Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease
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Derivation and External Validation of a High-Sensitivity Cardiac Troponin-Based Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease. / McCarthy, Cian P; Neumann, Johannes T; Michelhaugh, Sam A; Ibrahim, Nasrien E; Gaggin, Hanna K; Sörensen, Nils A; Schäefer, Sarina; Zeller, Tanja; Magaret, Craig A; Barnes, Grady; Rhyne, Rhonda F; Westermann, Dirk; Januzzi, James L.
in: J AM HEART ASSOC, Jahrgang 9, Nr. 16, e017221, 18.08.2020.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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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 -