Use of High-Sensitivity Cardiac Troponin for the Exclusion of Inducible Myocardial Ischemia: A Cohort Study

Multi-proteomic Biomarker Risk Scores for Predicting Risk and Guiding Therapy in Patients with Coronary Artery Disease

PURPOSE OF REVIEW

Patients with established coronary artery disease (CAD) are at high residual risk for adverse events, despite guideline-based treatments. Herein, we aimed to determine whether risk scores based on multiple circulating biomarkers that represent activation of various pathophysiologically important pathways involved in atherosclerosis and myocardial dysfunction help identify those at greatest residual risk.

RECENT FINDINGS

Numerous circulating proteins, representing dysregulation of the pathways involved in the development and stability of coronary and myocardial diseases, have been identified. When aggregated together, biomarker risk scores (BRS) more accurately stratify patients with established CAD that may help target interventions in those individuals who are at elevated risk. Moreover, intensification of guideline-based therapies has been associated with parallel improvements in both BRS and outcomes, indicating that these risk scores may be employed clinically to target therapy. Multi-protein BRS are predictive of risk, independent of, and in addition to traditional risk factor assessments in patients with CAD. Those with elevated risk may benefit from optimization of therapies, and improvements in the BRS will identify those with improved outcomes.

Diagnostic and prognostic value of H-ficolin for functionally relevant coronary artery disease

BACKGROUND

We aimed to test the diagnostic and prognostic ability of H-ficolin, an initiator of the lectin pathway of the complement system, for functionally relevant coronary artery disease (fCAD), and explore its determinants.

METHODS

The presence of fCAD was adjudicated using myocardial perfusion imaging single-photon emission tomography and coronary angiography. H-ficolin levels were measured by a sandwich-type immunoassay at rest, peak stress-test, and 2 h after stress-test. Cardiovascular death and non-fatal myocardial infarction were assessed during 5-year follow-up.

RESULTS

Among 1,571 patients (32.3 % women), fCAD was detected in 462 patients (29.4 %). H-ficolin concentration at rest was 18.6 (15.3-21.8) µg/ml in patients with fCAD versus 17.8 (15.4-21.5) µg/ml, p = 0.33, in patients without fCAD, resulting in an AUC of 0.53 (95 %CI 0.48-0.56). During follow-up, 107 patients (6.8 %) had non-fatal myocardial infarction and 99 patients (6.3 %) experienced cardiovascular death. In Cox regression analysis, H-ficolin was not a predictor of events in the overall cohort. Subgroup analysis suggested a potential link between H-ficolin and non-fatal myocardial infarction in patients without fCAD (adjusted HR 1.03, 95 % CI 1.02-1.15, p = 0.005). H-ficolin concentration showed a weak positive correlation with systolic (r = 0.069, p < 0.001) and diastolic blood pressure (r = 0.111, p < 0.001).

CONCLUSION

H-ficolin concentration did not have diagnostic and/or prognostic value in patients referred for fCAD work-up.

Can high-sensitive troponin levels within the normal range predict positivity in treadmill test?

BACKGROUND

This study aimed to determine whether a high level of high-sensitivity troponin T (hsTnT) predicts a positive treadmill test in patients with suspected stable ischemic heart disease (SIHD).

METHODS

In all, 366 patients with suspected SIHD were included in the study. We measured the serum hsTnT levels before the treadmill test. The treadmill test was performed according to the Bruce protocol.

RESULTS

Of the 366 patients, 97 had positive treadmill tests. The hsTnT levels were significantly higher in the positive group than in the negative group. In the binary logistic regression analysis, hsTnT, pretest probability, metabolic equivalents (METs), target heart rate (THR) percentage, and Duke treadmill score (DTS) were independent predictors of a positive treadmill test [hsTnT odds ratio (OR): 2.178, P  < 0.001; pretest probability OR: 1.036, P  = 0.007; METs OR: 0.755, P  = 0.008; THR OR: 0.773, P  < 0.001; DTS OR: 2.661, P  = 0.012]. In the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC) value of the model with the combined parameters of hsTnT, pretest probability, METs, THR, and DTS was statistically significant in predicting a positive treadmill test [combined model AUC: 0.945 (0.922-0.968), P  < 0.001].

CONCLUSIONS

In sum, high pretest hsTnT levels predicted a positive treadmill test in patients with suspected SIHD. Analysis of the hsTnT levels before the treadmill test can increase the sensitivity and specificity of the treadmill test. The methods for measuring hsTnT levels are cheap and easily accessible and can be used before the treadmill test in patients with suspected SIHD.

Serum neurofilament light chain in functionally relevant coronary artery disease and adverse cardiovascular outcomes

Background: Functionally relevant coronary artery disease (fCAD), causing symptoms of myocardial ischemia, can currently only be reliably detected with advanced cardiac imaging. Serum neurofilament light chain (sNfL) is a biomarker for neuro-axonal injury known to be elevated by cardiovascular (CV) risk factors and cerebrovascular small-vessel diseases. Due to their pathophysiological similarities with fCAD and the link to CV risk factors, we hypothesised that sNfL may have diagnostic and prognostic value for fCAD and adverse cardiovascular outcomes.Methods: Of the large prospective Basel VIII study (NCT01838148), 4'016 consecutive patients undergoing cardiac work-up for suspected fCAD were included (median age 68 years, 32.5% women, 46.9% with history of CAD). The presence of fCAD was adjudicated using myocardial perfusion imaging single-photon emission tomography (MPI-SPECT) and coronary angiography. sNfL was measured using a high-sensitive single-molecule array assay. All-cause and cardiovascular death, myocardial infarction (MI), and stroke/transient ischaemic attack (TIA) during 5-year follow-up were the prognostic endpoints.Results: The diagnostic accuracy of sNfL for fCAD as quantified by the area under the curve (AUC) was low (0.58, 95%CI 0.56-0.60). sNfL was strongly associated with age, renal dysfunction, and body mass index and was a strong and independent predictor of all-cause death, cardiovascular death, and stroke/TIA but not MI. Time-dependent AUC for cardiovascular-death at 1-year was 0.85, 95%CI 0.80-0.89, and 0.81, 95%CI 0.77-0.86 at 2-years.Conclusion: While sNfL concentrations did not show a diagnostic role for fCAD, in contrast, sNfL was a strong and independent predictor of cardiovascular outcomes, including all-cause death, cardiovascular death and stroke/TIA.

Cardiac Biomarkers in Sports Cardiology

Sustained physical activity induces morphological and functional changes in the cardiovascular system. While mostly physiological, they can also become a trigger for major adverse cardiovascular events, the most severe of which are sudden cardiac arrest and sudden cardiac death. Therefore, any novel method which can help more accurately estimate the cardiovascular risk should be considered for further studying and future implementation in the standard protocols. The study of biomarkers is gaining more and more ground as they have already established their utility in diagnosing ischemic cardiac disease or in evaluating cardiac dysfunction in patients with heart failure. Nowadays, they are being implemented in the screening of apparently healthy individuals for the assessment of the cardiovascular risk. The aim of this paper is to gather published data regarding the measurements of cardiac biomarkers in athletes, i.e., troponins, myoglobin, CK-MB, NT-proBNP, and D-Dimers, and their potential use in the field of sports cardiology.

Cardiopulmonary Exercise Testing and Cardiac Biomarker Measurements in Young Football Players: A Pilot Study

Constant and intense physical activity causes physiological adaptive changes in the human body, but it can also become a trigger for adverse events, such as sudden cardiac arrest or sudden cardiac death. Our main objective was to assess the use of combined cardiopulmonary exercise testing (CPET) and cardiac biomarker determinants in young professional athletes. We conducted a study which involved the full examination of 19 football players, all male, aged between 18 and 20 years old. They underwent standard clinical and paraclinical evaluation, a 12-lead electrocardiogram (ECG), and transthoracic echocardiography (TTE). Afterwards, a tailored CPET was performed and peripheral venous blood samples were taken before and 3 h after the test in order to determine five biomarker levels at rest and post-effort. The measured biomarkers were cardiac troponin I (cTnI), myoglobin (Myo), the MB isoenzyme of creatine-kinase (CK-MB), the N-terminal prohormone of brain natriuretic peptide (NT-proBNP) and D-dimers. While cTnI and NT-proBNP levels were undetectable both at rest and post-effort in all subjects, the variations in Myo, CK-MB and D-dimers showed significant correlations with CPET parameters. This highlights the potential use of combined CPET and biomarker determinants to evaluate professional athletes, and encourages further research on larger study groups.

Prediction of Significant Coronary Artery Disease Through Advanced Echocardiography: Role of Non-invasive Myocardial Work

Background: Non-invasive prediction of critical coronary artery stenosis (CAST) in patients with coronary artery disease (CAD) is challenging. Strain parameters can often capture an impairment of regional longitudinal function; however, they are load dependent. A novel non-invasive method to estimate Myocardial Work (MW) has been recently proposed, showing a strong correlation with invasive work measurements. Our aim was to investigate the ability of non-invasive MW to predict the ischaemic risk area underlying a CAST. Methods and Results: The study population comprises 80 individuals: 50 patients with CAST and 30 controls (CTRL). Echocardiography recordings were obtained before coronary angiography to measure global longitudinal strain (GLS), Myocardial Work Index (MWI), Myocardial Constructive Work (MCW), Myocardial Wasted work (MWW), Myocardial Work Efficiency (MWE). Global MWI (p = 0.048), MWE (p < 0.001), and MCW (p = 0.048) at baseline were significantly reduced in patients with CAST compared to controls (p < 0.05). Regional MWE within the myocardial segments underlying the CAST, but not LS, was significantly reduced compared to non-target segments (p < 0.001). At ROC analysis, the diagnostic performance to predict CAST for regional MWE (AUC = 0.920, p < 0.001) was higher compared to both regional post-systolic shortening index (PSI) (AUC = 0.600, p = 0.129) and regional LS (AUC = 0.546, p = 0.469). Conclusions: Non-invasive estimation of MW work indices is able to predict a CAST before invasive angiography.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

Objectives

To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19.

Methods

We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients.

Results

There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness.

Conclusions

We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation.

Key Points

• Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources.

• We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images.

• Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07957-z.

Relation of High-sensitivity Cardiac Troponin I Elevation With Exercise to Major Adverse Cardiovascular Events in Patients With Coronary Artery Disease

High sensitive cardiac troponin I (hs-cTnI) increases with inducible myocardial ischemia in patients with coronary artery disease (CAD). We aimed to assess if the change in hs-cTnI levels with exercise stress testing is associated with major adverse cardiac events (MACE). A cohort of 365 (age 62 ± 9 years, 77% men) patients with stable CAD underwent 99mTc sestamibi myocardial perfusion imaging with treadmill testing. Plasma hs-cTnI level was measured at rest and at 45 min after stress. Multivariable Fine & Gray's subdistribution hazards models were used to determine the association between the change in hs-cTnI and MACE, a composite end point of cardiovascular death, myocardial infarction, and unstable angina requiring revascularization. During a median follow-up of 3 years, 39 (11%) patients experienced MACE. After adjustment, for each two-fold increment in hs-cTnI with stress, there was a 2.2 (95% confidence interval 1.3-3.6)-fold increase in the hazard for MACE. Presence of both a high resting hs-cTnI level (>median) and ≥ 20% stress-induced hs-cTnI elevation was associated with the highest incidence of MACE (subdistribution hazards models 4.6, 95% confidence interval 1.6 to 13.0) compared with low levels of both. Risk discrimination statistics significantly improved after addition of resting and change in hs-cTnI levels to a model including traditional risk factors and inducible ischemia (0.67 to 0.71). Conversely, adding inducible ischemia by SPECT did not significantly improve the C-statistic from a model including traditional risk factors, baseline and change in hs-cTnI (0.70 to 0.71). In stable CAD patients, higher resting levels and elevation of hs-cTnI with exercise are predictors of adverse cardiovascular outcomes beyond traditional cardiovascular risk factors and presence of inducible ischemia.

Possible molecular and paracrine involvement underlying the pathogenesis of COVID-19 cardiovascular complications

Coronavirus disease 2019 (COVID-19) has been declared a pandemic on 11 March 2020 by the WHO. Despite being mainly a respiratory virus, cardiac complications have been described. These range from sudden cardiac death to subtle diastolic dysfunction after recovery from COVID-19. The commonest cardiac presentation to date is acute heart failure resulting from biventricular or left ventricular hypokinesis and elevation of cardiac troponins. It has been shown that COVID-19 downregulates angiotensin-converting enzyme-2, which has protective effects on the endothelium and cardiomyocytes. It has also been proven that COVID-19 induces a state of hypercytokinaemia, some cytokines such as interleukin-1 and interleukin-6 have an injurious effect on the myocardium and endothelium, respectively. Such pathogenic mechanisms might play a crucial role in induction of cardiomyocyte injury and impaired myocardial perfusion probably through coronary endothelial dysfunction. The understanding and linking of such mechanisms might help in tailoring drug repurposing for treatment or prophylaxis of COVID-19 cardiovascular complications.

Soluble Urokinase-Type Plasminogen Activator Receptor and High-Sensitivity Troponin Levels Predict Outcomes in Nonobstructive Coronary Artery Disease

Background Multiple biomarkers have been independently and additively associated with major adverse cardiovascular events in patients with coronary artery disease. We investigated the prognostic value of suPAR (soluble urokinase-type plasminogen activator receptor) and hsTnI (high-sensitivity troponin I) levels in symptomatic patients with no obstructive coronary artery disease. We hypothesized that high levels of these biomarkers will be associated with the risk of future adverse outcomes. Methods and Results Plasma levels of suPAR and hsTnI were measured in 556 symptomatic patients with no obstructive coronary artery disease. A biomarker risk score was calculated by counting the number of biomarkers above the median in this cohort (suPAR>2523 pg/mL and hsTnI>2.7 pg/mL). Survival analyses were performed with models adjusted for traditional risk factors. All-cause death and major adverse cardiovascular events (cardiovascular death, myocardial infarction, stroke, and heart failure) served as clinical outcomes over a median follow-up of 6.2 years. Mean age was 57±10 years, 49% of the cohort patients were female, and 68% had a positive stress test. High suPAR and hsTnI levels were independent predictors of all-cause death (hazard ratio=3.2 [95% CI, 1.8-5.7] and 1.3 [95% CI, 1.0-1.7], respectively; both P<0.04) and major adverse cardiovascular events (hazard ratio=2.7 [95% CI, 1.4-5.4] and 1.5 [95% CI, 1.2-2.0], respectively; both P<0.002). Compared with a biomarker risk score of 0, biomarker risk scores of 1 and 2 were associated with 19- and 14-fold increased risk of death and development of major adverse cardiovascular events, respectively. Conclusions Among symptomatic patients with no obstructive coronary artery disease, higher levels of suPAR and hsTnI were independently and additively associated with an increased risk of adverse events. Whether modification of these biomarkers will improve risk in these patients needs further investigation.

Using High-Sensitivity Cardiac Troponin for the Exclusion of Inducible Myocardial Ischemia in Symptomatic Patients: A Cohort Study

BACKGROUND

The optimal noninvasive method for surveillance in symptomatic patients with stable coronary artery disease (CAD) is unknown.

OBJECTIVE

To apply a novel approach using very low concentrations of high-sensitivity cardiac troponin I (hs-cTnI) for exclusion of inducible myocardial ischemia in symptomatic patients with CAD.

DESIGN

Prospective diagnostic cohort study. (ClinicalTrials.gov: NCT01838148).

SETTING

University hospital.

PATIENTS

1896 consecutive patients with CAD referred with symptoms possibly related to inducible myocardial ischemia.

MEASUREMENTS

Presence of inducible myocardial ischemia was adjudicated using myocardial perfusion imaging with single-photon emission computed tomography, as well as coronary angiography and fractional flow reserve measurements where available. Staff blinded to adjudication measured circulating hs-cTn concentrations. An hs-cTnI cutoff of 2.5 ng/L, derived previously in mostly asymptomatic patients with CAD, was assessed. Predefined target performance criteria were at least 90% negative predictive value (NPV) and at least 90% sensitivity for exclusion of inducible myocardial ischemia. Sensitivity analyses were based on measurements with an hs-cTnT assay and an alternative hs-cTnI assay with even higher analytic sensitivity (limit of detection, 0.1 ng/L).

RESULTS

Overall, 865 patients (46%) had inducible myocardial ischemia. The hs-cTnI cutoff of 2.5 ng/L provided an NPV of 70% (95% CI, 64% to 75%) and a sensitivity of 90% (CI, 88% to 92%) for exclusion of inducible myocardial ischemia. No hs-cTnI cutoff reached both performance characteristics predefined as targets. Similarly, using the alternative assays for hs-cTnI or hs-cTnT, no cutoff achieved the target performance: hs-cTnT concentrations less than 5 ng/L yielded an NPV of 66% (CI, 59% to 72%), and hs-cTnI concentrations less than 2 ng/L yielded an NPV of 68% (CI, 62% to 74%).

LIMITATION

Data were generated in a large single-center diagnostic study using central adjudication.

CONCLUSION

In symptomatic patients with CAD, very low hs-cTn concentrations, including hs-cTnI concentrations less than 2.5 ng/L, do not generally allow users to safely exclude inducible myocardial ischemia.

PRIMARY FUNDING SOURCE

European Union, Swiss National Science Foundation, Kommission für Technologie und Innovation (Innosuisse), Swiss Heart Foundation, Cardiovascular Research Foundation Basel, University of Basel, University Hospital Basel, Roche, Abbott, and Singulex.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation and discusses related analytic considerations, caveats, and reservations.