Associations between left ventricular function, vascular function and measures of cerebral small vessel disease: a cross-sectional magnetic resonance imaging study of the UK Biobank

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study

Objective

To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods

A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set (n = 116), an internal validation set (n = 30), and an external validation set (n = 80). Patients who experienced progression of WMH were identified from subsequent MRI results. We calculated CT-FFR and pFAI from CCTA images using semi-automated software, and segmented the pericoronary adipose tissue (PCAT) and myocardial ROI. A total of 1,073 features were extracted from each ROI, and were then refined by Elastic Net Regression. Firstly, different machine learning algorithms (Logistic Regression [LR], Support Vector Machine [SVM], Random Forest [RF], k-nearest neighbor [KNN] and eXtreme Gradient Gradient Boosting Machine [XGBoost]) were used to evaluate the effectiveness of radiomics signatures for predicting WMH progression. Then, the optimal machine learning algorithm was used to compare the predictive performance of individual and hybrid models based on independent risk factors of WMH progression. Receiver operating characteristic (ROC) curve analysis, calibration and decision curve analysis were used to evaluate predictive performance and clinical value of the different models.

Results

CT-FFR, pFAI, and radiomics signatures were independent predictors of WMH progression. Based on the machine learning algorithms, the PCAT signatures led to slightly better predictions than the myocardial signatures and showed the highest AUC value in the XGBoost algorithm for predicting WMH progression (AUC: 0.731 [95% CI: 0.603-0.838] vs.0.711 [95% CI: 0.584-0.822]). In addition, pFAI provided better predictions than CT-FFR (AUC: 0.762 [95% CI: 0.651-0.863] vs. 0.682 [95% CI: 0.547-0.799]). A hybrid model that combined CT-FFR, pFAI, and two radiomics signatures provided the best predictions of WMH progression [AUC: 0.893 (95%CI: 0.815-0.956)].

Conclusion

pFAI was more effective than CT-FFR, and PCAT signatures were more effective than myocardial signatures in predicting WMH progression. A hybrid model that combines pFAI, CT-FFR, and two radiomics signatures has potential use for identifying WMH progression.

Multi-organ imaging demonstrates the heart-brain-liver axis in UK Biobank participants

Medical imaging provides numerous insights into the subclinical changes that precede serious diseases such as heart disease and dementia. However, most imaging research either describes a single organ system or draws on clinical cohorts with small sample sizes. In this study, we use state-of-the-art multi-organ magnetic resonance imaging phenotypes to investigate cross-sectional relationships across the heart-brain-liver axis in 30,444 UK Biobank participants. Despite controlling for an extensive range of demographic and clinical covariates, we find significant associations between imaging-derived phenotypes of the heart (left ventricular structure, function and aortic distensibility), brain (brain volumes, white matter hyperintensities and white matter microstructure), and liver (liver fat, liver iron and fibroinflammation). Simultaneous three-organ modelling identifies differentially important pathways across the heart-brain-liver axis with evidence of both direct and indirect associations. This study describes a potentially cumulative burden of multiple-organ dysfunction and provides essential insight into multi-organ disease prevention.

Cognitive Impairment in Heart Failure—A Review

Simple Summary

Compared to the general population, patients with heart failure have reduced cognition and increased dementia risk. Brain changes have been observed in these individuals, including reduced brain volumes and abnormal areas suggestive of ischaemia (lack of blood and hence oxygen supply to tissues). Patients with heart failure who have cognitive impairment have poorer self-care and are at increased risk of rehospitalisation and death. Causes of cognitive impairment in heart failure have been suggested, including reduced blood supply to the brain, inflammatory processes, protein abnormalities and thromboembolic disease (formation of blood clots which may travel to the brain and impede blood flow). In this article, we discuss these potential causes linking heart failure and cognitive impairment, and discuss the recognition and management of cognitive impairment in patients with heart failure.

Abstract

Cognitive impairment (CI) is common in heart failure (HF). Patients with HF demonstrate reduced global cognition as well as deficits in multiple cognitive domains compared to controls. Degree of CI may be related to HF severity. HF has also been associated with an increased risk of dementia. Anatomical brain changes have been observed in patients with HF, including grey matter atrophy and increased white matter lesions. Patients with HF and CI have poorer functional independence and self-care, more frequent rehospitalisations as well as increased mortality. Pathophysiological pathways linking HF and CI have been proposed, including cerebral hypoperfusion and impaired cerebrovascular autoregulation, systemic inflammation, proteotoxicity and thromboembolic disease. However, these mechanisms are poorly understood. We conducted a search on MEDLINE, Embase and Scopus for original research exploring the connection between HF and CI. We then reviewed the relevant literature and discuss the associations between HF and CI, the patterns of brain injury in HF and their potential mechanisms, as well as the recognition and management of CI in patients with HF.