Coronary Calcium Scoring Improves Risk Prediction in Patients With Suspected Obstructive Coronary Artery Disease

Winther, Simon; Schmidt, Samuel; Foldyna, Borek; Mayrhofer, Thomas; Rasmussen, Laust Dupont; Dahl, Jonathan Nørtoft; Hoffmann, Udo; Douglas, Pamela S.; Knuuti, Juhani; Böttcher, Morten

doi:10.1016/j.jacc.2022.08.805

Cited by 23 publications

(19 citation statements)

References 18 publications

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“…Overall, our findings are consistent with previous studies, and the improved very-low CL categorisation by the RF-CL and CACS-CL models seem safe across various study cohorts. 6 …”

Section: Discussionmentioning

confidence: 99%

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

Rasmussen,

Williams,

Newby

et al. 2023

Open Heart

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ObjectivesThe 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.MethodsStable 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.ResultsObstructive 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.ConclusionIn 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.

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“…Overall, our findings are consistent with previous studies, and the improved very-low CL categorisation by the RF-CL and CACS-CL models seem safe across various study cohorts. 6 …”

Section: Discussionmentioning

confidence: 99%

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

Rasmussen,

Williams,

Newby

et al. 2023

Open Heart

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“…Alternative approaches developed are the generation of statistical scores that combine variables through linear dependencies and the extraction of single biomarkers with population-based prognostic value (such as coronary Calcium Score). 26 The effective operators of these premises are embodied in the present study, firstly in the resulting 58 variables from CCTA (based on dominance, segmental presence of plaque, degree of luminal narrowing and calcification) and the 17 variables from PET imaging (segmental absolute myocardial blood flow during stress), and secondly in the selected comparator predictors (the reference CCTA score promoted by De Graaf and colleagues and the global Calcium Score) analyzed.…”

Section: Discussionmentioning

confidence: 99%

Hybridizing machine learning in survival analysis of cardiac PET/CT imaging

Juarez-Orozco,

Niemi,

Yeung

et al. 2023

Journal of Nuclear Cardiology

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Background Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up. Methods Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model. Results Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score. Conclusion Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation.

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“…Multivariate analyses were adjusted for the components from a validated pre-test likelihood score of obstructive CAD and cardiac events including sex, age, symptom characteristics, smoking-status, dyslipidemia, diabetes mellitus, antihypertensive therapy, and family history. 2 , 22 Multicollinearity was checked, and missing data were excluded from the logistic regression analysis. Data on biomarkers was categorized according to pre-specified cut-offs defined above.…”

Section: Methodsmentioning

confidence: 99%

“…In patients with suspected CAD, coronary computed tomography angiography (CCTA) is a first-line non-invasive test with high diagnostic accuracy of obstructive disease. 1 In general, patients with de novo symptoms suggestive of obstructive CAD show favorable prognosis, 2 however CCTA is able to identify non-obstructive coronary plaques, plaque burden, and high-risk plaque (HRP) features which predict adverse cardiovascular events. 3 – 5 Positive remodeling, low attenuation plaques, napkin ring sign, and spotty calcifications are examples of HRP features which often serve as culprits in later coronary events.…”

Section: Introductionmentioning

confidence: 99%

Biomarkers for Identification of High-Risk Coronary Artery Plaques in Patients with Suspected Coronary Artery Disease

Pedersen,

Dahl,

Rasmussen

et al. 2023

Preprint

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Background: Patients with atherosclerotic plaques containing high-risk features have an increased likelihood of events and a worse prognosis. Whether increased levels of Troponin I (TnI) and C-reactive protein (CRP) are associated with the presence of high-risk coronary atherosclerotic plaques (HRP) is not well described. We assessed the association between 1) TnI and 2) CRP with quantified coronary plaque burden, luminal diameter stenosis, and HRP in patients with low/intermediate pre-test probability of obstructive coronary artery disease (CAD) referred for coronary computed tomography angiography (CCTA). Methods: The CCTA from 1,615 patients were analyzed using a semiautomatic software for coronary artery plaque characterization. Patients with high TnI (>6 ng/L) and high CRP (>2 mg/L) were identified. Associations of TnI and CRP with plaque burden, stenosis (?50% luminal diameter stenosis on CCTA), and HRP were investigated. Results: TnI and CRP were both positively correlated with total plaque burden (TnI rs=0.14, p<0.001; CRP rs=0.08, p<0.001). In multivariate logistic regression analyses, high TnI was associated with stenosis (OR 1.43, 95% confidence interval (CI) 1.03-1.99, p=0.034), the presence of HRP (OR 1.79, 95% CI: 1.17? 2.74, p=0.008), and the subtypes of HRP; low attenuation plaque (OR 1.93, 95% CI: 1.24?3.00, p=0.003), and positive remodeling (OR 1.51, 95% CI: 1.07?2.13, p=0.018). For CRP, only stenosis and napkin ring sign correlated significantly. Conclusion: In patients with suspected CAD, TnI and CRP are associated with HRP features. These findings may suggest that inflammatory and particularly ischemic biomarkers might improve early risk stratification and affect patient management. ClinicalTrials.gov identifier:NCT02264717

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Coronary Calcium Scoring Improves Risk Prediction in Patients With Suspected Obstructive Coronary Artery Disease

Cited by 23 publications

References 18 publications

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

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

Hybridizing machine learning in survival analysis of cardiac PET/CT imaging

Biomarkers for Identification of High-Risk Coronary Artery Plaques in Patients with Suspected Coronary Artery Disease

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