Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease

BACKGROUND

The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence.

METHODS

Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features.

RESULTS

HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively.

CONCLUSIONS

In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence.

TRIAL REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

External Validation of Proposed American Heart Association Algorithm for Cardiovascular Screening Before Kidney Transplantation

BACKGROUND

Screening for cardiovascular disease is currently recommended before kidney transplantation. The present study aimed to validate the proposed algorithm by the American Heart Association (AHA-2022) considering cardiovascular findings and outcomes in kidney transplant candidates, and to compare AHA-2022 with the previous recommendation (AHA-2012).

METHODS AND RESULTS

We applied the 2 screening algorithms to an observational cohort of kidney transplant candidates (n=529) who were already extensively screened for coronary heart disease by referral to cardiac computed tomography between 2014 and 2019. The cohort was divided into 3 groups as per the AHA-2022 algorithm, or into 2 groups as per AHA-2012. Outcomes were degree of coronary heart disease, revascularization rate following screening, major adverse cardiovascular events, and all-cause death. Using the AHA-2022 algorithm, 69 (13%) patients were recommended for cardiology referral, 315 (60%) for cardiac screening, and 145 (27%) no further screening. More patients were recommended cardiology referral or screening compared with the AHA-2012 (73% versus 53%; P<0.0001). Patients recommended cardiology referral or cardiac screening had a higher risk of major adverse cardiovascular events (hazard ratio [HR], 5.5 [95% CI, 2.8-10.8]; and HR, 2.1 [95% CI, 1.2-3.9]) and all-cause death (HR, 12.0 [95% [CI, 4.6-31.4]; and HR, 5.3 [95% CI, 2.1-13.3]) compared with patients recommended no further screening, and were more often revascularized following initial screening (20% versus 7% versus 0.7%; P<0.001).

CONCLUSIONS

The AHA-2022 algorithm allocates more patients for cardiac referral and screening compared with AHA-2012. Furthermore, the AHA-2022 algorithm effectively discriminates between kidney transplant candidates at high, intermediate, and low risk with respect to major adverse cardiovascular events and all-cause death.

External validation of novel clinical likelihood models to predict obstructive coronary artery disease and prognosis

OBJECTIVES

The risk factor-weighted and coronary artery calcium score-weighted clinical likelihood (RF-CL and CACS-CL, respectively) models improve discrimination of patients with suspected obstructive coronary artery disease (CAD). However, external validation is warranted.Compared to the 2019 European Society of Cardiology pretest probability (ESC-PTP) model, the aims were (1) to validate the RF-CL and CACS-CL models for identification of obstructive CAD and revascularisation, and (2) to investigate prognosis by CL thresholds.

METHODS

Stable de novo chest pain patients (n=1585) undergoing coronary CT angiography (CTA) were investigated. Obstructive CAD was defined as >70% diameter stenosis in a major epicardial vessel on CTA. Decision of revascularisation within 120 days was based on onsite judgement. The endpoint was non-fatal myocardial infarction or cardiovascular death. The ESC-PTP was calculated based on age, sex and symptom typicality, the RF-CL additionally included number of risk factors, and the CACS-CL incorporated CACS to the RF-CL.

RESULTS

Obstructive CAD was present in 386/1585 (24.4%) patients, and 91/1585 (5.7%) patients underwent revascularisation. Both the RF-CL and CACS-CL classified more patients to very-low CL (<5%) of obstructive CAD compared with the ESC-PTP model (41.4% and 52.2% vs 19.2%, p<0.001). In very-low CL patients, obstructive CAD and revascularisation prevalences (≤6% and <1%) remained similar combined with low event risk during 5.0 years follow-up.

CONCLUSION

In an external validation cohort, the novel RF-CL and CACS-CL models improve categorisation to a very-low CL group with preserved prevalences of obstructive CAD, revascularisation and favourable prognosis.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain

BACKGROUND

Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).

METHODS

Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning.

RESULTS

Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.

CONCLUSIONS

For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT02264717.

In a real-life setting, risk factors, coronary artery calcium score, and coronary stenosis at computed tomography angiography are associated with major adverse cardiovascular events and all-cause mortality among kidney transplant candidates

Kidney failure is associated with an increased risk of cardiovascular disease and death. This single-center, a retrospective study evaluated the association between risk factors, coronary artery calcium score (CACS), coronary computed tomography angiography (CTA), major adverse cardiovascular events (MACEs), and all-cause mortality in kidney transplant candidates. Data on clinical risk factors, MACE, and all-cause mortality were collected from patient records. A total of 529 kidney transplant candidates were included (median follow-up of 4.7 years). CACS was evaluated in 437 patients and CTA in 411. Both the presence of ≥3 risk factors, CACS of ≥400, as well as multiple-vessel stenoses or left main artery disease predicted MACE (hazard ratio, 2.09; [95% confidence interval, 1.35-3.23]; 4.65 [2.20-9.82]; 3.70 [1.81-7.57]; 4.90 [2.40-10.01]) and all-cause mortality (harad ratio, 4.44; [95% confidence interval, 2.54-7.76]; 4.47 [2.22-9.02]; 2.82 [1.34-5.94]; 5.41 [2.81-10.41]) in univariate analyses. Among patients eligible for CACS and CTA (n = 376), only CACS and CTA were associated with both MACE and all-cause mortality. In conclusion, risk factors, CACS, and CTA provide information on the risk of MACE and mortality in kidney transplant candidates. An additional value of CACS and CTA compared with risk factors was observed for the prediction of MACE in a subpopulation undergoing both CACS and CTA.

Danish study of Non-Invasive Testing in Coronary Artery Disease 3 (Dan-NICAD 3): study design of a controlled study on optimal diagnostic strategy

INTRODUCTION

Current guideline recommend functional imaging for myocardial ischaemia if coronary CT angiography (CTA) has shown coronary artery disease (CAD) of uncertain functional significance. However, diagnostic accuracy of selective myocardial perfusion imaging after coronary CTA is currently unclear. The Danish study of Non-Invasive testing in Coronary Artery Disease 3 trial is designed to evaluate head to head the diagnostic accuracy of myocardial perfusion imaging with positron emission tomography (PET) using the tracers ⁸²Rubidium (⁸²Rb-PET) compared with oxygen-15 labelled water PET (¹⁵O-water-PET) in patients with symptoms of obstructive CAD and a coronary CT scan with suspected obstructive CAD.

METHODS AND ANALYSIS

This prospective, multicentre, cross-sectional study will include approximately 1000 symptomatic patients without previous CAD. Patients are included after referral to coronary CTA. All patients undergo a structured interview and blood is sampled for genetic and proteomic analysis and a coronary CTA. Patients with possible obstructive CAD at coronary CTA are examined with both ⁸²Rb-PET, ¹⁵O-water-PET and invasive coronary angiography with three-vessel fractional flow reserve and thermodilution measurements of coronary flow reserve. After enrolment, patients are followed with Seattle Angina Questionnaires and follow-up PET scans in patients with an initially abnormal PET scan and for cardiovascular events in 10 years.

ETHICS AND DISSEMINATION

Ethical approval was obtained from Danish regional committee on health research ethics. Written informed consent will be provided by all study participants. Results of this study will be disseminated via articles in international peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT04707859.

Case Report: Rare Case of Staphylococcus pasteuri Endocarditis

A 45-year-old woman was admitted with severe pain in the right leg and dyspnea. Her medical history included previous Staphylococcus aureus endocarditis, biological aortic valve replacement, and intravenous drug abuse. She was febrile but did not have any focal signs of infection. Blood tests showed raised infectious markers and troponin levels. Electrocardiogram showed sinus rhythm without signs of ischemia. Ultrasound revealed thrombosis of the right popliteal artery. The leg was not critically ischemic, and therefore, treatment with dalteparin was chosen. Transesophageal echocardiography showed an excrescence on the biological aortic valve. Empiric treatment for endocarditis was started with intravenous vancomycin, gentamicin, and oral rifampicin. Blood cultures subsequently grew Staphylococcus pasteuri. On day 2, treatment was changed to intravenous cloxacillin. Due to the comorbidity, the patient was not a candidate for the surgical treatment. On day 10, the patient developed moderate expressive aphasia and weakness in the right upper limb. Magnetic resonance imaging showed micro-embolic lesions scattered across both hemispheres of the brain. Treatment was changed from cloxacillin to cefuroxime. On day 42, infectious markers were normal, and echocardiography showed regression of the excrescence. Antibiotic treatment was stopped. Follow-up on day 52 did not show any signs of active infection. However, on day 143, the patient was readmitted with cardiogenic shock due to aortic root fistulation to the left atrium. She quickly deteriorated and died.