Search

Background Classical risk scoring systems underestimate the risk of cardiovascular disease in chronic kidney disease (CKD). Coronary artery calcium score (CACS) has improved prediction of cardiovascular events in patients with CKD. The maximal carotid plaque thickness (cPTmax) measured in ultrasound scans of the carotid arteries has demonstrated similar predictive value as CACS in the general population. This is the first study to investigate whether cPTmax can predict cardiovascular events in CKD and to compare the predictive value of cPTmax and CACS in CKD. Method Two hundred patients with CKD stage 3 from the Copenhagen CKD Cohort underwent ultrasound scanning of the carotid arteries. The assessment consisted of locating plaque and measuring the thickest part of the plaque, cPTmax. Based on the distribution of cPTmax, the participants were divided into 3 groups: No plaques, cPTmax 1.0–1.9 mm and cPTmax > 1.9 mm (median cPTmax = 1.9 mm among patients with plaques). To measure CACS, 175 of the patients underwent a non-contrast CT scan of the coronary arteries. The follow-up time spanned between the ultrasound scan and a predefined end-date or the time of first event, defined as a composite of major cardiovascular events or death of any cause (MACE). Results The median follow-up time was 5.4 years during which 45 patients (22.5%) developed MACE. In a Cox-regression adjusted for classical cardiovascular risk factors, patients with cPTmax > 1.9 mm had a significantly increased hazard ratio of MACE (HR 3.2, CI: 1.1–9.3), p = 0.031) compared to patients without plaques. C-statistics was used to evaluate models for predicting MACE. The improvement in C-statistics was similar for the two models including classical cardiovascular risk factors plus cPTmax (0.247, CI: 0.181–0.312) and CACS (0.243, CI: 0.172–0.315), respectively, when compared to a model only controlled for time since baseline (a Cox model with no covariates). Conc, BACKGROUND: Classical risk scoring systems underestimate the risk of cardiovascular disease in chronic kidney disease (CKD). Coronary artery calcium score (CACS) has improved prediction of cardiovascular events in patients with CKD. The maximal carotid plaque thickness (cPTmax) measured in ultrasound scans of the carotid arteries has demonstrated similar predictive value as CACS in the general population. This is the first study to investigate whether cPTmax can predict cardiovascular events in CKD and to compare the predictive value of cPTmax and CACS in CKD.METHOD: Two hundred patients with CKD stage 3 from the Copenhagen CKD Cohort underwent ultrasound scanning of the carotid arteries. The assessment consisted of locating plaque and measuring the thickest part of the plaque, cPTmax. Based on the distribution of cPTmax, the participants were divided into 3 groups: No plaques, cPTmax 1.0-1.9 mm and cPTmax > 1.9 mm (median cPTmax = 1.9 mm among patients with plaques). To measure CACS, 175 of the patients underwent a non-contrast CT scan of the coronary arteries. The follow-up time spanned between the ultrasound scan and a predefined end-date or the time of first event, defined as a composite of major cardiovascular events or death of any cause (MACE).RESULTS: The median follow-up time was 5.4 years during which 45 patients (22.5%) developed MACE. In a Cox-regression adjusted for classical cardiovascular risk factors, patients with cPTmax > 1.9 mm had a significantly increased hazard ratio of MACE (HR 3.2, CI: 1.1-9.3), p = 0.031) compared to patients without plaques. C-statistics was used to evaluate models for predicting MACE. The improvement in C-statistics was similar for the two models including classical cardiovascular risk factors plus cPTmax (0.247, CI: 0.181-0.312) and CACS (0.243, CI: 0.172-0.315), respectively, when compared to a model only controlled for time since baseline (a Cox model with no covariates).CONCLUSION: Our res

To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability.