scholarly journals A pooled multi-national validation study of a machine learning, high-sensitivity troponin-based multi-proteomic model to predict the presence of obstructive coronary artery disease

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
J T Neumann ◽  
N A Sorenson ◽  
C P McCarthy ◽  
C A Magaret ◽  
R F Rhyne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Coronary Artery ◽  
Characteristic Curve ◽  
Obstructive Coronary Artery Disease ◽  
Kidney Injury ◽  
High Sensitivity ◽  
Multiple Protein ◽  
Artery Disease ◽  
Kidney Injury Molecule 1 ◽  
Coronary Obstruction

Abstract Background Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed. Purpose To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone. Methods Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as >50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or >70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone. Results The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p<0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p<0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p<0.001)) for oCAD (Figure 1). Conclusions In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable Data Figure 1. ROC for HART CADhs and hs-cTnI

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

2020 ◽  
Vol 9 (16) ◽  
Author(s):  
Cian P. McCarthy ◽  
Johannes T. Neumann ◽  
Sam A. Michelhaugh ◽  
Nasrien E. Ibrahim ◽  
Hanna K. Gaggin ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Predictive Value ◽  
Cardiac Troponin ◽  
Coronary Stenosis ◽  
Characteristic Curve ◽  
Obstructive Coronary Artery Disease ◽  
External Validation ◽  
High Sensitivity ◽  
Artery Disease

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.

scholarly journals Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus

Open Heart ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. e000955 ◽  
Author(s):  
Cian P McCarthy ◽  
Shreya Shrestha ◽  
Nasrien Ibrahim ◽  
Roland R J van Kimmenade ◽  
Hanna K Gaggin ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Peripheral Artery Disease ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Kidney Injury ◽  
Clinical Variable ◽  
Peripheral Artery ◽  
Peripheral Vessel ◽  
Artery Disease ◽  
Kidney Injury Molecule 1

BackgroundPatients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM.Methods and resultsThe HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up.ConclusionA clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM.Trial registration numberNCT00842868.

P3645Prognosis in relation to high-sensitivity C-reactive protein levels in patients with non-obstructive coronary artery disease

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
H W Zhang ◽  
Y X Cao ◽  
J L Jin ◽  
Y L Guo ◽  
Y Gao ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Obstructive Coronary Artery Disease ◽  
High Sensitivity ◽  
C Reactive Protein ◽  
Development Fund ◽  
Reactive Protein ◽  
Increased Risk ◽  
Hs Crp ◽  
Artery Disease

Abstract Background It has been reported that coronary artery disease (CAD) is characterized by inflammation and non-obstructive CAD (NOCAD) increases the risk of cardiovascular events (CVEs) compared with ones with normal or near-normal coronary arteries (NNCA), even is similar to obstructive CAD (OCAD). We hypothesized that elevated high-sensitivity C-reactive protein (hs-CRP) may be linked to CVEs in those patients with NOCAD. Purpose To investigate the predictive role of hs-CRP in patients with NOCAD. Methods Of 7,746 consecutive patients with angina-like chest pain admissions, 4,662 eligible patients were enrolled who received coronary artery angiography (CAG) and followed up for the CVEs comprising all-cause mortality, myocardial infarction, stroke and late revascularization. According to the results of CAG, the patients were classified as NNCA group (<20% stenosis, n=698, 15.0%), NOCAD group (20–49% stenosis, n=639, 14.3%), and OCAD group (≥50% stenosis, n=3325, 70.7%). They were further subdivided into 3 groups according to baseline hs-CRP levels (<1, 1–3 and >3 mg/L). Proportional hazards models were used to assess the risk of CVEs in all patients enrolled. Results A total of 338 patients (7.3%) experienced CVEs during an average of 13403 person-years follow-up. Patients with NOCAD and OCAD had higher rates of CVEs compared to those with NNCA (p<0.05, respectively). In Cox's models after adjustment of confounders, the risk of CVEs elevated with the increasing degrees of CAD with hazard ratio of 2.01 [95% confidence interval (95% CI): 1.07–3.79, p=0.03] for patients with NOCAD and 2.81 (95% CI: 1.60–4.93, p<0.001) for patients with OCAD compared with the NNCA group. Moreover, elevated hs-CRP levels were associated with the severity of coronary lesions and an elevated increased risk of CVEs in patients with NOCAD and OCAD compared those with NNCA (p<0.05, respectively). Conclusions Patients with NOCAD had indeed worse outcomes and hs-CRP levels were positively in relation to the CVEs in those with NOCAD, which may help to the risk assessment in ones with NOCAD. Acknowledgement/Funding This study was partly supported by Capital Health Development Fund (201614035) and CAMS Innovation Fund for Medical Sciences (2016-I2M-1-011) awarded

Troponin elevation: is it ischemia?

2020 ◽  
pp. 7-11
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Cardiac Troponin ◽  
Stress Test ◽  
Obstructive Coronary Artery Disease ◽  
Plasma Concentrations ◽  
High Sensitivity ◽  
Hemodynamic Overload ◽  
Artery Disease ◽  
Transient Elevation

Cardiac troponin is the preferred biomarker for myocardial infarction, thanks to its sensitivity and absolute specificity for the heart. The availability of high-sensitivity assays (hs-cTnT and hs-cTnI), capable of measuring with excellent analytical precision very low levels of circulating troponin, raised the issue of whether transient ischemia is a sufficient stimulus for troponin release. For this purpose, in a series of patients submitted to a stress test (exercise ECG/echo test; dipyridamole echo test; dobutamine echo test), we measured plasma levels of hs-cTnT at baseline and 6 hours after the end of the test. Plasma concentrations of hs-cTnT significantly increased in the vast majority of patients after the test. Significant elevations were documented in response to each stressor, regardless of the test result, after both positive and negative tests. Moreover, troponin significantly increased in response to the stress, both in patients with and in patients without obstructive coronary artery disease. Despite a good sensitivity (80% and 89%), troponin showed a very low specificity (32% and 47%) for stress-induced ischemia and coronary artery disease, respectively. Myocardial release of troponin is a multifactorial process, mediated not only by cardiomyocyte necrosis, but also through several different mechanisms such as myocardial ischemia, increase in cardiac work, and hemodynamic overload. Transient elevation of high sensitivity cardiac troponin is not a useful tool for detecting spontaneous or stress-induced ischemia. L

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