1. Quantification and classification of potassium and calcium disorders with the electrocardiogram: What do clinical studies, modeling, and reconstruction tell us?
- Author
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Nicolas Pilia, Jochen G. Raimann, Peter Kotanko, Axel Loewe, Cristiana Corsi, Simonetta Genovesi, Olaf Dössel, Stefano Severi, Pilia, N, Severi, S, Raimann, J, Genovesi, S, Dossel, O, Kotanko, P, Corsi, C, Loewe, A, Pilia N., Severi S., Raimann J.G., Genovesi S., Dossel O., Kotanko P., Corsi C., and Loewe A.
- Subjects
CHRONIC KIDNEY-DISEASE ,lcsh:Medical technology ,BIG DATA ,Computer science ,lcsh:Biotechnology ,Biomedical Engineering ,Biophysics ,Reviews ,Bioengineering ,electrocardiogram ,Machine learning ,computer.software_genre ,HYPERKALEMIA ,End stage renal disease ,Biomaterials ,lcsh:TP248.13-248.65 ,end stage renal disease ,Wearable technology ,Engineering & allied operations ,HEART-RATE-VARIABILITY ,business.industry ,ECG ,potassium ,QT INTERVAL ,IN-VIVOHEMODIALYSIS ,electrolyte concentration ,Clinical Practice ,lcsh:R855-855.5 ,Calcium concentration ,Artificial intelligence ,ddc:620 ,VENTRICULAR REPOLARIZATION ,Calcium disorder ,business ,computer ,SUDDEN CARDIAC DEATH - Abstract
Diseases caused by alterations of ionic concentrations are frequently observed challenges and play an important role in clinical practice. The clinically established method for the diagnosis of electrolyte concentration imbalance is blood tests. A rapid and non-invasive point-of-care method is yet needed. The electrocardiogram (ECG) could meet this need and becomes an established diagnostic tool allowing home monitoring of the electrolyte concentration also by wearable devices. In this review, we present the current state of potassium and calcium concentration monitoring using the ECG and summarize results from previous work. Selected clinical studies are presented, supporting or questioning the use of the ECG for the monitoring of electrolyte concentration imbalances. Differences in the findings from automatic monitoring studies are discussed, and current studies utilizing machine learning are presented demonstrating the potential of the deep learning approach. Furthermore, we demonstrate the potential of computational modeling approaches to gain insight into the mechanisms of relevant clinical findings and as a tool to obtain synthetic data for methodical improvements in monitoring approaches.
- Published
- 2020