MERCI: a machine learning approach to identifying hydroxychloroquine retinopathy using mfERG.

PURPOSE: Hydroxychloroquine (HCQ) is an anti-inflammatory drug in widespread use for the treatment of systemic auto-immune diseases. Vision loss caused by retinal toxicity is a significant risk associated with long term HCQ therapy. Identifying patients at risk of developing retinal toxicity can help prevent vision loss and improve the quality of life for patients. This paper presents updated reference thresholds and examines the diagnostic accuracy of a machine learning approach for identifying retinal toxicity using the multifocal Electroretinogram (mfERG). METHODS: A retrospective study of patients referred for mfERG testing to detect HCQ retinopathy. A consecutive series of all patients referred to Kensington Vision and Research Centre between August 2017 and July 2020 were considered eligible. Eyes suspect for other ocular pathology including widespread retinal disease and advanced macular pathology unrelated to HCQ or with poor quality mfERG recordings were excluded. All patients received mfERG testing and Ocular Coherence Tomography (OCT) imaging. Presence of HCQ retinopathy was based on ring ratio analysis using clinical reference thresholds established at KVRC coupled with structural features observed on OCT, the clinical reference standard. A Support Vector Machine (SVM) using selected features of the mfERG was trained. Accuracy, sensitivity and specificity are reported. RESULTS: 1463 eyes of 748 patients were included in the study. SVM model performance was assessed on 293 eyes from 265 patients. 55 eyes from 54 patients were identified as demonstrating HCQ retinopathy based on the clinical reference standard, 50 eyes from 49 patients were identified by the SVM. Our SVM achieves an accuracy of 85.3% with a sensitivity of 90.9% and specificity of 84.0%. CONCLUSIONS: Machine learning approaches can be applied to mfERG analysis to identify patients at risk of retinopathy caused by HCQ therapy.

Revisiting percutaneous balloon mitral valvotomy technique and safety in various population: an evidence-based case report and literature review.

Percutaneous balloon mitral valvotomy (PBMV) is a good and preferred therapy choice over surgical commissurotomy for patients with rheumatic mitral stenosis (MS). However, interventional cardiologists must recognize that treating patients with rheumatic MS poses unique challenges for each patient, especially in special populations such as pregnant patients or patients with arrhythmias like atrial fibrillation (AF), which can complicate procedures. Based on information from observational studies, PBMV may be a safe and efficient treatment for improving outcomes in MS women who do not have substantial subvalve illness in a specific demographic. A successful PBMV helps to tolerate hemodynamic changes during pregnancy and dramatically reduces mortality. However, there is a paucity of studies on women with poor valve morphology who are not contraindicated, and it has to be seen if PBMV is used in these situations during pregnancy. Conversely, AF leads to a lower PBMV success rate as well as worse long-term and in-hospital outcomes.

Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images.

The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pretrained deep convolutional neural networks (CNNs). Our results demonstrated that the deep CNN models (e.g., ResNeXt) trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error (MAE) of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC (area under the receiver operating characteristic curve) of 88%. ResNeXt model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a MAE of 9.06 kg. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pretrained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for noninvasive, convenient, and inexpensive patient health monitoring and diagnosis.

Microalbuminuria and mortality in individuals with coronary heart disease: A meta-analysis of a prospective study.

AIM: Microalbuminuria has been elevated as an outcome predictor in cardiovascular medicine. However, due to the small number of studies investigating the association of microalbuminuria and mortality in the coronary heart disease (CHD) population, the prognosis value of microalbuminuria in CHD remains under debate. The objective of this meta-analysis was to investigate the relationship between microalbuminuria and mortality in individuals with CHD. METHOD: A comprehensive literature search was performed using Pubmed, EuroPMC, Science Direct, and Google Scholar from 2000 to September 2022. Only prospective studies investigating microalbuminuria and mortality in CHD patients were selected. The pooled effect estimate was reported as risk ratio (RR). RESULTS: 5176 patients from eight prospective observational studies were included in this meta-analysis. Individuals with CHD have a greater overall risk of all-cause mortality (ACM) [rR = 2.07 (95% CI = 1.70-2.44); p = 0.0003; I2 = 0.0%] as well as cardiovascular mortality (CVM) [rR = 3.23 (95% CI = 2.06-4.39), p < 0.0001; I2 = 0.0%]. Subgroup analysis based on follow-up duration and a subset of CHD patients were similarly associated with an increased risk of ACM. CONCLUSION: This meta-analysis indicates that microalbuminuria is associated with a higher risk of mortality in individuals with CHD. Microalbuminuria can serve as a predictor of poor outcomes in CHD patients.