Predictive Solution

Improving the patient's quality of life and limiting (re)hospitalization thanks to the predictive solution

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.

How the predictive solution works

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.

Prediction of Worsening Heart Failure

Remote Patient Monitoring for Predicting Worsening Heart Failure

Heart Failure (HF) is a global health concern that’s affecting over 64 million people around the world. In the USA for example, the total healthcare cost for HF in 2020 was estimated at $43.6 bilion, with more than 70% coming from inpatient medical expenditures. Research has found that HF patients with a history of hospitalization experienced 80.4% “all-cause readmission”, 43.3% HF-specific readmissions and 75.4% mortality rates.

Reducing Hospital Readmission for Patients with Heart Failure 

With the global shortage of healthcare resources especially hospital bed units and the growing number of patients suffering from heart failure (46% increase by 2030), innovative solutions are urgently needed to monitor HF patients post-discharge and prevent worsening events. Thanks to the increasing adoption of remote patient monitoring (RPM) solutions, HF patients can safely and reliably have their heart conditions continuously monitored as they recover from the comfort of their homes.

Importance of Multiparametric Monitoring for Detecting CHF 

The accurate monitoring and prediction of worsening heart failure events is complicated as it requires a sophisticated set of data analysis into key clinical features that positively correlate with cardiac exacerbations. Therefore, relying on basic, singular methods such as weights monitoring and symptoms checking are simply not adequate. More importantly, commodity consumer-grade wearable sensors have not been scientifically configured to monitor complex physiological signals such as thoracic impedance, which is a significant indicator of potential heart failure exacerbation.

How Chronolife’s Predictive Solution Addresses the Clinical Efficacy of RPM in Predicting HF

Chronolife’s HF prediction solution is based on a multiparametric remote patient monitoring solution featuring multifactorial algorithms combining thoracic impedance and abdominal respiration with a holistic set of biometrics including heart rate, electrocardiogram, physical activity, and skin temperature, to detect HF clinical decompensation. The comprehensive nature of the medical-grade device allows it to monitor all interrelated symptoms and physiological indicators for HF, including increased lung fluid content, shortness of breath, palpitations, electrical heart signals, and so forth. 

According to the International Journal of Heart Failure’s ​​review of existing evidence regarding RPM in HF management, enhancing patient adherence is key to clinical efficacy. Several numbers of existing trials on HF telemonitoring have shown high degrees of patient nonadherence or drop off rates over time. Among the studies of noninvasive biometric sensing demonstrating clinical efficacy, higher patient adherence to data collection and transmission was observed. The study also notes that “automated data transmission via Bluetooth-enabled devices to a central hub has also become an often-employed strategy across many digital RPMs to prevent the need for manual input by patients.”

Chronolife’s HF predictive solution is based on a 100% machine-washable smart t-shirt that wears like any other daily undergarments. It has been shown to achieve a patient compliance rate of 97% because it’s intuitive to use, nonobstructive to patients, and doesn’t require any manual data intake thanks to our proprietary HOTS machine learning algorithm that is capable of extracting, processing, and analyzing complex data streams on low bandwidth such as smartphones or tablets via bluetooth.

The multiparametric algorithm and the high adherence nature of Chronolife’s HF predictive solution means that all patient data feeding into the predictive algorithm is reliable and comprehensive to generate as accurate alerts as possible regarding potential exacerbation events.

The HOTS algorithm can also be embedded with a wide variety of connected devices (such as smart weights and blood pressure monitors) to customize patients’ health status monitoring and prediction programs based on their unique medical history, doctors’ prescriptions, and other lifestyle peculiarities.