Assessing cytokines' talking patterns following experimental myocardial damage by applying Shannon's information theory

Inflammation in the Pathogenesis of Arrhythmogenic Cardiomyopathy: Secondary Event or Active Driver?

Arrhythmogenic cardiomyopathy (ACM) is a rare inherited cardiac disease characterized by arrhythmia and progressive fibro-fatty replacement of the myocardium, which leads to heart failure and sudden cardiac death. Inflammation contributes to disease progression, and it is characterized by inflammatory cell infiltrates in the damaged myocardium and inflammatory mediators in the blood of ACM patients. However, the molecular basis of inflammatory process in ACM remains under investigated and it is unclear whether inflammation is a primary event leading to arrhythmia and myocardial damage or it is a secondary response triggered by cardiomyocyte death. Here, we provide an overview of the proposed players and triggers involved in inflammation in ACM, focusing on those studied using in vivo and in vitro models. Deepening current knowledge of inflammation-related mechanisms in ACM could help identifying novel therapeutic perspectives, such as anti-inflammatory therapy.

Arrhythmogenic cardiomyopathy: what blood can reveal?

Blood, serum and plasma represent accessible sources of data about physiological and pathologic status. In arrhythmogenic cardiomyopathy (ACM), circulating nucleated cells are routinely used for detection of germinal genetic mutations. In addition, different biomarkers have been proposed for diagnostic purposes and for monitoring disease progression, including inflammatory cytokines, markers of myocardial dysfunction and damage, and microRNAs. This review summarizes the current information that can be retrieved from the blood of ACM patients and considers the future prospects. Improvements in current knowledge of circulating factors may provide noninvasive means to simplify and improve the diagnosis, prognosis prediction, and management of ACM patients.

Using information theory to identify redundancy in common laboratory tests in the intensive care unit

BACKGROUND

Clinical workflow is infused with large quantities of data, particularly in areas with enhanced monitoring such as the Intensive Care Unit (ICU). Information theory can quantify the expected amounts of total and redundant information contained in a given clinical data type, and as such has the potential to inform clinicians on how to manage the vast volumes of data they are required to analyze in their daily practice. The objective of this proof-of-concept study was to quantify the amounts of redundant information associated with common ICU lab tests.

METHODS

We analyzed the information content of 11 laboratory test results from 29,149 adult ICU admissions in the MIMIC II database. Information theory was applied to quantify the expected amount of redundant information both between lab values from the same ICU day, and between consecutive ICU days.

RESULTS

Most lab values showed a decreasing trend over time in the expected amount of novel information they contained. Platelet, blood urea nitrogen (BUN), and creatinine measurements exhibited the most amount of redundant information on days 2 and 3 compared to the previous day. The creatinine-BUN and sodium-chloride pairs had the most redundancy.

CONCLUSIONS

Information theory can help identify and discourage unnecessary testing and bloodwork, and can in general be a useful data analytic technique for many medical specialties that deal with information overload.