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Objectives: To evaluate the value of CT-based whole lung radiomics nomogram for identifying the risk of cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD). Materials and methods: A total of 974 patients with COPD were divided into a training cohort (n = 402), an internal validation cohort (n = 172), and an external validation cohort (n = 400) from three hospitals. Clinical data and CT findings were analyzed. Radiomics features of whole lung were extracted from the non-contrast chest CT images. A radiomics signature was constructed with algorithms. Combined with the radiomics score and independent clinical factors, multivariate logistic regression analysis was used to establish a radiomics nomogram. ROC curve was used to analyze the prediction performance of the model. Results: Age, weight, and GOLD were the independent clinical factors. A total of 1218 features were extracted and reduced to 15 features to build the radiomics signature. In the training cohort, the combined model (area under the curve [AUC], 0.731) showed better discrimination capability (p < 0.001) than the clinical factors model (AUC, 0.605). In the internal validation cohort, the combined model (AUC, 0.727) performed better (p = 0.032) than the clinical factors model (AUC, 0.629). In the external validation cohort, the combined model (AUC, 0.725) performed better (p < 0.001) than the clinical factors model (AUC, 0.690). Decision curve analysis demonstrated the radiomics nomogram outperformed the clinical factors model. Conclusion: The CT-based whole lung radiomics nomogram has the potential to identify the risk of CVD in patients with COPD. Clinical relevance statement: This study helps to identify cardiovascular disease risk in patients with chronic obstructive pulmonary disease on chest CT scans. Key Points: • To investigate the value of CT-based whole lung radiomics features in identifying the risk of cardiovascular disease in chronic obstructive pulmonary disease patients. • The radiomics nomogram showed better performance than the clinical factors model to identify the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. • The radiomics nomogram demonstrated excellent performance in the training, internal validation, and external validation cohort (AUC, 0.731; AUC, 0.727; AUC, 0.725). [ABSTRACT FROM AUTHOR]

Abstract The present study aims to assess arsenic accumulation and explore its association with renal function and biomarkers of CVD risk in chronic kidney disease patients receiving continuous ambulatory peritoneal dialysis (CAPD). The serum was collected from 87 CAPD patients and 26 healthy subjects between 2015 and 2016. The arsenic concentration was measured by inductively coupled plasma mass spectrometer. Clinical variables related to CVD risk were determined with automatic biochemical analyzer. Serum arsenic was higher in CAPD patients as compared to healthy volunteers. Moreover, significant differences of BMI, serum phosphorus, eGFR and Ccr were observed among groups. Positive correlation between serum arsenic and serum phosphorus was found (r = 0.453, p < 0.001). While serum arsenic was negatively associated with Ccr (r = −0.328, p = 0.002) and eGFR (r = −0.248, p = 0.020). The logistic regression models revealed that high serum arsenic was related to hyperphosphatemia (ORs, 1.827; 95%CI, 1.145–2.913) after adjusted for the potential confounding factors. Overall, our findings inferred the accumulation of arsenic in CAPD patients. In addition, high serum arsenic was independently associated with the occurrence of hyperphosphatemia, which was a special and ubiquitous CVD risk factor in CAPD patients. This study provided a clue for the association between arsenic and CVD burden in CKD patients. At the same time, it suggested that prevention of arsenic accumulation should be taken into consideration clinically. [ABSTRACT FROM AUTHOR]