Axial length, more than aging, decreases the thickness and superficial vessel density of retinal nerve fiber layer in non-glaucomatous eyes.

PURPOSE: This study seeks to build a normative database for the vessel density of the superficial retina (SVD) and evaluate how changes and trends in the retinal microvasculature may be influenced by age and axial length (AL) in non-glaucomatous eyes, as measured with optical coherence tomography angiography (OCTA). METHODS: We included 500 eyes of 290 healthy subjects visiting a county hospital. Each participant underwent comprehensive ophthalmological examinations and OCTA to measure the SVD and thickness of the macular and peripapillary areas. To analyze correlations between SVD and age or AL, multivariable linear regression models with generalized estimating equations were applied. RESULTS: Age was negatively correlated with the SVD of the superior, central, and inferior macular areas and the superior peripapillary area, with a decrease rate of 1.06%, 1.36%, 0.84%, and 0.66% per decade, respectively. However, inferior peripapillary SVD showed no significant correlation with age. AL was negatively correlated with the SVD of the inferior macular area and the superior and inferior peripapillary areas, with coefficients of -0.522%/mm, -0.733%/mm, and -0.664%/mm, respectively. AL was also negatively correlated with the thickness of the retinal nerve fiber layer and inferior ganglion cell complex (p = 0.004). CONCLUSION: Age and AL were the two main factors affecting changes in SVD. Furthermore, AL, a relative term to represent the degree of myopia, had a greater effect than age and showed a more significant effect on thickness than on SVD. This relationship has important implications because myopia is a significant issue in modern cities.

Incidence and Predictors of Acute Coronary Syndrome in Patients on Maintenance Hemodialysis: A Prospective Cohort Study.

Background: Cardiovascular diseases are the leading cause of death among patients on hemodialysis, with approximately 40% of the cardiovascular deaths linked to acute coronary syndrome. We aimed to investigate the incidence and risk factors of acute coronary syndrome in patients undergoing hemodialysis. Methods: Patients undergoing hemodialysis were prospectively enrolled from January 2018. Data regarding hospitalization due to acute coronary syndrome were collected at 3-month intervals through December 31, 2021. Cox regression model was used to estimate the association between baseline factors and incident acute coronary syndrome during follow-up. Results: Patients' mean age was 66 years, 48% were men, and 16% had a history of coronary artery disease at enrolment. Over a median follow-up of 1,187 days, 85 patients were hospitalized due to acute coronary syndrome. Left main or triple vessel disease was identified in 67 patients. Risk factors associated with incident acute coronary syndrome included aging, male sex, smoking, low diastolic blood pressure, and baseline comorbidities, in addition to dialysis factors including low urea clearance, central venous catheter use, and history of dialysis access dysfunction. After multivariate analysis, age, diabetes, hyperlipidemia, smoking, and frequent interventions for vascular access remained significant risk factors. Conclusions: A high acute coronary syndrome incidence was observed in our cohort, with traditional risk factors playing a consistent role with that in the general population. A history of frequent dialysis access dysfunction was also associated with incident acute coronary syndrome.

Left Ventricular Segmentation, Warping, and Myocardial Registration for Automated Strain Measurement.

The left ventricular global longitudinal strain (LVGLS) is a crucial prognostic indicator. However, inconsistencies in measurements due to the speckle tracking algorithm and manual adjustments have hindered its standardization and democratization. To solve this issue, we proposed a fully automated strain measurement by artificial intelligence-assisted LV segmentation contours. The LV segmentation model was trained from echocardiograms of 368 adults (11,125 frames). We compared the registration-like effects of dynamic time warping (DTW) with speckle tracking on a synthetic echocardiographic dataset in experiment-1. In experiment-2, we enrolled 80 patients to compare the DTW method with commercially available software. In experiment-3, we combined the segmentation model and DTW method to create the artificial intelligence (AI)-DTW method, which was then tested on 40 patients with general LV morphology, 20 with dilated cardiomyopathy (DCMP), and 20 with transthyretin-associated cardiac amyloidosis (ATTR-CA), 20 with severe aortic stenosis (AS), and 20 with severe mitral regurgitation (MR). Experiments-1 and -2 revealed that the DTW method is consistent with dedicated software. In experiment-3, the AI-DTW strain method showed comparable results for general LV morphology (bias - 0.137 ± 0.398%), DCMP (- 0.397 ± 0.607%), ATTR-CA (0.095 ± 0.581%), AS (0.334 ± 0.358%), and MR (0.237 ± 0.490%). Moreover, the strain curves showed a high correlation in their characteristics, with R-squared values of 0.8879-0.9452 for those LV morphology in experiment-3. Measuring LVGLS through dynamic warping of segmentation contour is a feasible method compared to traditional tracking techniques. This approach has the potential to decrease the need for manual demarcation and make LVGLS measurements more efficient and user-friendly for daily practice.

The role of wearable home blood pressure monitoring in detecting out-of-office control status.

Ambulatory blood pressure (ABP) and home blood pressure (HBP) monitoring is currently recommended for management of hypertension. Nonetheless, traditional HBP protocols could overlook diurnal fluctuations, which could also be linked with adverse cardiovascular outcomes. In this observational study, we studied among a group of treated hypertensive patients (N = 62, age: 52.4 ± 10.4 years) by using out-of-office ABP and wearable HBP. They received one session of 24-h ABP measurement with an oscillometric upper-arm monitor, and totally three sessions of 7-day/6-time-daily wearable HBP measurement separated in each month with HeartGuide. Controlled hypertension is defined as an average BP <130/80 mmHg for both daytime ABP and HBP. There was substantial reliability (intraclass correlation coefficient, ICC 0.883-0.911) and good reproducibility (Cohen's kappa = 0.600) for wearable HBP measurement, especially before breakfast and after dinner. Among all patients, 27.4% had both uncontrolled HBP and ABP, 30.6% had uncontrolled HBP only, while 6.5% had uncontrolled ABP only. Female gender and increased numbers of anti-hypertensive agents are correlated with controlled hypertension. Patients with uncontrolled hypertension had a significantly higher maximal daytime blood pressure, which was previously signified as an imperial marker for cardiovascular risk. In conclusion, wearable HBP monitoring in accordance with a dedicated daily-living schedule results in good reliability and reproducibility. Patients with an uncontrolled wearable HBP should benefit from repeated HBP or ABP measurement for risk stratification.

Real-time coronary artery segmentation in CAG images: A semi-supervised deep learning strategy.

BACKGROUND: When treating patients with coronary artery disease and concurrent renal concerns, we often encounter a conundrum: how to achieve a clearer view of vascular details while minimizing the contrast and radiation doses during percutaneous coronary intervention (PCI). Our goal is to use deep learning (DL) to create a real-time roadmap for guiding PCI. To this end, segmentation, a critical first step, paves the way for detailed vascular analysis. Unlike traditional supervised learning, which demands extensive labeling time and manpower, our strategy leans toward semi-supervised learning. This method not only economizes on labeling efforts but also aims at reducing contrast and radiation exposure. METHODS AND RESULTS: CAG data sourced from eight tertiary centers in Taiwan, comprising 500 labeled and 8952 unlabeled images. Employing 400 labels for training and reserving 100 for validation, we built a U-Net based network within a teacher-student architecture. The initial teacher model was updated with 8952 unlabeled images inputted, employing a quality control strategy involving consistency regularization and RandAugment. The optimized teacher model produced pseudo-labels for label expansion, which were then utilized to train the final student model. We attained an average dice similarity coefficient of 0.9003 for segmentation, outperforming supervised learning methods with the same label count. Even with only 5 % labels for semi-supervised training, the results surpassed a supervised method with 100 % labels inputted. This semi-supervised approach's advantage extends beyond single-frame prediction, yielding consistently superior results in continuous angiography films. CONCLUSIONS: High labeling cost hinders DL training. Semi-supervised learning, quality control, and pseudo-label expansion can overcome this. DL-assisted segmentation potentially provides a real-time PCI roadmap and further diminishes radiation and contrast doses.

Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department.

BACKGROUND AND OBJECTIVES: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. METHODS: Using the open-source dataset MIMIC-IV and MIMICCXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. RESULTS: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. CONCLUSION: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments.