Retinal Vascular Geometry and the Prevalence of Atrial Fibrillation and Heart Failure in a Clinic-Based Sample

Retinal Microvasculature: A Potential Window Into Heart Failure Prevention

Endothelial dysfunction and microvascular disease have been shown to play an important role in the development and progression of heart failure (HF). Retinal imaging provides a unique opportunity to noninvasively assess vascular structure and function, vessel features, and microcirculation within the retina. Accumulating evidence suggests that retinal vessel caliber, microvascular features, and vascular characteristics extracted from various imaging modalities are associated with alterations in left ventricular structure and function in stage B HF, as well as incident development of symptomatic HF in the general population. Moreover, dynamic retinal vessel analysis has been shown to differentiate HF patients based on their phenotypes. Given the increasing availability of rapid image acquisition devices (eg, nonmydriatic widefield systems and smartphone-based retinal cameras) and the integration of artificial intelligence-based interrogation/assessment techniques, retinal imaging is a promising noninvasive tool, in conjunction with cardiac imaging and biomarkers, to prevent HF and risk stratify those at risk of developing HF. This review focuses on the current evidence on retinal microvasculature changes, and potential clinical relevance and promising utility of retinal imaging in HF.

Retinal Microvascular Abnormalities Predict Clinical Outcomes in Patients with Heart Failure

Background: Narrower retinal arterioles and wider retinal venules have been associated with the incidence of heart failure (HF). However, whether they are predictive of the prognosis of heart failure (HF) is unclear. We aimed to explore the role of retinal vessel calibers in predicting long-term clinical outcomes of HF. Methods: This is a prospective, single-center, observational study that surveyed patients in a tertiary referral hospital for the treatment of HF. Retinal vessel caliber was graded using retinal photography. The primary endpoint was the composite endpoint of HF rehospitalization and mortality at 12 months. Results: There were 55 patients with chronic HF included in the final analysis. At 12 months, the cumulative incidence of the primary endpoint, HF rehospitalization, and mortality tended to be higher with the widening of the central retinal venular equivalent (CRVE) (p for non-linearity = 0.059) and was significantly increased when CRVE reached a cut-off value (283 μm) (p = 0.011) following adjustment for age, sex, etiology of HF, and diabetes. No association between the central retinal arteriolar equivalent (CRAE) and arteriolar-to-venular caliber ratio (AVR) was found with the clinical outcome in both univariable and multivariable Cox regression. CRAE, CRVE, and AVR had no relationship with the concentration of the N-terminal pro-B-type natriuretic peptide. In addition, CRVE was not associated with cardiac diastolic and systolic function. Conclusions: When the retinal venular caliber widens to a certain point, the composite incidence of HF rehospitalization and mortality significantly increase, suggesting retinal vessel caliber imaging may provide insight into the development of HF.

Application of multi-label classification models for the diagnosis of diabetic complications

Background

Early diagnosis for the diabetes complications is clinically demanding with great significancy. Regarding the complexity of diabetes complications, we applied a multi-label classification (MLC) model to predict four diabetic complications simultaneously using data in the modern electronic health records (EHRs), and leveraged the correlations between the complications to further improve the prediction accuracy.

Methods

We obtained the demographic characteristics and laboratory data from the EHRs for patients admitted to Changzhou No. 2 People’s Hospital, the affiliated hospital of Nanjing Medical University in China from May 2013 to June 2020. The data included 93 biochemical indicators and 9,765 patients. We used the Pearson correlation coefficient (PCC) to analyze the correlations between different diabetic complications from a statistical perspective. We used an MLC model, based on the Random Forest (RF) technique, to leverage these correlations and predict four complications simultaneously. We explored four different MLC models; a Label Power Set (LP), Classifier Chains (CC), Ensemble Classifier Chains (ECC), and Calibrated Label Ranking (CLR). We used traditional Binary Relevance (BR) as a comparison. We used 11 different performance metrics and the area under the receiver operating characteristic curve (AUROC) to evaluate these models. We analyzed the weights of the learned model and illustrated (1) the top 10 key indicators of different complications and (2) the correlations between different diabetic complications.

Results

The MLC models including CC, ECC and CLR outperformed the traditional BR method in most performance metrics; the ECC models performed the best in Hamming loss (0.1760), Accuracy (0.7020), F1_Score (0.7855), Precision (0.8649), F1_micro (0.8078), F1_macro (0.7773), Recall_micro (0.8631), Recall_macro (0.8009), and AUROC (0.8231). The two diabetic complication correlation matrices drawn from the PCC analysis and the MLC models were consistent with each other and indicated that the complications correlated to different extents. The top 10 key indicators given by the model are valuable in medical application.

Conclusions

Our MLC model can effectively utilize the potential correlation between different diabetic complications to further improve the prediction accuracy. This model should be explored further in other complex diseases with multiple complications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01525-7.

A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.