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. 2019 May 1;4(5):428-436.
doi: 10.1001/jamacardio.2019.0640.

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Conner D Galloway  1 , Alexander V Valys  1 , Jacqueline B Shreibati  1 , Daniel L Treiman  1 , Frank L Petterson  1 , Vivek P Gundotra  1 , David E Albert  1 , Zachi I Attia  2 , Rickey E Carter  2   3 , Samuel J Asirvatham  2 , Michael J Ackerman  2 , Peter A Noseworthy  2 , John J Dillon  4 , Paul A Friedman  2
Affiliations

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Conner D Galloway et al. JAMA Cardiol. .

Abstract

Importance: For 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, and participants: A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018.

Exposures: Use of a deep-learning model.

Main outcomes and measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity.

Results: Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona.

Conclusions and relevance: In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

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Conflict of interest statement

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Potassium sensing technology from the ECG was developed by Mayo Clinic in collaboration with Ben Gurion University. Mayo Clinic licensed patent applications and technology know-how to AliveCor and also invested in the company. Joint weekly telephone calls and meetings between Mayo Clinic and AliveCor scientists further enhanced the potassium-sensing technology. Several of the Mayo clinic authors contributing to the technology (Drs Attia, Asirvatham, Ackerman, Dillon, and Friedman) may benefit from its commercialization. However, Mayo Clinic and Mayo inventors will not receive financial benefit from use of the technology at Mayo Clinic. Dr Galloway reported support from AliveCor Inc during the conduct of the study and outside the submitted work; in addition, Dr Galloway had a patent to ECG Deep Learning for Detection of Hyperkalemia pending and is an employee of AliveCor. Dr Valys reported a patent to deep learning for analyte measurement from ECG pending. Dr Shreibati reported support from AliveCor during the conduct of the study. Dr Gundotra reported support from AliveCor during the conduct of the study. Dr Albert reported support from AliveCor Inc during the conduct of the study and outside the submitted work; in addition, Dr Albert had a patent to ECG Deep Learning for Detection of Hyperkalemia pending and is the founder and Chief Medical Officer of AliveCor, which has licensed technology and intellectual property from the Mayo Clinic with regard to this research. AliveCor developed the technology and has filed patent applications regarding this work and has collaborated with Mayo Clinic in the clinical validation of this technology. Mr Attia reported patents to US 8948854, US 9307921, US 9907478, and IL 224207 issued, licensed, and with royalties paid and patents to EP 2593001, US 2018/014687, US 2016/0256063 A1, and EP 3048965 pending, licensed, and with royalties paid. Dr Ackerman reported support from AliveCor during the conduct of the study and a patent to potassium-sensing technology. Dr Dillon reported a patent to ECG-based potassium measurement issued and licensed. Dr Friedman reported a patent to potassium-sensing technology. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Development and Validation Data Sets Generation and Electrocardiogram (ECG) Labeling of Hyperkalemia
To convert potassium to millimoles per liter, multiply by 1. aEstimated glomerular filtration rate calculated from the Chronic Kidney Disease Epidemiology Collaboration equation, using the mean serum creatinine values within 24 hours prior to time of ECG recording. bGiven error in serum potassium, and to avoid potentially irrelevant misclassification rates around hyperkalemia threshold, ECGs with serum potassium greater than 5.3 mEq/L or less than 5.7 mEq/L were excluded. cRandom ECG selected for Minnesota dataset. Most recent ECG selected for Arizona and Florida datasets.
Figure 2.
Figure 2.. Validation Data Set Performance for Hyperkalemia From Lead I and II of the Electrocardiogram
AUC indicates area under the receiver operating characteristic curve.
See this image and copyright information in PMC

References

    1. Fitch K, Woolley JM, Engel T, Blumen H. The clinical and economic burden of hyperkalemia on medicare and commercial payers. Am Health Drug Benefits. 2017;10(4):202-210. - PMC - PubMed
    1. Luo J, Brunelli SM, Jensen DE, Yang A. Association between serum potassium and outcomes in patients with reduced kidney function. Clin J Am Soc Nephrol. 2016;11(1):90-100. doi: 10.2215/CJN.01730215 - DOI - PMC - PubMed
    1. Einhorn LM, Zhan M, Hsu VD, et al. The frequency of hyperkalemia and its significance in chronic kidney disease. Arch Intern Med. 2009;169(12):1156-1162. doi: 10.1001/archinternmed.2009.132 - DOI - PMC - PubMed
    1. Raebel MA, Ross C, Xu S, et al. Diabetes and drug-associated hyperkalemia: effect of potassium monitoring. J Gen Intern Med. 2010;25(4):326-333. doi: 10.1007/s11606-009-1228-x - DOI - PMC - PubMed
    1. Kidney Disease Outcomes Quality Initiative (K/DOQI) K/DOQI clinical practice guidelines on hypertension and antihypertensive agents in chronic kidney disease. Am J Kidney Dis. 2004;43(5)(suppl 1):S1-S290. - PubMed

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