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.

Learning Intelligent for Effective Sonography (LIFES) Model for Rapid Diagnosis of Heart Failure in Echocardiography.

BACKGROUND: The accuracy of an artificial intelligence model based on echocardiography video data in the diagnosis of heart failure (HF) called LIFES (Learning Intelligent for Effective Sonography) was investigated. METHODS: A cross-sectional diagnostic test was conducted using consecutive sampling of HF and normal patients' echocardiography data. The gold-standard comparison was HF diagnosis established by expert cardiologists based on clinical data and echocardiography. After pre-processing, the AI model is built based on Long-Short Term Memory (LSTM) using independent variable estimation and video classification techniques. The model will classify the echocardiography video data into normal and heart failure category. Statistical analysis was carried out to calculate the value of accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and likelihood ratio (LR). RESULTS: A total of 138 patients with HF admitted to Harapan Kita National Heart Center from January 2020 to October 2021 were selected as research subjects. The first scenario yielded decent diagnostic performance for distinguishing between heart failure and normal patients. In this model, the overall diagnostic accuracy of A2C, A4C, PLAX-view were 92,96%, 90,62% and 88,28%, respectively. The automated ML-derived approach had the best overall performance using the 2AC view, with a misclassification rate of only 7,04%. CONCLUSION: The LIFES model was feasible, accurate, and quick in distinguishing between heart failure and normal patients through series of echocardiography images.