The Heart in Diabetic Ketoacidosis: A Narrative Review Focusing on the Acute Cardiac Effects and Electrocardiographic Abnormalities

Tunable Q-factor wavelet transform based identification of diabetic patients using ECG signals

Diabetes is a chronic health condition that is characterized by increased levels of glucose (sugar) in the blood. It can have harmful effects on different parts of the body, such as the retina of the eyes, skin, nervous system, kidneys, and heart. Diabetes affects the structure of electrocardiogram (ECG) impulses by causing cardiovascular autonomic dysfunction. Multi-resolution analysis of the input ECG signal is utilized in this paper to develop a machine learning-based system for the automated detection of diabetic patients. In the first step, the input ECG signal is decomposed into sub-bands utilizing the tunable Q-factor wavelet transform (TQWT) technique. In the second step, four entropy-based characteristics are evaluated from each SB and elected using the K-W test method. To develop an automatic diabetes detection system, selected features are given as input with 10-fold validation to a SVM classifier using various kernel functions. The 3 rd sub-band of TQWT with the Coarse Gaussian kernel function kernel of the SVM classifier yields a classification accuracy of 91.5%. In the same dataset, the comparative analysis demonstrates that the proposed method outperforms other existing methods.

Early Repolarization Augmentation Mimicking Pseudo-Infarction in a Patient With Diabetic Ketoacidosis and Normokalemia

Early repolarization (ER) changes, characterized by J point elevation with or without ST-segment elevation, are dynamic in their presentation and can be exacerbated by factors such as hypothermia, hypercalcemia, vagotonia, and certain medications. There is limited research regarding the mechanism of these changes and the dynamic changes of ER secondary to diabetic ketoacidosis (DKA). This case report highlights the augmentation of early repolarization changes resembling ST-segment elevation myocardial infarction (STEMI) in a patient with DKA that resolved with the treatment of acidosis. The misinterpretation of ER changes on electrocardiogram (ECG) as STEMI or pericarditis may result in the inappropriate utilization of resources, increased patient risk, and elevated morbidity and mortality. Recognition of the potential of DKA to cause ER changes can potentially avoid these unfavorable outcomes.

Diabetic Ketoacidosis With Refractory Hypokalemia Leading to Cardiac Arrest

Diabetic ketoacidosis (DKA) is known to cause total body potassium depletion, but during initial presentation, very few patients are hypokalemic, and even fewer patients experience clinical effects. As the correction of acidosis and insulin drive potassium intracellularly, measured serum potassium levels decrease and require repletion. This phenomenon is well described, and severe hypokalemia necessitates delaying insulin therapy. Less well described is the kaliuretic nature of treatments of cerebral edema. We present a case of an adolescent male with new-onset type 2 diabetes who presented in DKA with signs of cerebral edema, hyperosmolarity, and hypokalemia. As insulin and cerebral edema therapy were initiated, his hypokalemia worsened despite significant IV repletion, eventually leading to ventricular tachycardia and cardiac arrest. Over the following 36 hours, the patient received >590 milliequivalents (mEq) of potassium. He was discharged home 12 days after admission without sequelae of his cardiac arrest.