The association between coronary artery calcification progression and loss of bone density in non-dialyzed CKD patients

Background: Coronary artery calcification (CAC) and low bone density are coexisting deleterious conditions commonly shared by chronic kidney disease (CKD) patients. In the present study, we aimed to investigate whether the progression of CAC was associated with overtime reduction in bone density in non-dialyzed CKD patients. Methods: This is a prospective study of 24 months including 72 non-dialyzed CKD patients Stages 2 - 4 (age 57.6 +/- 10.3 years, 62% male, 22% diabetics). CAC and vertebral bone density (VBD) were measured by computed tomography. Results: At baseline, 46% of the patients had CAC (calcified group) and calcification was not identified in 54% of the patients (non-calcified group). The calcified group was older, predominantly male, and had lower VBD in comparison to non-calcified group. CAC progression was observed only in the calcified group (91% of the patients increased calcium score). The multiple regression analysis revealed loss of VBD as the independent determinant of CAC progression in these patients. Conclusion: CAC progression was associated with loss of VBD in non-dialyzed CKD patients.

Phosphorus Is Associated with Coronary Artery Disease in Patients with Preserved Renal Function

High serum phosphorus levels have been associated with mortality and cardiovascular events in patients with chronic kidney disease and in the general population. In addition, high phosphorus levels have been shown to induce vascular calcification and endothelial dysfunction in vitro. The aim of this study was to evaluate the relation of phosphorus and coronary calcification and atherosclerosis in the setting of normal renal function. This was a cross-sectional study involving 290 patients with suspected coronary artery disease and undergoing elective coronary angiography, with a creatinine clearance >60 ml/min/1.73 m(2). Coronary artery obstruction was assessed by the Friesinger score and coronary artery calcification by multislice computed tomography. Serum phosphorus was higher in patients with an Agatston score >10 than in those with an Agatston score <= 10 (3.63 +/- 0.55 versus 3.49 +/- 0.52 mg/dl; p = 0.02). In the patients with Friesinger scores >4, serum phosphorus was higher (3.6 +/- 0.5 versus 3.5 +/- 0.6 mg/dl, p = 0.04) and median intact fibroblast growth factor 23 was lower (40.3 pg/ml versus 45.7 pg/ml, p = 0.01). Each 0.1-mg/dl higher serum phosphate was associated with a 7.4% higher odds of having a Friesinger score >4 (p = 0.03) and a 6.1% greater risk of having an Agatston score >10 (p = 0.01). Fibroblast growth factor 23 was a negative predictor of Friesinger score ( p = 0.002). In conclusion, phosphorus is positively associated with coronary artery calcification and obstruction in patients with suspected coronary artery disease and preserved renal function.

Prognostic implications of coronary calcification in patients with obstructive coronary artery disease treated by percutaneous coronary intervention: a patient-level pooled analysis of 7 contemporary stent trials

OBJECTIVE To investigate the long-term prognostic implications of coronary calcification in patients undergoing percutaneous coronary intervention for obstructive coronary artery disease. METHODS Patient-level data from 6296 patients enrolled in seven clinical drug-eluting stents trials were analysed to identify in angiographic images the presence of severe coronary calcification by an independent academic research organisation (Cardialysis, Rotterdam, The Netherlands). Clinical outcomes at 3-years follow-up including all-cause mortality, death-myocardial infarction (MI), and the composite end-point of all-cause death-MI-any revascularisation were compared between patients with and without severe calcification. RESULTS Severe calcification was detected in 20% of the studied population. Patients with severe lesion calcification were less likely to have undergone complete revascularisation (48% vs 55.6%, p<0.001) and had an increased mortality compared with those without severely calcified arteries (10.8% vs 4.4%, p<0.001). The event rate was also high in patients with severely calcified lesions for the combined end-point death-MI (22.9% vs 10.9%; p<0.001) and death-MI- any revascularisation (31.8% vs 22.4%; p<0.001). On multivariate Cox regression analysis, including the Syntax score, the presence of severe coronary calcification was an independent predictor of poor prognosis (HR: 1.33 95% CI 1.00 to 1.77, p=0.047 for death; 1.23, 95% CI 1.02 to 1.49, p=0.031 for death-MI, and 1.18, 95% CI 1.01 to 1.39, p=0.042 for death-MI- any revascularisation), but it was not associated with an increased risk of stent thrombosis. CONCLUSIONS Patients with severely calcified lesions have worse clinical outcomes compared to those without severe coronary calcification. Severe coronary calcification appears as an independent predictor of worse prognosis, and should be considered as a marker of advanced atherosclerosis.

Registry for the Evaluation of the Prognostik value of a novel integrated imaging approach combining Single Photon Emission Computed Tomography with coronary calcification imaging (REPROSPECT)

AIMS: Although an added diagnostic and prognostic value of the global coronary artery calcification (CAC) score as an adjunct to single-photon emission computed tomography (SPECT)-myocardial perfusion image (MPI) has been repeatedly documented, none of the previous studies took advantage of the anatomic information provided by the unenhanced cardiac CT. Therefore, no co-registration has so far been used to match a myocardial perfusion defect with calcifications in the subtending coronary artery. To evaluate the prognostic value of integrating SPECT-MPI with CAC images were obtained from non-enhanced cardiac computed tomography (CT) for attenuation correction to predict major adverse cardiac events (MACE). METHODS AND RESULTS: Follow-up was obtained in 462 patients undergoing a 1-day stress/rest (99m)Tc-teterofosmin SPECT and non-enhanced cardiac CT for attenuation correction. Survival free of MACE was determined using the Kaplan-Meier method. After integrating MPI and CT findings, patients were divided into three groups (i) MPI defect matched by calcification (CAC ≥ 1) in the subtending coronary artery (ii) unmatched MPI and CT finding (iii) normal finding by MPI and CT. At a mean follow-up of 34.5 ± 13 months, a MACE was observed in 80 patients (33 death, 6 non-fatal myocardial infarction, 9 hospitalizations due to unstable angina, and 32 revascularizations). Survival analysis revealed the most unfavourable outcome (P < 0.001 log-rank test) for patients with a matched finding. CONCLUSION: In the present study, a novel approach using a combined integration of cardiac SPECT-CAC imaging allows for refined risk stratification, as a matched defect emerged as an independent predictor of MACE.

Diagnostic accuracy of 64-slice computed tomography for detecting angiographically significant coronary artery stenosis in an unselected consecutive patient population: comparison with conventional invasive angiography

BACKGROUND: Multislice computed tomography (MSCT) is a promising noninvasive method of detecting coronary artery disease (CAD). However, most data have been obtained in selected series of patients. The purpose of the present study was to investigate the accuracy of 64-slice MSCT (64 MSCT) in daily practice, without any patient selection. METHODS AND RESULTS: Using 64-slice MSCT coronary angiography (CTA), 69 consecutive patients, 39 (57%) of whom had previously undergone stent implantation, were evaluated. The mean heart rate during scan was 72 beats/min, scan time 13.6 s and the amount of contrast media 72 mL. The mean time span between invasive coronary angiography (ICAG) and CTA was 6 days. Significant stenosis was defined as a diameter reduction of > 50%. Of 966 segments, 884 (92%) were assessable. Compared with ICAG, the sensitivity of CTA to diagnose significant stenosis was 90%, specificity 94%, positive predictive value (PPV) 89% and negative predictive value (NPV) 95%. With regard to 58 stented lesions, the sensitivity, specificity, PPV and NPV were 93%, 96%, 87% and 98%, respectively. On the patient-based analysis, the sensitivity, specificity, PPV and NPV of CTA to detect CAD were 98%, 86%, 98% and 86%, respectively. Eighty-two (8%) segments were not assessable because of irregular rhythm, calcification or tachycardia. CONCLUSION: Sixty-four-MSCT has a high accuracy for the detection of significant CAD in an unselected patient population and therefore can be considered as a valuable noninvasive technique.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

New drugs for the treatment of coronary artery syndromes

Background: Acute coronary syndromes are a major cause of mortality and morbidity. Objectives/Methods: The objective of this evaluation is to review the clinical trials of two new drugs being developed for the treatment of acute coronary syndromes. The first drug is the anti-coagulant otamixaban, and the trial compared otamixaban with unfractionated heparin and eptifibatide in acute coronary syndromes. The second drug is the anti-platelet ticagrelor, and the trial compared ticagrelor with clopidogrel in acute coronary syndromes. Results: In the SEPIA-ACS1 TIMI 42 trial, the primary efficacy endpoint occurred in 6.2% of subjects treated with unfractionated heparin and eptifibatide, and to a significantly lesser extent with otamixaban. In the PLATO trial, the primary efficacy endpoint had occurred less in the ticagrelor group (9.8%) than in the clopidogrel group (11.7%) at 12 months. Conclusions: Two new drugs for acute coronary syndromes, otamixaban and ticagrelor, have recently been shown to have benefits in subjects undergoing percutaneous interventions compared to the present standard regimens for this condition.