1. Cardiac Troponin for Assessment of Myocardial Injury in COVID-19: JACC Review Topic of the Week.
作者:Sandoval Yader , Januzzi James L , Jaffe Allan S
期刊:Journal of the American College of Cardiology
日期:2020-07-08
DOI :10.1016/j.jacc.2020.06.068
Increases in cardiac troponin indicative of myocardial injury are common in patients with coronavirus disease-2019 (COVID-19) and are associated with adverse outcomes such as arrhythmias and death. These increases are more likely to occur in those with chronic cardiovascular conditions and in those with severe COVID-19 presentations. The increased inflammatory, prothrombotic, and procoagulant responses following severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection increase the risk for acute nonischemic myocardial injury and acute myocardial infarction, particularly type 2 myocardial infarction, because of respiratory failure with hypoxia and hemodynamic instability in critically ill patients. Myocarditis, stress cardiomyopathy, acute heart failure, and direct injury from SARS-CoV-2 are important etiologies, but primary noncardiac conditions, such as pulmonary embolism, critical illness, and sepsis, probably cause more of the myocardial injury. The structured use of serial cardiac troponin has the potential to facilitate risk stratification, help make decisions about when to use imaging, and inform stage categorization and disease phenotyping among hospitalized COVID-19 patients.
添加收藏
创建看单
引用
1区Q1影响因子: 38.6
打开PDF
登录
英汉
2. Machine Learning in Medicine.
作者:Deo Rahul C
期刊:Circulation
日期:2015-11-17
DOI :10.1161/CIRCULATIONAHA.115.001593
Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.