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

AIM: To develop and employ machine learning (ML) algorithms to analyse electrocardiograms (ECGs) for the diagnosis of cardiac autonomic neuropathy (CAN). MATERIALS AND METHODS: We used motif and discord extraction techniques, alongside long short-term memory networks, to analyse 12-lead, 10-s ECG tracings to detect CAN in patients with diabetes. The performance of these methods with the support vector machine classification model was evaluated using 10-fold cross validation with the following metrics: accuracy, precision, recall, F1 score, and area under the receiver-operating characteristic curve (AUC). RESULTS: Among 205 patients (mean age 54 ± 17 years, 54% female), 100 were diagnosed with CAN, including 38 with definite or severe CAN (dsCAN) and 62 with early CAN (eCAN). The best model performance for dsCAN classification was achieved using both motifs and discords, with an accuracy of 0.92, an F1 score of 0.92, a recall at 0.94, a precision of 0.91, and an excellent AUC of 0.93 (95% confidence interval [CI] 0.91-0.94). For the detection of any stage of CAN, the approach combining motifs and discords yielded the best results, with an accuracy of 0.65, F1 score of 0.68, a recall of 0.75, a precision of 0.68, and an AUC of 0.68 (95% CI 0.54-0.81). CONCLUSION: Our study highlights the potential of using ML techniques, particularly motifs and discords, to effectively detect dsCAN in patients with diabetes. This approach could be applied in large-scale screening of CAN, particularly to identify definite/severe CAN where cardiovascular risk factor modification may be initiated.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project.

AIMS: As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach. METHODS AND RESULTS: We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration. CONCLUSIONS: Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.

Relationship of vitamin D deficiency with cardiovascular disease and glycemic control in patients with type 2 diabetes mellitus: the Silesia Diabetes-Heart Project.

INTRODUCTION: Vitamin D (VD) has a pleiotropic effect on many health­related aspects, yet the results of studies regarding vitamin D deficiency (VDD) and both glycemic control and cardiovascular disease (CVD) are conflicting. OBJECTIVE: The aim of this work was to determine the prevalence of VDD and its associations with CVD and glycemic control among patients with type 2 diabetes mellitus (T2DM). PATIENTS AND METHODS: This was an observational study in T2DM patients recruited at the diabetology clinic in Zabrze, Poland (April-September 2019 and April-September 2020). The presence of CVD was determined based on medical records. Blood biochemical parameters, densitometry, and carotid artery ultrasound examination were performed. Control of diabetes was assessed based on glycated hemoglobin A1c (HbA1c) levels. A serum VD level below 20 ng/ml was considered as VDD. RESULTS: The prevalence of VDD in 197 patients was 36%. CVD was evident in 27% of the patients with VDD and in 33% of the patients with VD within the normal range (vitamin D sufficiency [VDS]) (P = 0.34). The difference between the groups regarding diabetes control was insignificant (P = 0.05), as for the VDD patients the median value (interquartile range) of HbA1c was 7.5% (6.93%-7.9%), and for VDS patients it was 7.5% (6.56%-7.5%). The VDD patients were more often treated with sodium­glucose cotransporter­2 inhibitors (SGLT­2is) (44% vs 25%; P = 0.01). CONCLUSIONS: About one­third of the patients showed VDD. The VDD and VDS groups did not differ in terms of CVD occurrence and the difference in glycemic control was insignificant. The patients with VDD were more often treated with SGLT­2is, which requires further investigation.