Molecool:Clinician's Perspective

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Clinician Interactions

We are planning to conduct an interview with a clinician that specialises in treating diabetes to gain insights into CAN that could help our project.

Background Information

Prior to the interview, we will provide the clinician with background information so that the interview can be focused on asking the clinician our questions.

What is Heart Rate Variability?

A measure of the R-R intervals between the R peaks of successive QRS complexes from an ECG. It provides an accurate, non-invasive measure of autonomic nervous system activity. A low HRV can indicate a dominance of the sympathetic nervous system and poorer cardiac adaptability in response to stimulation. The clinical relevance of HRV has been appreciated since 1965 when it was first noticed that foetal distress was preceded by alterations in inter-beat intervals before any appreciable change occurred in the heart rate itself.

How is HRV measured and analysed?

• 1. Obtain ECGs
• 2. Apply algorithms to calculate R-R intervals between successive R peaks
• 3. Calculate HRV parameters using R-R intervals
• 4. Statistical tests can be carried out to compare HRV data between experimental and control groups

What is the physiological significance of HRV?

Heart rate and HRV are under the control of the autonomic nervous system which is composed of the sympathetic and parasympathetic nervous systems. HRV measurements assess the condition of these nervous systems. The sympathetic nervous system controls the stimulation of the ‘fight or flight stress response’, therefore its dominance is reflected by increased HR and decreased HRV. The parasympathetic nervous system controls stimulation of ‘rest and digest’ activities essential for recovery, therefore its dominance is reflected by decreased HR and increased HRV. Changes in HRV are largely dependent on parasympathetic activity which dominates during resting conditions. Parameters can be calculated from R-R interval HRV data for further analysis and provide physiological information. Examples of such parameters include:

• pNN50 – the percentage of adjacent R-R intervals that differ from each other by more than 50ms, it is closely correlated with PNS activity
• RMSSD – the root the mean square of successive differences between normal heartbeats, one of the primary measures used to estimate the parasympathetic contributions to HRV
• SDNN – the standard deviation of R-R intervals, it has contributions from both the sympathetic and parasympathetic nervous systems

How can HRV be applied in clinical settings?

Many studies have been carried out to investigate the effects of a disease on HRV; diseases are generally associated with a decrease in HRV. These changes in HRV generally preclude the more significant symptoms and offer the possibility of diagnosis before the disease has progressed to a more debilitating stage. Examples of diseases and conditions which are associated with changes in HRV include: depression, diabetes, lupus, and Parkinson’s disease. An ideal clinical usage of HRV measures would be to serve as additional information to support the diagnosis/monitoring of a disease. It offers many benefits in a clinical setting; it is a quantitative measure, and its extraction is quick and non-invasive.

What is the efffect of diabetes on HRV?

Diabetes is associated with a general decrease in HRV, particularly SDNN, which worsens with increasing diabetes duration. A low SDNN value is an early sign of cardiac diabetic autonomic neuropathy and can predict the progression of coronary artery calcification. The former marks the onset of atherosclerosis while the latter is a potential cause for the development and acceleration of atherosclerosis. Therefore, HRV measures could be used to diagnose cardiac autonomic neuropathy and monitor the progression of atherosclerosis.

What is the purpose of our project?

• Investigate the advantages of HRV measurements in clinical settings and how it could be used to support current methods for diagnosis/monitoring disease progression
• Design a program which can extract HRV data from an ECG recording
• Investigate the effects of diabetes complications such as neuropathy on HRV with the intent of using HRV to help diagnose and monitor these conditions
• Compare HRV data from a sample of diabetes patients to a healthy control group to find HRV parameters which are significantly different between them
  • Measurements of these parameters could serve as additional information to help diagnose diabetes conditions

Interview Questions

The questions we wish to ask the clinician are divided into several categories

General Diabetes

• Does a significant number of diabetic patient suffer from heart conditions?
• What do you believe to be the most effective method for diagnosing diabetes/monitoring diabetes progression?
• What are some issues or unmet needs with this method?
• What diabetes complications are not well understood or diagnosed?

Diabetic Neuropathy

• Is cardiac autonomic neuropathy a significant issue in patients?
• Are most of the cases of neuropathy directly caused by diabetes?
• Do patients which suffer from CAN commonly suffer from more serious cardiac evenets (such as strokes)?
• Is diagnosing CAN difficult?
• What are the commonly used methods for diagnosing CAN?
• What some issues or unmet needs with these methods?
• Do you believe CAN to be underdiagnosed?
• Is CAN insufficiently understood?

HRV discussion

• Do you or other clinicians you know currently take ECG/HRV measurements in diabetes treatment?
  • If so:
    • What do you use them for?
    • How effective or useful do you think they are?
  • If not:
    • Do you think that HRV is underused and has the potential to be used in diabetes diagnosis/monitoring?
    • What do you think needs to be to done for HRV to be more widely used?