Aortic arch calcification with pericardial fat mass detected on a single chest X-ray image is closely associated with the predictive variables of future cardiovascular disease

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Aortic arch calcification increases major adverse cardiac event risk, modifiable by echocardiographic left ventricular hypertrophy, in end-stage kidney disease patients

Background

The factors affecting cardiovascular risk associated with vascular calcification in patients with chronic kidney disease are less well addressed. Distinct risk factors may contribute synergistically to this elevated cardiovascular risk in this population.

Objectives

We aimed to determine whether echocardiographic left ventricular hypertrophy (LVH) affects the risk of major adverse cardiac events (MACE) associated with vascular calcification in end-stage kidney disease (ESKD) patients.

Methods

In this retrospective cohort study, ESKD patients underwent chest radiography and echocardiography to assess aortic arch calcification (AoAC) and LVH, respectively, and were classified into three groups accordingly: non-to-mild AoAC without LVH, non-to-mild AoAC with LVH, and moderate-to-severe AoAC. The risks of MACE, cardiovascular mortality, and overall mortality were assessed using Cox proportional hazard analysis.

Results

Of the 283 enrolled ESKD patients, 44 (15.5%) had non-to-mild AoAC without LVH, 117 (41.3%) had non-to-mild AoAC with LVH, and 122 (43.1%) had moderate-to-severe AoAC. After 34.1 months, 107 (37.8%) participants developed MACE, including 6 (13.6%), 40 (34.2%), and 61 (50%) from each respective group. Those with moderate-to-severe AoAC (Hazard ratio, 3.72; 95% confidence interval, 1.58-8.73) had a significantly higher risk of MACE than did those with non-to-mild AoAC without LVH or with non-to-mild AoAC and LVH (Hazard ratio, 2.73; 95% confidence interval, 1.16-6.46). A similar trend was observed for cardiovascular and overall mortality.

Conclusion

Echocardiographic LVH could modify the risk of adverse cardiovascular events associated with vascular calcification in ESKD patients. Interventions aiming to ameliorate both morbidities might be translated into a lower MACE risk in this population.

Deep learning to detect significant coronary artery disease from plain chest radiographs AI4CAD

BACKGROUND

The predictive role of chest radiographs in patients with suspected coronary artery disease (CAD) is underestimated and may benefit from artificial intelligence (AI) applications.

OBJECTIVES

To train, test, and validate a deep learning (DL) solution for detecting significant CAD based on chest radiographs.

METHODS

Data of patients referred for angina and undergoing chest radiography and coronary angiography were analysed retrospectively. A deep convolutional neural network (DCNN) was designed to detect significant CAD from posteroanterior/anteroposterior chest radiographs. The DCNN was trained for severe CAD binary classification (absence/presence). Coronary angiography reports were the ground truth. Stenosis severity of ≥70% for non-left main vessels and ≥ 50% for left main defined severe CAD.

RESULTS

Information of 7728 patients was reviewed. Severe CAD was present in 4091 (53%). Patients were randomly divided for algorithm training (70%; n = 5454) and fine-tuning/model validation (10%; n = 773). Internal clinical validation (model testing) was performed with the remaining patients (20%; n = 1501). At binary logistic regression, DCNN prediction was the strongest severe CAD predictor (p < 0.0001; OR: 1.040; CI: 1.032-1.048). Using a high sensitivity operating cut-point, the DCNN had a sensitivity of 0.90 to detect significant CAD (specificity 0.31; AUC 0.73; 95% CI DeLong, 0.69-0.76). Adding to the AI chest radiograph interpretation angina status improved the prediction (AUC 0.77; 95% CI DeLong, 0.74-0.80).

CONCLUSION

AI-read chest radiographs could be used to pre-test significant CAD probability in patients referred for suspected angina. Further studies are required to externally validate our algorithm, develop a clinically applicable tool, and support CAD screening in broader settings.

Fibrosis-4 Index Is Closely Associated with Arterial Damage and Future Risk of Coronary Heart Disease in Type 2 Diabetes

This study evaluated the association between fibrosis-4 (FIB 4) index and arterial damage or future risk of coronary heart disease (CHD) in type 2 diabetes. The study subjects were 253 patients with type 2 diabetes. The FIB4 index, as a marker of hepatic fibrosis based on age, aspartate aminotransferase and alanine aminotransferase levels, and platelet count, was calculated for all subjects. Carotid intima-media thickness (IMT), carotid artery calcification (CAC), and aortic arch calcification (AAC) grade (0–2) were assessed as atherosclerotic variables. The Suita score was calculated as the future risk of coronary heart disease (CHD). We assessed whether the FIB4 index was associated with both atherosclerotic variables and the Suita score. FIB4 index was significantly associated with IMT (r = 0.241, $P<0.001$ ) and Suita score (r = 0.291, $P<0.001$ ). Subjects with CAC showed a significantly higher FIB4 index score compared to subjects without (1.70 ± 0.74 and 1.24 ± 0.69, respectively, $P<0.001$ ), whereas the FIB4 index was significantly elevated with a higher grade of AAC (1.24 ± 0.74, 1.56 ± 0.66, and 1.79 ± 0.71, respectively, $P<0.001$ ). Linear regression analysis adjusted for clinical characteristics indicated that the FIB4 index was positively associated with IMT, Suita score, CAC, and AAC grade (β = 0.241, $P=0.004$ ; β = 2.994, $P<0.001$ ; β = 0.139, $P=0.001$ ; and β = 0.265, $P<0.001$ , respectively). FIB4 index is closely associated with arterial damage and future risk of CHD in type 2 diabetes.