Molecool

From OpenWetWare
Jump to navigationJump to search

Molecool HRV Logo.png

Home        Background Research        Technology        Product        Journal        Lab Members       


Project Overview

Heart Rate Variability (HRV) is a measure of the intervals between the peaks of successive QRS complexes from an ECG. This measure provides an accurate, non-invasive measure of autonomic nervous system activity where low HRV values indicate a dominance of the sympathetic nervous system and poorer cardiac adaptability in response to stimulation. Research so far has proved specific links of HRV to poorer clinical outcomes, and morbidity.

While HRV is not widely used in current clinical settings, it does have great potential to provide a quantitative, objective measure for medical purposes and clinicians. Also, as a non-invasive measure, the measurement of HRV does not require much patient involvement and impact on their daily lives. HRV is proven to have the ability to preclude more serious symptoms of a disease, with great potential for early diagnosis before the disease has advanced to a stage where the symptoms are irreversible.

With this sense in mind, our team investigates the relationship between HRV and various diseases and finally focuses on cardiac autonomic neuropathy. Cardiac autonomic neuropathy (CAN) is a serious complication of diabetes mellitus (DM) that is strongly associated with an approximately five-fold increased risk of cardiovascular mortality. The development of CAN is associated with the lesion of the autonomic nervous system (ANS) and is more prevalent in type 2 diabetic patients. In 2021, there were approximately 537 million adults living with diabetes. In studies into patient cohorts, CAN prevalence was reported to range from as low as 2.5% to 90%. However, the official figures are not available because CAN is underdiagnosed in hospital settings.

In terms of its clinical implications, CAN is associated with a high risk of cardiac arrhythmias and sudden death. Longitudinal studies of subjects with CAN have shown 5-year mortality rates with 16% - 50% in type 1 and type 2 diabetes with a high proportion attributed to sudden cardiac death. In the EURODIAB Prospective Cohort Study of 2,787 type 1 diabetic patients, CAN was the strongest predictor for mortality during a 7 year follow up, exceeding the effect of traditional cardiovascular risk factors. In the detection of ischemia in an asymptomatic diabetes study of 1,123 patients with type 2 diabetes mellitus, CAN was a strong predictor of silent ischemia and subsequent cardiovascular events. Moreover, CAN has also been shown to be a strong predictor of ischemic stroke.

There are some existent diagnosis methods for CAN, including the most commonly used method utilising cardiac autonomic reflexes tests to calculate a total score known as the Ewing score. Scores between 0 - 5 with a greater score reflecting a more severe degree of CAN. Other measures include obtaining blood pressure or heart rate responses to standing, deep breathing, sustained handgrip.

However, there are unmet needs with existing diagnosis methods compared to HRV diagnosis. Firstly, it requires considerable patient participation and it only monitors patient condition at the time of the test.

When HRV analysis is applied in CAN indication, it has strong advantages. Firstly, spectral HRV analysis has been shown to be more sensitive and can detect disorders even in the early stages. HRV can be monitored over a longer period of time, providing a more holistic representation of the patient's autonomic function. HRV is also proven to be an easier, less stressful and faster method during daily use and relatively independent of patient cooperation. As some patients may not be able to perform the CARTs, HRV can be used as an alternative.

The application of HRV for the detection of diabetic CVDs and early diagnosis of CAN has much significance. As CAN is typically underdiagnosed in hospital settings, HRV can help overcome that. As currently there are no approved medicines/therapies limited to clinical trials and the fact that once CAN has progressed to an advanced stage, it is difficult to treat or reverse, applying HRV allows for an early diagnosis of CAN which would be beneficial for further monitoring and the treatment process. When diagnosed early, interventions can be conducted to slow or prevent progression: for type 1 diabetics, more intensive glycaemic control can be applied; for type 2 diabetics, multifactor treatment including lifestyle changes and more intensive glycaemic control will be implemented.

Main Objective

Our project aim to achieve the following objectives:

  • Investigate the potential of using HRV as a diagnostic tool to be widely applied in clinical settings
  • Design a program which can extract HRV measures from ECG recordings
  • Investigate the effects of diabetic neuropathy on HRV measures to assess whether HRV can be used as to screen diabetic patients who are at risk of diabetic neuropathy
  • Apply our HRV extraction program to a sample database to test the accuracy of the program and verify the conclusions gained from the literature regarding the associations between DCAN and HRV
  • Design a potential application for diabetic patients which regularly records ECGs and extracts HRV data to screen them for CAN or other CVDs