the setting of ACS (18).
They found that baseline hemoglobin levels are a strong predictor of 30-day cardiovascular
mortality in STEMI patients with an increased mortality already observed at levels as high as
14 g/dL. Similarly, Maluenda et al. found that decreased baseline, as well as a drop after PCI
in hematocrit levels, was associated with 1-year mortality (19). Several mechanisms have
been proposed which may explain these findings (18). However, the management of anemia
in this setting seems to be problematic. Although blood transfusion has been shown to be
beneficial in anemic elderly AMI patients, the overall consensus is that it is associated with
increased all-cause mortality and should not be encouraged (20,21). Effective therapeutic
interventions are therefore warranted to manage and/or prevent anemia in the setting of an
AMI.
Few studies have analyzed the prognostic value of acid-base disturbances in STEMI patients.
Metabolic acidosis is frequently observed in the acute phase of an MI and if persistent, can be
an underlying cause of arrhythmias which increase short-term risk of death (7). In the current
study, no association was found between pH and 1-year mortality. A possible explanation of
this finding may be that metabolic acidosis is quickly corrected by respiratory compensation if
there is no coexisting pulmonary disease (22). Lactic acidosis, a subtype of metabolic
acidosis, is a common occurrence during CS (16). CS is a common complication of a STEMI,
arising in approximately 6-10% of all cases (7). It also continues to be the leading cause of in-
hospital death in patients presenting with a STEMI (7). Our findings did not confirm lactate as
a predictor of 1-year mortality; however, they do confirm that CS is a fatal complication in
STEMI patients.
The prognostic value of heart rate has been scarcely investigated in STEMI patients in the era
of PCI. It is a relevant modifiable risk factor which has been investigated in a variety of
cardiovascular diseases (23). One of the few studies performed, found that discharge heart
rate predicted mortality in a follow-up period of up to 4 years in STEMI patients treated with
PCI (24). Parodi et al. is the only study which looked at admission heart rate to the best of our
knowledge. They concluded that a heart rate of 80 bpm or above significantly increased the
risk of death in STEMI patients treated with PCI (25). Heart rate was also an independent
predictor in our multivariate model although with a very modest OR. Moreover, when heart
rate was dichotomized for the risk score, it did not remain to be a significant predictor.
23
Due to the complex pathophysiology and wide spectrum of clinical presentations of a STEMI,
risk scores are useful to help narrow down who is at greatest risk of an adverse outcome.
The derived risk score, using only three risk factors, accurately stratified patients into low and
high risk groups. In contrast to the guideline recommended risk scores such as GRACE and
TIMI, the risk score we developed in this study is specific for STEMI patients who underwent
PCI. The TIMI risk score was originally designed for patients receiving fibrinolytic therapy
whereas the GRACE risk score was developed for patients along the whole ACS spectrum
(26,27). The presentation and prognosis of NSTEMI and STEMI differ substantially making
the joint risk score less reliable. For example, anemia on admission was a strong prognostic
factor in STEMI patients in the current risk score which is not incorporated in the above
mentioned risk scores. Furthermore, the GRACE risk score consists of a complex scoring
system which cannot be easily calculated at the bedside, restricting its applicability. The
current risk score uses readily available parameters making it simple and practical for rapid
risk stratification of patients at the bedside. Further analyses are required to find out the
transportability of this risk score to shorter- or longer-term mortality.
Limitations
This study has several limitations which need to be taken into consideration. First, the study
cohort was relatively small and a low overall mortality rate was observed compared to other
prediction models. Ideally, the cohort should consist of a large representative population for
the development of prediction models and the subsequent risk score. Our study could have
been subject to selection bias since it is of a retrospective nature and patients with missing
data were excluded. Moreover, previously determined prognostic factors such as Killip class
or some electrocardiographic findings were not included in the present analysis (27,28). In
addition, the risk score was internally validated on a subgroup of the same population
restricting the generalizability to other populations. External validation in a different and
preferably larger population is desirable to further test the risk scores’ performance and
robustness.
Conclusion
In conclusion, the current findings do not support the use of the main ABG parameters as
predictors of 1-year all-cause mortality in STEMI patients treated with PCI. The risk score,
developed from relevant clinical variables, accurately stratified patients into a low-risk and
high-risk group. It is a simple and reliable bedside tool with a good discriminative ability but
needs external validation before it could potentially be applied clinically. It can aid physicians
in allocating resources and to initiate more aggressive therapy for patients at high risk of
death. Prospective studies are needed to determine which interventions are appropriate and
effective for managing STEMI patients in the high risk group.
Acknowledgements
I would like to take this opportunity to thank Pim van der Harst for his time, support and
expertise throughout this research project. I am also very grateful for the (especially
statistical) support Lawien Al Ali and Tom Hendriks have provided. Lastly, I would like to
thank the whole research group for making this an enjoyable experience.
24
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