A solution aimed at predicting cardiac decompensation

The accurate prediction of heart failure events is complicated as it requires a sophisticated set of data analysis into key clinical features that positively correlate with cardiac exacerbations. These risk factors need to be continuously examined individually to determine clinically-relevant deviations, but also together as a whole to paint a comprehensive picture of patients’ health status which will reveal insights into how these risk actors react against each other –– key to isolating potential heart failure events from other medical or non-medical incidents, reducing false positive results or false alarms. 

Chronolife’s predictive solution, combined with the connected t-shirt Keesense, is thus designed to well address these challenges: 

• The comfortable and machine-washable t-shirt integrates naturally into patients’ daily life and facilitates a great degree of patient compliance, ensuring the data integrity, continuity and accuracy to feed into predictive analysis 
• Keesense continuously monitors 6 key risk factors relevant to heart failures in a concurrent and synchronized manner to accurately paint a detailed picture of patients’ overall health status 
• Chronolife’s machine learning algorithm HOTS is capable of extracting, processing, and analyzing complex data streams: physicians will benefit from near-real time alerts generated by the HOTS algorithm, that indicate a deterioration in the patient’s health.