Transcript Sensitive Troponin I Assay in Early Diagnosis of Acute

Sensitive Troponin I Assay in Early
Diagnosis of Acute Myocardial
Infarction
Journal Club
Mohammed AlShamsi
R4
OUTLINES
• Receiver-Operating-Characteristic Curves
(ROC).
• Introduction.
• Methods.
• Statistical Analysis.
• Results.
• Critical appraisal.
Receiver Operating Characteristic
Curve (ROC)
• The name "Receiver Operating Characteristic"
came from "Signal Detection Theory" developed
during World War II for the analysis of radar
images.
• Radar operators had to decide whether a blip on
the screen represented an enemy target, a friendly
ship, or just noise.
• Signal detection theory measures the ability of
radar receiver operators to make these important
distinctions. Their ability to do so was called the
Receiver Operating Characteristics.
• ROC curves were developed in the 1950's as a byproduct of research into making sense of radio signals
contaminated by noise.
• During 1970's that signal detection theory was
recognized as useful for interpreting medical test
results.
• More recently it's become clear that they are
remarkably useful in medical decision-making
• ROC curves plot the sensitivity of a test versus its false
positive rate for various points
• ROC analysis has wide applicability in radiology
research for comparing observers, modalities, and
tests.
T4 value
Hypothyroid
Euthyroid
5 or less
18
1
5.1 - 7
7
17
7.1 - 9
4
36
9 or more
3
39
Totals:
32
93
T4 value
Hypothyroid
Euthyroid
5 or less
18
1
>5
14
92
Totals:
32
93
T4 value
Hypothyroid
Euthyroid
7 or less
25
18
>7
7
75
Totals:
32
93
Cutoff Level
Sensitivity
Specificity
5
0.563
0.989
7
0.781
0.806
9
0.906
0.419
• The sensitivity and
specificity of a diagnostic
test depends on more
than just the "quality" of
the test.
• They also depend on the
definition of what
constitutes an abnormal
test.
• No test distinguish
normal from disease with
100% accuracy.
• The area of overlap
indicates where the test
cannot distinguish normal
from disease.
•
•
•
•
This graph shows three ROC curves
representing excellent, good, and
worthless tests plotted on the same
graph.
The accuracy of the test depends on
how well the test separates the group
being tested into those with and
without the disease in question.
Accuracy is measured by the area
under the ROC curve. An area of 1
represents a perfect test; an area of
.5 represents a worthless test.
A rough guide for classifying the
accuracy of a diagnostic test is the
traditional academic point system:
0.90 – 1.00 = excellent
0.80 - 0.90 = good
0.70 - 0.80 = fair
0.60 - 0.70 = poor
Introduction
• Cardiac troponin testing is central to the diagnosis of
acute myocardial infarction
• An early diagnosis of AMI facilitates rapid decision
making and treatment and therefore improves the
outcome in patients presenting with chest pain.
• Introduction of cardiac markers is a milestone in
cardiology.
• The sensitivity of the available cardiac markers is weak
within the first hours after the onset of chest pain
????????
Is Sensitive trop sensitive enough?
Methods
Study Population.
• They enrolled 1818 consecutive patients
presenting with new-onset chest pain at
chest-pain units at three German study
centers between January 2007 and
December 2008
• Study was approved by local ethics
committees
• All patients provided written informed
consent
• Inclusion:
1. Age between 18 to 85 years
2. Angina pectoris or equivalent
• Exclusion criteria
1. Major surgery or trauma within the previous 4
weeks
2. Pregnancy
3. IV drug abuse
4. Hemoglobin level <10 g/dL.
• Blood was drawn for routine blood work and
sample storage at admission, 3 and 6 hours
after
• Concordantly, a 12-lead ECG was obtained.
• All patients were followed up 30 days after
initial hospitalization
• Additionally , the local civil registry office
provided information about death.
• Follow-up information was available in 98% of
the population.
Outcome measures:
• composite of death.
• MI.
• Stroke.
• Hospital admission because of cardio-vascular
reasons .
• Need for unplanned coronary intervention
• Chest pain onset time was carefully assessed
by independent research staff.
• Troponin I Ultra was purchased from Siemens
Healthcare Diagnostics, which had no role in
the design of the study or analysis of the
results.
In-house troponin
• TropT (Roche Diagnostics, Germany) or
• Trop I (Siemens Healthcare Diagnostics).
• Detection limits are 0.01 ng/mL (Troponin T) and 0.04
ng/mL (Troponin I) with assay ranges of 0.01-25 ng/mL
(Troponin T) and 0.04-40 ng/mL (Troponin I),
respectively.
• Reference limits based on the 99th percentile for a
healthy population are 0.01 ng/mL (Troponin T) and
0.07 ng/mL (Troponin I) with 10% CV cut-offs of 0.03
ng/mL (Troponin T) and 0.14 ng/mL (Troponin I),
respectively.
Laboratory Methods
Investigational troponin
• Investigational troponin I was measured with the
TnI-Ultra assay on an ADVIA Centaur XP system
(Siemens Healthcare Diagnostics, Germany).
• The assay range is 0.006-50 ng/mL.
• The reference limit based on the 99th percentile
for a healthy population is 0.04 ng/mL
Adjudication of the Final Diagnosis
• Based on all available clinical, laboratory, and
imaging findings
• Adjudicated by an expert committee of two
independent cardiologists who were unaware
of the results of the troponin I assays
Diagnosis Based on Conventional
Troponin Assays
• Primary diagnosis of AMI was adjudicated according to current guidelines .
Evidence of myocardial necrosis that was consistent with
myocardial ischemia, together with clinical symptoms
of ischemia
or
ECG changes indicative of new ischemia (new ST-segment or T-wave
changes or new LBBB)
or
Imaging evidence of new loss of viable myocardium
or
Detection of a culprit lesion on coronary angiography
Diagnosis Based on Sensitive Troponin I
Assay
• They used the concentration of 0.04 ng per
mL as the upper reference limit and
established the diagnosis of myocardial
infarction if one value of more than 0.04 ng
per mL was documented,
combined with a rise or fall in the value of
30% or more within 6 hours after admission.
Statistical Analysis
• Skewed variables were described by median and interquartile
range.
• Normally distributed variables were characterized by their
arithmetic mean and standard deviation.
• They calculated ROC curves on the basis of the continuously
measured biomarker levels by taking every measured biomarker
level as a cutoff value and then deriving sensitivity and specificity
values from the resulting two-by-two tables for each cutoff value.
• The diagnosis variable used for the two by two tables contrasted
non-coronary chest pain against acute myocardial infarction.
• P values are based on the Wald z-test statistic
• All statistical analyses were performed with
the use of R software (version 2.8.1) and SAS
software (version 9.2).
Results
Low or Moderate Troponin I Levels
585 pts
86 pts
0-006-0.04
6
H
115 pt > 0.04
30 pts diagnosed as
AMI according to
trop T
Moder. Elevated.
(>0.04 ) without rise or fall (<30%)in
repeated testing.
62 had a distinct noncoronary diagnosis
Short-Term Outcome
• A troponin I level of more than 0.04 ng per
milliliter was independently associated with
an increased risk of an adverse outcome at 30
days (hazard ratio, 1.96; 95% confidence
interval, 1.27 to 3.05; P=0.003).
Assessment of validity
Was there an independent, blind comparison with a
reference standard (gold standard) of diagnosis?
Was the diagnostic test evaluated in an appropriate
spectrum of patients?
Were all patients analyzed in the groups to which they
were randomized (intention to treat analysis)?
Was the test, or group of tests, validated in a second,
independent group of patients?
Assessment of importance of the
results
• Are likelihood ratios or data necessary for
their calculation provided for the diagnostic
test?
Target Disorder
Present
Diagnostic Test
Result
Totals
Absent
Positive
a
b
a+b
c
d
c+d
a+c
b+d
a+b+c+d
Negative
Totals
Sensitivity = a/(a+c)
Specificity = d/(b+d)
Likelihood ratio for a positive test result =
LR+ = sens/(1-spec)
Likelihood ratio for a negative test result =
LR - = (1-sens)/spec
Positive Predictive Value = a/(a+b)
Negative Predictive Value = d/(c+d)
Pre-test probability (prevalence) =
(a+c)/(a+b+c+d)
Post-test odds=prevalence/(1-prevlance) x
LR
Post-test probability = post-test odds/(post-test
odds +1)
Assessment of Utility:
Will the reproducibility of the test result and its
interpretation be satisfactory in my setting?
Are the results applicable to my patient?
Will the results change my management?
Will patients be better off as a result of the test?