Heart rate variability evaluation in the assessment of cardiac autonomic neuropathy in patients with type 2 diabetes

Metelka, Rudolf; Cibičková, Ľubica; Gajdova, Jaromira; Krystynik, Ondrej

doi:10.1016/j.crvasa.2017.05.001

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Increase in HbA1c showed progressively increasing risks of CVD [66]. Ewing tests has shown to an independent prognostic indicator of sudden arrhythmic death risk [67]. Abnormalities in HbA1c was associated with both DM and CVD [59,68,69].…”

Section: Discussionmentioning

confidence: 99%

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Abdalrada

Abawajy

Al-Quraishi

et al. 2022

J Diabetes Metab Disord

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Background Diabetic mellitus (DM) and cardiovascular diseases (CVD) cause significant healthcare burden globally and often co-exists. Current approaches often fail to identify many people with co-occurrence of DM and CVD, leading to delay in healthcare seeking, increased complications and morbidity. In this paper, we aimed to develop and evaluate a two-stage machine learning (ML) model to predict the co-occurrence of DM and CVD. Methods We used the diabetes complications screening research initiative (DiScRi) dataset containing >200 variables from >2000 participants. In the first stage, we used two ML models (logistic regression and Evimp functions) implemented in multivariate adaptive regression splines model to infer the significant common risk factors for DM and CVD and applied the correlation matrix to reduce redundancy. In the second stage, we used classification and regression algorithm to develop our model. We evaluated the prediction models using prediction accuracy, sensitivity and specificity as performance metrics. Results Common risk factors for DM and CVD co-occurrence was family history of the diseases, gender, deep breathing heart rate change, lying to standing blood pressure change, HbA1c, HDL and TC\HDL ratio. The predictive model showed that the participants with HbA1c >6.45 and TC\HDL ratio > 5.5 were at risk of developing both diseases (97.9% probability). In contrast, participants with HbA1c >6.45 and TC\HDL ratio ≤ 5.5 were more likely to have only DM (84.5% probability) and those with HbA1c ≤5.45 and HDL >1.45 were likely to be healthy (82.4%. probability). Further, participants with HbA1c ≤5.45 and HDL <1.45 were at risk of only CVD (100% probability). The predictive accuracy of the ML model to detect co-occurrence of DM and CVD is 94.09%, sensitivity 93.5%, and specificity 95.8%. Conclusions Our ML model can significantly predict with high accuracy the co-occurrence of DM and CVD in people attending a screening program. This might help in early detection of patients with DM and CVD who could benefit from preventive treatment and reduce future healthcare burden.

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Section: Discussionmentioning

confidence: 99%

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Abdalrada

Abawajy

Al-Quraishi

et al. 2022

J Diabetes Metab Disord

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“…In contrast, the AACE, ACE, and Toronto Consensus recognize the clinical and prognostic value of the HRV test[ 10 ]. Despite the importance of early detection, there is no harmonized definition of CAN, and CAN is frequently diagnosed late[ 83 ].…”

Section: Critical Reflectionmentioning

confidence: 99%

Cardiovascular autonomic neuropathy in diabetes: Pathophysiology, clinical assessment and implications

Duque

Mediano

Lorenzo

et al. 2021

WJD

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Cardiovascular autonomic neuropathy (CAN) is a debilitating condition that mainly occurs in long-standing type 2 diabetes patients but can manifest earlier, even before diabetes is diagnosed. CAN is a microvascular complication that results from lesions of the sympathetic and parasympathetic nerve fibers, which innervate the heart and blood vessels and promote alterations in cardiovascular autonomic control. The entire mechanism is still not elucidated, but several aspects of the pathophysiology of CAN have already been described, such as the production of advanced glycation end products, reactive oxygen species, nuclear factor kappa B, and pro-inflammatory cytokines. This microvascular complication is an important risk factor for silent myocardial ischemia, chronic kidney disease, myocardial dysfunction, major cardiovascular events, cardiac arrhythmias, and sudden death. It has also been suggested that, compared to other traditional cardiovascular risk factors, CAN progression may have a greater impact on cardiovascular disease development. However, CAN might be subclinical for several years, and a late diagnosis increases the mortality risk. The duration of the transition period from the subclinical to clinical stage remains unknown, but the progression of CAN is associated with a poor prognosis. Several tests can be used for CAN diagnosis, such as heart rate variability (HRV), cardiovascular autonomic reflex tests, and myocardial scintigraphy. Currently, it has already been described that CAN could be detected even during the subclinical stage through a reduction in HRV, which is a non-invasive test with a lower operating cost. Therefore, considering that diabetes mellitus is a global epidemic and that diabetic neuropathy is the most common chronic complication of diabetes, the early identification and treatment of CAN could be a key point to mitigate the morbidity and mortality associated with this long-lasting condition.

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Impaired Cardiovagal Activity as a Link Between Hyperglycemia and Arterial Stiffness in Adults With Type 2 Diabetes Mellitus Among an Eastern Indian Population: A Cross-sectional Study

Priyadarsini,

Likhitha,

Ramachandran

et al. 2024

Canadian Journal of Diabetes

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Heart rate variability evaluation in the assessment of cardiac autonomic neuropathy in patients with type 2 diabetes

Cited by 6 publications

References 10 publications

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study

Cardiovascular autonomic neuropathy in diabetes: Pathophysiology, clinical assessment and implications

Impaired Cardiovagal Activity as a Link Between Hyperglycemia and Arterial Stiffness in Adults With Type 2 Diabetes Mellitus Among an Eastern Indian Population: A Cross-sectional Study

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