Presentation on theme: "The ACC/ESC Recommendation for 99 th Percentile of the Reference Normal Overestimates the Risk of an Acute Myocardial Infarct Jerard Kneifati-Hayek, Salman."— Presentation transcript:
The ACC/ESC Recommendation for 99 th Percentile of the Reference Normal Overestimates the Risk of an Acute Myocardial Infarct Jerard Kneifati-Hayek, Salman Haq, M.D, Madeleine Schlefer, Larry Bernstein, M.D. New York Methodist Hospital Cornell-Weill
Background A joint committee of the American College of Cardiology and European Society of Cardiology (ACC/ESC) has established the criteria for acute recent or evolving AMI predicated on a typical increase in troponin in the clinical setting of myocardial ischemia, which includes the 99 th percentile of a healthy normal population.
Problems in Recommendation 1.A Reference normal population doesn t present to the emergency room 2.The cutoff used to make a decision has an effect on overuse of hospital resources, especially costly and limited telemetry 3.The cutoff selection is not scientifically validated 4.The cutoff selection is informed by a potential projected risk extending to perhaps four months after the presenting event
Recommendation Challenged 1.Lin JC, Apple FS, Murakami MM, Luepker RV. Rates of positive cardiac troponin I and creatine kinase MB mass among patients hospitalized for suspected acute coronary syndromes. Clin Chem 2004. 2.Zarich SW, Bradley K, Mayall ID, Bernstein, LH. Minor Elevations in Troponin T Values Enhance Risk Assessment in Emergency Department Patients with Suspected Myocardial Ischemia: Analysis of Novel Troponin T Cut-off Values. Clin Chim Acta 2004.
Hypothesis An AMI is better identified by a TnI value exceeding the 99 th percentile of patients seen with acute coronary syndrome (ACS) who are subsequently excluded than using the ACC/ESC guideline.
Patient selection 1480 successive patients who presented to the emergency room at New York Methodist Hospital in two month periods in 2003 and 2004 and were required to have troponin I (TnI) were identified. No Randomization Observational study Exclusion Criteria: Pacemaker, ST elevation, patients under 30 years old Inclusion Criteria: ST depression, T wave inversion, LBBB, anginal equivalent chest pain, short breath
Patient Demographics Women 56% Men 44% 46% white, 32% Negro, 16% Hispanic, 6% Asian. ECG Findings: 24% were normal, 73.3% were nonspecific, and 55.2% were other than NSSTT changes. ECG findings of AMI were present 4.6 percent of the time.
Laboratory Methods The CKMB and TnI were measured on the Centaur (Bayer, Tarrytown, NY) at the time of presentation and at least 4 hours later.
Data Collection The information collected was – serum cardiac marker concentration, discharge diagnosis, ECG finding, chest pain characteristics. all relevant cardiac diagnoses, including, CHF, atrial or ventricular dysrhythmias, and other relevant diagnoses, such as, hypothyroid disease, hypertension and type 2 diabetes were included. Computerized ECGs were reviewed by two cardiologists
Reference Normal Population A control population of 140 blood donors was also measured for TnI. The control population TnI values were all less than 0.1 g/L.
Statistical evaluation The data was organized for paired comparisons of troponin I and CKMB. SPSS 11.5 (Chicago, IL) software was used for ROC curves and basic statistics. SYSTAT 10.0 (Chicago, IL) was used for logistic regression, Ordinal Regression, GOLDminer TM 3.0 (Statistical Innovation, Inc., Belmont, MA) and Latent Class Cluster Analysis, Latent Gold 2.0 (ibid).
Two Step Procedure 1.Trial set to determine best decision value Trial set was itself partitioned by an initial set for which half the charts were unavailable because of transition to a new information system 2.Second trial set had 619 patients 3.Validation set
ROC curve on 2 nd Training Set The area under the ROC curve is 99%. The best cutoff selected is 0.60 0.77 µ g/L at a sensitivity of 98.9% and a false positive rate at 4%. This is actually lower than the 99 th percentile of the non-MI population and significantly higher than the 99 th percentile of the healthy donor population, approximately the 97.5 th percentile of the ruled-out MI population.
Latent Class Analysis A latent class model partitions the data into separate, nonoverlapping sets (the fit measured by chi square). We assume that within each latent class, each variable is statistically independent of every other variable. This is referred to as conditional independence. However, within a latent class that corresponds to a distinct medical syndrome, while the presence/ absence of one symptom is viewed as unrelated to presence/absence of all others, this may NOT be the case.
Cluster1Cluster2 Overall Probability 0.91620.0838 Not MIMI TNISCA g/L 01.00000.0000< 0.3 10.99970.0003 0.3 – 0.6 20.00010.9999 0.61 – 1.2 30.00001.0000> 1.2 Eight percent of patients are in cluster 2, consistent with MI. A cutoff value of less than 0.61 is consistent with classification of not MI.
In the previous two models we fitted the data to a binary classification using the diagnosis for training (ROC), or using the information in the data combinations (LCA) to cluster into MI or not MI at a cutoff of 0.61. We next explored a method that allows us to look at the probability of MI using the diagnosis as a training value, and scaled values of troponin for the Y variable in an ORDINAL RESPONSE.
Ordinal Regression The situation we describe is an ordered response with graded categories.
Magidson has developed a unified maximum likelihood methodology for simultaneously assessing the statistical significance of treatment effects and the model fit when the response variable contains ordered categories. He explores the fit for different logit model extensions (log-odds) to data derived under the assumption of bivariate normality and finds that the parallel log-odds model based on adjacent odds often gives a better fit than the proportional odds model based on cumulative odds.
Frequency cross-table, probabilities and odds-ratios for scaled TnI versus expected diagnosis TnI scaled RangeNot MIMINPct in MI Prob by TnI Odds Ratio 0< 0.456552657201 10.46-0.670700.0313 20.61- 0.75 4040.0.26175 30.76-0.913597257.30.822307 4> 0.9042 40.80.9830482 679103782100
There are 13 TnI values between 0.76-0.9 that are not MI, that would be falsely classified as MI at a cutoff below 0.76, 17 at a cutoff below 0.61, and 24 at a cutoff below 0.46. The false positive rate at a cutoff of 0.61 is 2.5%.
We created a second data set including 322 patients from a population different than the trial population to validate the troponin I cutoff selected. See the frequencies of TnI at a cutoff of 0.15 µ g/L in rows versus the diagnosis of MI, and the frequencies of TnI at 0.65 µ g/L versus the diagnosis of MI.
Myocardial Risks Previous MI High Blood Pressure High Cholesterol Diabetes Age>65 Stroke, peripheral vascular disease Hypercholesterolemia Hypertension
LDL CholesterolLevel for Drug Consideration Goal of Therapy Without coronary heart disease and with fewer than two risk factors 190 mg/dL or higher* less than 160 mg/dL Without coronary heart disease and with two or more risk factors 160 mg/dL or higher less than 130 mg/dL With coronary heart disease130 mg/dL or higher** 100 mg/dL or less
0=Non MI 1=MI Comparison of TnI Level with Number of Active Medications in Groups of MI and Non MI Medscore 0= No Statins 1= 1 Statin Drug
The documentation of chest pain characteristics in the medical record is inconsistent, sometimes absent, and often unclear. This results in reliance on TnI and emphasis on ECG evidence for the diagnosis of MI, even though many patients lack a definitive ECG pattern. This places greater importance on the presence of risk factors, such as, diabetes, hypertension, hypercholesterolemia, previous MI, and anginal equivalent findings in addition to TnI in assessing the probability of MI. A similar model can be constructed using TnI and risk factors.