2019
DOI: 10.1001/jamacardio.2019.0640
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Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Abstract: IMPORTANCEFor patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition.OBJECTIVE To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. DESIGN, SETTING, A… Show more

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Cited by 235 publications
(144 citation statements)
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“…Introduction of AI in medicine enables the analysis of high-density data such as biosignals; in particular, the conventional 12-electrode ECG is at the forefront of this research. One example is a study that screens for hyperkalemia (serum potassium levels > 5.5 mEq/L) among patients with chronic kidney disease based on a 12-electrode ECG [18]. In the study, researchers used data from 1,576,581 ECGs collected from a single hospital in the United States from 449,380 patients with potassium test records taken within 12 hours of recording their ECG.…”
Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning
confidence: 99%
“…Introduction of AI in medicine enables the analysis of high-density data such as biosignals; in particular, the conventional 12-electrode ECG is at the forefront of this research. One example is a study that screens for hyperkalemia (serum potassium levels > 5.5 mEq/L) among patients with chronic kidney disease based on a 12-electrode ECG [18]. In the study, researchers used data from 1,576,581 ECGs collected from a single hospital in the United States from 449,380 patients with potassium test records taken within 12 hours of recording their ECG.…”
Section: Recent Cases Of Application Of Ai To Biosignals In Medicinementioning
confidence: 99%
“…The application of deep learning technology in the cardiovascular eld for arrhythmias, dyskalemia, and valvular heart disease had become popularized recently. [19][20][21][27][28][29] However, no large scale study has been designed to apply deep learning technology for MI detection. Previous DLMs for MI detection by ECG were analyzed mainly from the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG Database.…”
Section: Discussionmentioning
confidence: 99%
“…[15][16][17][18] DLM have been con rmed to surpass the cardiologist level on ECG interpretation when they are trained by large annotated ECG datasets. [19][20][21] To our knowledge, the available ECG databases of AMI are relative small. [22] Our study aimed to develop a DLM to timely, objectively and precisely diagnose AMI by ECG.…”
Section: Introductionmentioning
confidence: 99%
“…Additionally,the model is a screening test with low specificity, with upwards of 42% false-positive results, which may cause anxiety and inconvenience for patients. [22]. Pilia et al also use an artificial neural network to reconstruct the extracellular ionic concentrations for both potassium and calcium with an acceptable precision in CKD patients [23].…”
Section: Alerting Akimentioning
confidence: 99%