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)

2

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.

4

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

5

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)

6

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

7

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)

8

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.

10

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.

11

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

13

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

References

(1) Townsend N, Wilson L, Bhatnagar P, Wickramasinghe K, Rayner M, Nichols M.

Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J 2016 Nov

7;37(42):3232-3245.

(2) Roffi M, Patrono C, Collet JP, Mueller C, Valgimigli M, Andreotti F, et al. 2015 ESC

Guidelines for the management of acute coronary syndromes in patients presenting without

persistent ST-segment elevation: Task Force for the Management of Acute Coronary

Syndromes in Patients Presenting without Persistent ST-Segment Elevation of the European

Society of Cardiology (ESC). Eur Heart J 2016 Jan 14;37(3):267-315.

(3) Goodman SG, Huang W, Yan AT, Budaj A, Kennelly BM, Gore JM, et al. The expanded

Global Registry of Acute Coronary Events: baseline characteristics, management practices,

and hospital outcomes of patients with acute coronary syndromes. Am Heart J 2009

Aug;158(2):193-201.e1-5.

(4) Thygesen K, Alpert JS, Jaffe AS, Simoons ML, Chaitman BR, White HD, et al. Third

universal definition of myocardial infarction. Eur Heart J 2012 Oct;33(20):2551-2567.

(5) Peil P. Myocardial Infarction - The heart of the matter. 2014; Available at:

https://www.fanews.co.za/article/featured-disease/62/myocardial-infarction-the-heart-of-the-

matter/1350. Accessed 03/25, 2017.

(6) Overbaugh KJ. Acute coronary syndrome. Am J Nurs 2009 May;109(5):42-52; quiz 53.

(7) Steg PG, James SK, Atar D, Badano LP, Blomstrom-Lundqvist C, Borger MA, et al. ESC

Guidelines for the management of acute myocardial infarction in patients presenting with ST-

segment elevation: Task Force on the management of ST-segment elevation acute myocardial

infarction of the European Society of Cardiology (ESC). Eur Heart J 2012 Oct;33(20):2569-

2619.

(8) Minicucci MF, Azevedo PS, Polegato BF, Paiva SA, Zornoff LA. Heart failure after

myocardial infarction: clinical implications and treatment. Clin Cardiol 2011 Jul;34(7):410-

414.

(9) Klabunde R. Electrophysiological changes during cardiac ischemia. 2016; Available at:

http://www.cvphysiology.com/CAD/CAD012. Accessed 03/25, 2017.

(10) Wilkins E, Wilson L, Wickramasinghe K, Bhatnagar P, Leal J, Luengo-Fernandez R, et

al. European Cardiovascular Disease Statistics 2017 . 2017.

(11) Batra P, Dwivedi AK, Thakur N. Bedside ABG, electrolytes, lactate and procalcitonin in

emergency pediatrics. Int J Crit Illn Inj Sci 2014 Jul;4(3):247-252.

(12) Park JJ, Choi DJ, Yoon CH, Oh IY, Lee JH, Ahn S, et al. The prognostic value of arterial

blood gas analysis in high-risk acute heart failure patients: an analysis of the Korean Heart

Failure (KorHF) registry. Eur J Heart Fail 2015 Jun;17(6):601-611.

25

(13) Burri E, Potocki M, Drexler B, Schuetz P, Mebazaa A, Ahlfeld U, et al. Value of arterial

blood gas analysis in patients with acute dyspnea: an observational study. Crit Care

2011;15(3):R145.

(14) Lazzeri C, Valente S, Chiostri M, Gensini GF. Clinical significance of lactate in acute

cardiac patients. World J Cardiol 2015 Aug 26;7(8):483-489.

(15) Vermeulen RP, Hoekstra M, Nijsten MW, van der Horst IC, van Pelt LJ, Jessurun GA, et

al. Clinical correlates of arterial lactate levels in patients with ST-segment elevation

myocardial infarction at admission: a descriptive study. Crit Care 2010;14(5):R164.

(16) Khalid L, Dhakam SH. A review of cardiogenic shock in acute myocardial infarction.

Curr Cardiol Rev 2008 Feb;4(1):34-40.

(17) Nutritional anaemias. Report of a WHO scientific group. World Health Organ Tech Rep

Ser 1968;405:5-37.

(18) Sabatine MS, Morrow DA, Giugliano RP, Burton PB, Murphy SA, McCabe CH, et al.

Association of hemoglobin levels with clinical outcomes in acute coronary syndromes.

Circulation 2005 Apr 26;111(16):2042-2049.

(19) Maluenda G, Lemesle G, Collins SD, Ben-Dor I, Syed AI, Torguson R, et al. The clinical

significance of hematocrit values before and after percutaneous coronary intervention. Am

Heart J 2009 Dec;158(6):1024-1030.

(20) Wu WC, Rathore SS, Wang Y, Radford MJ, Krumholz HM. Blood transfusion in elderly

patients with acute myocardial infarction. N Engl J Med 2001 Oct 25;345(17):1230-1236.

(21) Chatterjee S, Wetterslev J, Sharma A, Lichstein E, Mukherjee D. Association of blood

transfusion with increased mortality in myocardial infarction: a meta-analysis and diversity-

adjusted study sequential analysis. JAMA Intern Med 2013 Jan 28;173(2):132-139.

(22) Seifter JL, Chang HY. Disorders of Acid-Base Balance: New Perspectives. Kidney Dis

(Basel) 2017 Jan;2(4):170-186.

(23) Custodis F, Reil JC, Laufs U, Bohm M. Heart rate: a global target for cardiovascular

disease and therapy along the cardiovascular disease continuum. J Cardiol 2013

Sep;62(3):183-187.

(24) Antoni ML, Boden H, Delgado V, Boersma E, Fox K, Schalij MJ, et al. Relationship

between discharge heart rate and mortality in patients after acute myocardial infarction treated

with primary percutaneous coronary intervention. Eur Heart J 2012 Jan;33(1):96-102.

(25) Parodi G, Bellandi B, Valenti R, Memisha G, Giuliani G, Velluzzi S, et al. Heart rate as

an independent prognostic risk factor in patients with acute myocardial infarction undergoing

primary percutaneous coronary intervention. Atherosclerosis 2010 Jul;211(1):255-259.

(26) GRACE Investigators. Rationale and design of the GRACE (Global Registry of Acute

Coronary Events) Project: a multinational registry of patients hospitalized with acute coronary

syndromes. Am Heart J 2001 Feb;141(2):190-199.

26

(27) Morrow DA, Antman EM, Charlesworth A, Cairns R, Murphy SA, de Lemos JA, et al.

TIMI risk score for ST-elevation myocardial infarction: A convenient, bedside, clinical score

for risk assessment at presentation: An intravenous nPA for treatment of infarcting

myocardium early II trial substudy. Circulation 2000 Oct 24;102(17):2031-2037.

(28) Fox KA, Dabbous OH, Goldberg RJ, Pieper KS, Eagle KA, Van de Werf F, et al.

Prediction of risk of death and myocardial infarction in the six months after presentation with

acute coronary syndrome: prospective multinational observational study (GRACE). BMJ

2006 Nov 25;333(7578):1091.