The predictive solution is being developed with the objective to detect and predict any deterioration in the health status of heart failure patients. It comes with a smartphone application dedicated to patients as well as the predictive algorithm HOTS (Hierarchy Of event-based Time-Surfaces), directly embedded into the smartphone. The CE marking of the predictive solution is currently ongoing*. Combined with the connected medical device Keesense (CE Class IIa), the predictive solution will thus rely on the multiple physiological parameters collected by the t-shirt to generate clinically relevant alerts.
The predictive solution will be able to predict any cardiac decompensation before it occurs, allowing for more accurate and early treatment.
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 physiological parameters collected by the Keesense t-shirt will be transmitted via Bluetooth to the predictive solution’s smartphone application, which will embed the HOTS algorithm. Once integrated into the smartphone, HOTS will be able to merge the data collected by the t-shirt to create trends in the patient’s health status.
Eventually, HOTS will be able to generate, if necessary, clinical alerts on the patient’s current and future health status.
Extraction of the data
Fusion of the data
Analysis and alerts
* HOTS must go through a clinical trial in order to valiate its ability to detect and predict cardiac decompensations