Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease

TY - JOUR

T1 - Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease

AU - McCarthy, Cian P

AU - Neumann, Johannes T

AU - Michelhaugh, Sam A

AU - Ibrahim, Nasrien E

AU - Gaggin, Hanna K

AU - Sörensen, Nils A

AU - Schäefer, Sarina

AU - Zeller, Tanja

AU - Magaret, Craig A

AU - Barnes, Grady

AU - Rhyne, Rhonda F

AU - Westermann, Dirk

AU - Januzzi, James L

PY - 2020/8/18

Y1 - 2020/8/18

N2 - Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs-cTn (high-sensitivity cardiac troponin)-based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs-cTnI [high-sensitivity cardiac troponin I], adiponectin, and kidney injury molecule-1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (P<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs-cTnI testing ("indeterminate zone," n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001). Conclusions A model including hs-cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs-cTnI concentrations.

AB - Background Current noninvasive modalities to diagnose coronary artery disease (CAD) have several limitations. We sought to derive and externally validate a hs-cTn (high-sensitivity cardiac troponin)-based proteomic model to diagnose obstructive coronary artery disease. Methods and Results In a derivation cohort of 636 patients referred for coronary angiography, predictors of ≥70% coronary stenosis were identified from 6 clinical variables and 109 biomarkers. The final model was first internally validated on a separate cohort (n=275) and then externally validated on a cohort of 241 patients presenting to the ED with suspected acute myocardial infarction where ≥50% coronary stenosis was considered significant. The resulting model consisted of 3 clinical variables (male sex, age, and previous percutaneous coronary intervention) and 3 biomarkers (hs-cTnI [high-sensitivity cardiac troponin I], adiponectin, and kidney injury molecule-1). In the internal validation cohort, the model yielded an area under the receiver operating characteristic curve of 0.85 for coronary stenosis ≥70% (P<0.001). At the optimal cutoff, we observed 80% sensitivity, 71% specificity, a positive predictive value of 83%, and negative predictive value of 66% for ≥70% stenosis. Partitioning the score result into 5 levels resulted in a positive predictive value of 97% and a negative predictive value of 89% at the highest and lowest levels, respectively. In the external validation cohort, the score performed similarly well. Notably, in patients who had myocardial infarction neither ruled in nor ruled out via hs-cTnI testing ("indeterminate zone," n=65), the score had an area under the receiver operating characteristic curve of 0.88 (P<0.001). Conclusions A model including hs-cTnI can predict the presence of obstructive coronary artery disease with high accuracy including in those with indeterminate hs-cTnI concentrations.

KW - Acute Kidney Injury/blood

KW - Adiponectin/blood

KW - Aged

KW - Biomarkers/blood

KW - C-Reactive Protein/analysis

KW - Coronary Artery Disease/blood

KW - Coronary Stenosis/blood

KW - Female

KW - Hepatitis A Virus Cellular Receptor 1/blood

KW - Humans

KW - Machine Learning

KW - Male

KW - Middle Aged

KW - Models, Biological

KW - Myocardial Infarction/blood

KW - Natriuretic Peptide, Brain/blood

KW - Predictive Value of Tests

KW - Prospective Studies

KW - Proteomics/methods

KW - ROC Curve

KW - Sensitivity and Specificity

KW - Sex Factors

KW - Troponin I/blood

U2 - 10.1161/JAHA.120.017221

DO - 10.1161/JAHA.120.017221

M3 - SCORING: Journal article

C2 - 32757795

VL - 9

JO - J AM HEART ASSOC

JF - J AM HEART ASSOC

SN - 2047-9980

IS - 16

M1 - e017221

ER -