Novel Bloodless Potassium Determination Using a Signal‐Processed Single‐Lead ECG

Attia, Zachi I.; DeSimone, Christopher V.; Dillon, John; Sapir, Yehu; Somers, Virend K.; Dugan, Jennifer; Bruce, Charles J.; Ackerman, Michael J.; Asirvatham, Samuel J.; Striemer, Bryan L.; Bukartyk, Jan; Scott, Christopher G.; Bennet, Kevin E.; Ladewig, Dorothy J.; Gilles, Emily J.; Sadot, Dan; Geva, Amir B.; Friedman, Paul A.

doi:10.1161/jaha.115.002746

Cited by 72 publications

(73 citation statements)

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“…The filtered complexes were signal averaged using the R-peak as the fiducial point to eliminate noise. The averaged processed ECG complex was normalized to have a variance of one to adjust for the differences related to gain, fluid shifts, or skin impedance) [7,8] and was used for feature extraction for analysis. T wave peak and end points were selected in an automated manner.…”

Section: Methodsmentioning

confidence: 99%

“…T wave amplitude (T-amp) was measured as the difference in amplitude between the T wave peak and end. Any ECG samples for which the automatic detection software could not reliably extract the relevant ECG features were rejected [7,8]. …”

Section: Methodsmentioning

confidence: 99%

“…Model generation: Previous work identified several T-wave features on ECG that correlate with measured potassium including slope of the T-wave, T-wave duration and amplitude [5,6,8]. Based on our previous analysis, we used the

\frac{T - italicright slope}{\sqrt{T - amp}}

to create a personalized potassium prediction model that was unique to each individual patient [8].…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, ECG models were shown to be useful in the diagnosis of hyperkalemia [6]. We previously demonstrated that, with ECG filtering and signal processing, subclinical changes in the surface ECG can be detected and used to calculate serum potassium levels in patients undergoing hemodialysis [7,8]. More recently, we used signal-processed ECG data derived from a band-aid-like, single lead device, attached to the chest, to calculate potassium values to within 10% of blood test results in dialysis patients [8].…”

Section: Introductionmentioning

confidence: 99%

“…Our previous work utilized standard 12-lead ECG machines. In one trial [8], we simulated single-lead potassium measurement by using the lead with the greatest-amplitude T wave among leads V3–V6. Since the 12-lead ECG is impractical for home use, in the current study we assessed the feasibility of using a handheld smartphone with inexpensive, commercially available electrodes affixed to it to acquire single-lead ECG signals, of transmitting these signals to a remote server, and of digitally processing the signals to estimate potassium levels in a prospective cohort of dialysis patients.…”

Section: Introductionmentioning

confidence: 99%

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Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone

Yasin

Attia

Dillon

et al. 2017

Journal of Electrocardiology

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Objective We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. Patients and methods Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2 min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. Results The mean absolute error between the estimated potassium and blood potassium 0.38 ± 0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. Conclusions A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. Summary A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

\frac{T - italicright slope}{\sqrt{T - amp}}

to create a personalized potassium prediction model that was unique to each individual patient [8].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone

Yasin

Attia

Dillon

et al. 2017

Journal of Electrocardiology

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

Galloway¹,

Valys²,

Shreibati³

et al. 2019

JAMA Cardiol

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236

115

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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, AND PARTICIPANTSA 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 MEASURESArea 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. RESULTSOf 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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Artificial Intelligence‐Enabled ECG : Physiologic and Pathophysiologic Insights and Implications

Kashou

Adedinsewo

Siontis

et al. 2022

Comprehensive Physiology

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Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date-the electrocardiogram (ECG). The application of artificial intelligence-enabled ECG (AI-ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI-ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield.

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Novel Bloodless Potassium Determination Using a Signal‐Processed Single‐Lead ECG

Cited by 72 publications

References 30 publications

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone

Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone

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

Artificial Intelligence‐Enabled ECG : Physiologic and Pathophysiologic Insights and Implications

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