Abdominal aortic calcification on a plain X-ray and the relation with significant coronary artery disease in asymptomatic chronic dialysis patients

Association between Abdominal Aortic Calcification and Coronary Heart Disease in Essential Hypertension: A Cross-Sectional Study from the 2013-2014 National Health and Nutrition Examination Survey

BACKGROUND

This study aimed to investigate the association between abdominal aortic calcification (AAC) and coronary heart disease (CHD) in essential hypertension (EH).

METHODS

This study included patients diagnosed with EH during the 2013-2014 NHANES survey cycle. The study cohort was categorized into the following four groups based on their AAC-24 score: no AAC (0); mild AAC (1-4); moderate AAC (5-15); and severe AAC (16-24). Logistic regression models were used to assess the association between AAC and CHD. Restricted cubic spline curves (RCS) were used to explore possible nonlinear relationships between AAC and CHD.

RESULTS

The prevalence of CHD was found to be higher in the moderate AAC and severe AAC groups than in the group without AAC (40.1% versus 30.9%, 47.7% versus 30.9%). On a continuous scale, the fully adjusted model showed a 7% increase in the risk of CHD prevalence per score increase in AAC [OR (95% CI) = 1.07 (1.03-1.11)]. On a categorical scale, the fully adjusted model showed the risk of CHD prevalence in EH patients with moderate AAC and severe AAC was 2.06 (95%CI, 1.23-3.45) and 2.18 (1.09-5.25) times higher than that in patients without AAC, respectively. The RCS curve suggested a dose-response linear relationship between AAC and CHD.

CONCLUSION

These findings highlight that in patients with EH, a higher severity of AAC is associated with a higher risk of CHD prevalence.

Abdominal aortic calcification volume (AACV) is a predictive factor for postoperative complications associated with biliary tract cancer

PURPOSE

Curative surgical treatment of biliary tract cancer is highly invasive and involves postoperative complications. Abdominal aortic calcification is a parameter that is reportedly linked to systemic arteriosclerosis. We measured the abdominal aortic calcification volume (AACV), assessed the correlation between AACV and postoperative complications, and evaluated the clinical utility of AACV.

METHODS

We retrospectively evaluated 97 patients (ampullary carcinoma, n = 21; distal bile duct cancer, n = 43; hilar bile duct cancer, n = 33). We assessed the calcification volume of the abdominal aorta from the renal artery ramification to the common iliac artery bifurcation. The correlation between AACV, clinical factors, and postoperative complications was evaluated.

RESULTS

The average AACV was 5.02 cm³, and the median AACV was 3.74 (range 0-27.4) cm³. The AACV was significantly related to age (P = 0.009), Brinkman index (P = 0.007), and history of cardiovascular disease (P = 0.015). The AACV was strongly correlated with postoperative complications (P < 0.001) and Clavien-Dindo grade > III postoperative complications (P < 0.001). The AACV was also correlated with pancreatic fistula in pancreatectomy cases (P < 0.001). A multivariate analysis revealed that the AACV was an independent predictor of postoperative complications.

CONCLUSION

The AACV was significantly associated with postoperative complications. The AACV could be used for the preoperative assessment of surgical risk.

Abdominal Aortic Calcification and Cardiovascular Outcomes in Chronic Kidney Disease: Findings from KNOW-CKD Study

To investigate the association between abdominal aortic calcification score (AACS) assessed by plain radiograph of the lateral abdomen and the risk of cardiovascular (CV) events in patients with pre-dialysis chronic kidney disease (CKD), a total of 2090 pre-dialysis CKD patients from the Korean Cohort Study for Outcome in Patients with Chronic Kidney Disease (KNOW-CKD) were categorized by AACS into 0, 1–2, 3–4, 5–6, and ≥7. The primary outcome of the study was the composite CV events, defined as a composite of non-fatal CV events and all-cause death. The risk of composite CV events was significantly higher in the subjects with AACS ≥ 7 (adjusted hazard ratio (HR) 1.888, 95% confidence interval (CI) 1.219 to 2.923), compared to that of the subjects with AACS 0. The risks of fatal and non-fatal CV events (adjusted HR 1.052, 95% CI 1.030 to 1.073) and all-cause death (adjusted HR 1.949, 95% CI 1.073 to 3.539) were also significantly higher in the subjects with AACS ≥ 7. In conclusion, AACS assessed by plain radiograph is independently associated with adverse CV outcomes in patients with pre-dialysis CKD. A simple radiographic examination of the lateral abdomen may help CV risk stratification in this population.

Kernelized k-Local Hyperplane Distance Nearest-Neighbor Model for Predicting Cerebrovascular Disease in Patients With End-Stage Renal Disease

Detecting and treating cerebrovascular diseases are essential for the survival of patients with chronic kidney disease (CKD). Machine learning algorithms can be used to effectively predict stroke risk in patients with end-stage renal disease (ESRD). An imbalance in the amount of collected data associated with different risk levels can influence the classification task. Therefore, we propose the use of a kernelized k-local hyperplane nearest-neighbor model (KHKNN) for the effective prediction of stroke risk in patients with ESRD. We compared our proposed method with other conventional machine learning methods, which revealed that our method could effectively perform the task of classifying stroke risk.

A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients

In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascular calcification risk in ESRD patients. As the amount of collected data is unbalanced under different risk levels, it has an influence on the classification task. So, an effective fuzzy support vector machine based on self-representation (FSVM-SR) is proposed to predict vascular calcification risk in this work. In addition, our method is also compared with other conventional machine learning methods, and the results show that our method can better complete the classification task of the vascular calcification risk.

Coronary Artery Disease Association With Arterial Calcifications on Routine Hand Radiographs

PURPOSE

Arterial calcifications in the lower extremity, chest, and cardiac vessels have been linked to coronary artery disease (CAD). However, the relation between arterial calcifications observed on routine hand and upper-extremity radiographs and atherosclerosis has not been determined. This study examined whether arterial calcifications found on hand radiographs are associated with CAD.

METHODS

A record review from a single institution identified 345 patients with both hand radiographs and CAD screening with cardiac stress testing or coronary angiography. Patients with chronic kidney disease, end-stage renal disease, or incomplete hand films were excluded. We reviewed x-rays for findings of arterial calcifications. Cardiac testing results were used to establish a baseline diagnosis of CAD. We made group comparisons and employed multivariable logistic regression to evaluate the association between upper-extremity calcification and CAD.

RESULTS

A total of 210 patients met inclusion criteria: 155 with CAD and 55 without it. Mean age was 72 years, body mass index was 28.8, and 54% were male. Patients had comorbidities of hypertension (91%), hyperlipidemia (87%), diabetes (39%), cerebrovascular accident (9%), and a history of tobacco use (53%). Of 155 CAD patients, 67 had arterial calcifications on hand radiographs (43%), compared with 6 of 55 without it (11%). In a multivariable model controlling for sex, hyperlipidemia, and diabetes, the presence of arterial calcifications on hand plain films indicated a 6.2-fold increased odds of CAD.

CONCLUSIONS

The current data demonstrate that arterial calcifications on hand radiographs are independently associated with CAD. This may represent an opportunity to the treating physician as a point of referral or investigation for underlying or occult CAD.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prevalence III.

The impact of abdominal fat on abdominal aorta calcification measured on non-enhanced CT

Cardiovascular (CV) morbidity, atherosclerosis, and obesity are all targets of clinical concern and vast research, as is the association between them. Aim of this study is to assess the impact of adipose tissue (including visceral and subcutaneous fat) on abdominal aorta calcification measured on non-enhanced computed tomography (CT). We retrospectively included 492 patients who underwent non-enhanced CT scans during workup for clinically suspected renal colic. All scans were reviewed for abdominal aorta calcification, liver attenuation, and thickness of visceral and subcutaneous fat. Multivariate general linear regression models were used to assess the association between abdominal aorta calcium score and adiposity measures. In the model that included only adiposity measures; visceral fat thickness had statistically significant direct association with abdominal aorta calcium score (B = 67.1, P <.001), whereas subcutaneous pelvic fat thickness had a significant inverse association with abdominal aorta calcium score (B = -22.34, P <.001). Only the association of subcutaneous pelvic fat thickness with abdominal aorta calcium score remained statistically significant when controlling for age, sex, smoking, hypertension, diabetes mellitus, and hyperlipidemia (B = -21.23, P <.001). In this model, the association of visceral fat remained statistically significant in females (B = 84.28, P = .001) but not in males (B = 0.47, P = .973). Visceral fat thickness and subcutaneous pelvic fat thickness were found to have opposing associations with abdominal aorta calcium score. This suggests that while visceral fat may have a lipotoxic effect on aortic atherosclerotic processes, subcutaneous pelvic fat may have a protective role in these processes.