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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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  • 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

  • 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

  • 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

  • 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

  • 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

  • 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 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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