Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes

Abstract

Background: Cardiac autonomic neuropathy (CAN) is a diabetes-related complication with increasing prevalence and remains challenging to detect in clinical settings. Machine learning (ML) approaches have the potential to predict CAN using clinical data. In this study, we aimed to develop and evaluate the performance of an ML model to predict early CAN occurrence in patients with diabetes. Methods: We used the diabetes complications screening research initiative data set containing 200 CAN-related tests on more than 2000 participants with type 2 diabetes in Australia. Data were collected on peripheral nerve functions, Ewing’s tests, blood biochemistry, demographics, and medical history. The ML model was validated using 10-fold cross-validation, of which 90% were used in training the model and the remaining 10% was used in evaluating the performance of the model. Predictive accuracy was assessed by area under the receiver operating curve, and sensitivity, specificity, positive predictive value, and negative predictive value. Results: Of the 237 patients included, 105 were diagnosed with an early stage of CAN while the remaining 132 were healthy. The ML model showed outstanding performance for CAN prediction with receiver operating characteristic curve of 0.962 [95% confidence interval (CI) = 0.939–0.984], 87.34% accuracy, and 87.12% sensitivity. There was a significant and positive association between the ML model and CAN occurrence ( p < 0.001). Conclusion: Our ML model has the potential to detect CAN at an early stage using Ewing’s tests. This model might be useful for healthcare providers for predicting the occurrence of CAN in patients with diabetes, monitoring the progression, and providing timely intervention.

Artificial intelligence‐enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes

“…Recent studies predicted CAN based on biochemical and demographic data and patients' history with the use of ML techniques 32 . Nedergaard et al 27 developed ensembled classification using established ECG‐derived features along with clinical and biochemical data.…”

Artificial intelligence-enhanced electrocardiogram analysis for identifying cardiac autonomic neuropathy in patients with diabetes

“…Recent studies predicted CAN based on biochemical, demographic data and patients' history with the use of ML techniques [34]. Nedergaard et al [25] developed ensembled classi cation using established ECGderived features along with clinical and biochemical data.…”

Consensus on Recommended Functions of a Smart Home System to Improve Self-Management Behaviors in People With Heart Failure: A Modified Delphi Approach

“…A smart home system with these functions could contribute to evidence gaps outlined in international clinical guidelines, including the need for more data on the effects of fluid restriction, dietary salt restriction and nutrition; the role of remote monitoring; optimal models for follow-up of stable heart failure patients; better definition and classification of patient phenotypes to facilitate improved treatment; and development of better strategies for congestion relief, including monitoring of diuretic administration (5,6). Smart homes can address these gaps by collecting these data directly from patients' home, using machine learning algorithms to create phenotypes, providing automated alerts, remote medication titrations and care (39)(40)(41). The findings may also have implications for technologybased programs for other chronic diseases in which selfmanagement is important (e.g., chronic obstructive pulmonary disease).…”