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Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram
Open accessAbstract
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.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
A deep-learning model using 2 or 4 ECG leads detected hyperkalemia in patients with chronic kidney disease, with 2-lead AUCs of 0.853 to 0.883 across three Mayo Clinic validation sets. At a 90% sensitivity operating point, specificity was 54.7% to 63.2% and negative predictive value was 99.0% to 99.6%.
Key findings
- For 2-lead ECG (leads I and II), the model achieved AUCs of 0.883 (95% CI, 0.873-0.893) in Minnesota, 0.860 (95% CI, 0.837-0.883) in Florida, and 0.853 (95% CI, 0.830-0.877) in Arizona; 4-lead AUCs were 0.880 to 0.901.
- At the high-sensitivity operating point, 2-lead sensitivity/specificity were 90.2%/63.2% in Minnesota, 91.3%/54.7% in Florida, and 88.9%/55.0% in Arizona, corresponding to negative predictive values of 99.0% to 99.6%.
- At the equal sensitivity/specificity operating point, 2-lead sensitivity/specificity were 79.9%/81.3% in Minnesota, 80.5%/75.2% in Florida, and 78.1%/75.3% in Arizona, with negative predictive values of 98.6% to 99.4%.
- False-positive results reached up to 42% at the high-sensitivity point, while false-negative results were less than 1% of all test results; among 58 false-negative patients reviewed, median blood potassium was 5.9 mEq/L and 52% had a subsequent potassium measurement below 5.5 mEq/L within 8 hours.
Methods
- Retrospective study using a deep convolutional neural network with 11 layers (10 convolutional plus a fully connected softmax layer), trained on 1,576,581 ECGs from 449,380 patients at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017.
- The model was trained with 2 leads (I and II) or 4 leads (I, II, V3, and V5) to detect serum potassium ≥5.5 mEq/L and validated on 61,965 ECG-potassium pairs from patients with stage 3 or greater CKD across Mayo Clinic sites in Minnesota, Florida, and Arizona; serum potassium was drawn within 4 hours after the ECG.
- Performance was assessed by AUC and by sensitivity and specificity at two operating points—equal sensitivity and specificity, and 90% sensitivity—with serum potassium as the reference standard; validation focused on ECGs with potassium ≤5.3 mEq/L or ≥5.7 mEq/L.
Limitations
- The paper states that data were retrospective and lacked clinical data such as medication use and medical history; the model was developed and validated using 12-lead ECG data in clinic/hospital settings, so prospective testing is needed for home or ambulatory ECG inputs and to determine whether it improves detection, care, and outcomes.
- The paper states that the DLM does not analyze renal function, that false-negative results may cause false reassurance, and that low specificity with up to 42% false-positive results may cause anxiety and inconvenience, with risk increasing over repeated testing; it also notes insufficient repeated measures to develop a personalized model.
- Evident from the text: validation excluded ECGs with potassium levels between 5.3 and 5.7 mEq/L to avoid misclassification near the threshold, and a sensitivity analysis including all potassium values decreased AUC by 0.02 to 0.04, suggesting performance may be lower in an unselected screening population.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the operating point with equal sensitivity and specificity, for ECG leads I and II, the DLM's sensitivity and specificity were 79.9% (95% CI, 77.6%-82.0%)and 81.3% (95% CI, 80.9%-81.6%) in Minnesota; 80.5% (95% CI, 75.4%-84.9%)and 75.2% (95% CI, 74.0%-76.3%) in Florida; and 78.1% (95% CI, 72.7%-82.9%)and 75.3% (95% CI, 74.1%-76.4%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“The algorithm's sensitivity and specificity for validation were 90.2% (95% CI, 88.4%-91.7%)and 63.2% (95% CI, 62.7%-63.6%) in Minnesota; 91.3% (95% CI, 87.4%-94.3%)and 54.7% (95% CI, 53.4%-56.0%) in Florida; and 88.9% (95% CI, 84.5%-92.4%)and 55.0% (95% CI, 53.7%-56.3%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“Given the less than 5% prevalence of hyperkalemia, these findings correspond to a negative predictive value of 98.6% to 99.4% across the validation data sets.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“Between 50% and 70% of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the operating point with equal sensitivity and specificity, for ECG leads I and II, the DLM's sensitivity and specificity were 79.9% (95% CI, 77.6%-82.0%)and 81.3% (95% CI, 80.9%-81.6%) in Minnesota; 80.5% (95% CI, 75.4%-84.9%)and 75.2% (95% CI, 74.0%-76.3%) in Florida; and 78.1% (95% CI, 72.7%-82.9%)and 75.3% (95% CI, 74.1%-76.4%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using the operating point with equal sensitivity and specificity, for ECG leads I and II, the DLM's sensitivity and specificity were 79.9% (95% CI, 77.6%-82.0%)and 81.3% (95% CI, 80.9%-81.6%) in Minnesota; 80.5% (95% CI, 75.4%-84.9%)and 75.2% (95% CI, 74.0%-76.3%) in Florida; and 78.1% (95% CI, 72.7%-82.9%)and 75.3% (95% CI, 74.1%-76.4%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“For detection of hyperkalemia using ECG leads I and II, the DLM achieved an AUC of 0.883 (95% CI, 0.873-0.893) in Minnesota; 0.860 (95% CI, 0.837-0.883) in Florida; and 0.853 (95% CI, 0.830-0.877) in Arizona (Figure 2).”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“The inclusion of all ECGs and serum potassium values decreased the AUC across all models by 0.02 to 0.04; with a higher prevalence of hyperkalemia, the positive predictive value increased by 3 to 5 percentage points.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“The algorithm's sensitivity and specificity for validation were 90.2% (95% CI, 88.4%-91.7%)and 63.2% (95% CI, 62.7%-63.6%) in Minnesota; 91.3% (95% CI, 87.4%-94.3%)and 54.7% (95% CI, 53.4%-56.0%) in Florida; and 88.9% (95% CI, 84.5%-92.4%)and 55.0% (95% CI, 53.7%-56.3%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The algorithm's sensitivity and specificity for validation were 90.2% (95% CI, 88.4%-91.7%)and 63.2% (95% CI, 62.7%-63.6%) in Minnesota; 91.3% (95% CI, 87.4%-94.3%)and 54.7% (95% CI, 53.4%-56.0%) in Florida; and 88.9% (95% CI, 84.5%-92.4%)and 55.0% (95% CI, 53.7%-56.3%) in Arizona.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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- Probably not a substantial claim. 20
- The quote only partly states this claim. 1
- It may state two separate claims as one. 16
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In the paper
“To try to understand how the DNN builds up its understanding of images for hyperkalemia over many layers, we performed feature visualization on the mean ECG beat [33][34][35] (eFigure 3 in the Supplement); sometimes, expected changes in QRS and T-wave morphology were seen.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [33]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [34]
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- Not matchedcites [35]
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The AUC was 0.02 to 0.03 higher using ECG leads I, II, V3, and V5.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Finally, the DLM is a screening test with low specificity, with upwards of 42% false-positive results”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fewer than 3% in Minnesota had hyperkalemia (n = 1282 of 50 099) compared with 4.6% (n = 270 of 5855) to 4.8% (n = 287 of 6011) in Florida and Arizona, respectively.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Strength:plain· not yet calibratedn = 270Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fewer than 3% in Minnesota had hyperkalemia (n = 1282 of 50 099) compared with 4.6% (n = 270 of 5855) to 4.8% (n = 287 of 6011) in Florida and Arizona, respectively.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Strength:plain· not yet calibratedn = 1,282Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fewer than 3% in Minnesota had hyperkalemia (n = 1282 of 50 099) compared with 4.6% (n = 270 of 5855) to 4.8% (n = 287 of 6011) in Florida and Arizona, respectively.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
Strength:plain· not yet calibratedn = 287Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hyperkalemia ECGs were more likely to be recorded in patients who were older and men and who had stage 3 or greater CKD.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Between 50% and 70% of patients in the validation data sets did not have hyperkalemia predicted by the DLM, with less than 1% of all test results being false-negative; on the other hand, up to 42% of all test results were false-positive.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Results
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In the paper
“H yperkalemia is potentially life threatening for the 35 million American adults with chronic kidney disease (CKD). 1”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Abstract
What the sources say
- Not matchedcites [1]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“However, guidelinedirected potassium monitoring 5,6 is severely underused: up to one-third of patients taking RAAS inhibitors receive no monitoring, and only 10% receive potassium monitoring before and after RAAS inhibitors are started.7,8”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Abstract
What the sources say
- Not matchedcites [7]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [8]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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In the paper
“A heightened fear of MRA-associated hyperkalemia and hyperkalemia-associated mortality 31 and the complex potassium monitoring requirements for MRA initiation and titration 6 are 2 potential reasons for underuse.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [31]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The DLM does not offer an analysis of renal function.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the diagnosis of hyperkalemia, particularly outside of the hospital or clinic, is challenging because patients are often asymptomatic, and guideline-directed blood potassium monitoring is severely underperformed.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other high-probability visualizations, QRS and T-wave features were not present because there are features the DNN extracts that surpass human visualization.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The sample size for each validation data set was determined to be 6133 ECGs and was based on the maximal sample size needs of the area under the receiving operating characteristic curve (AUC) (eMethods in the Supplement).”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Statistical Analysis
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In the paper
“Previous approaches have used standard regression models to estimate potassium and have highlighted the importance of T-wave width, T-wave amplitude, and descending T-wave slope as predictors of hyperkalemia.16,32”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [16]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [32]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“In patients with CKD, hyperkalemia is common and life threatening.23,24”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [23]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [24]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“an ECG-derived potassium level may be more germane to health and arrhythmia risk than the blood test.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, the patients who most likely require an assessment of renal function are those patients with possible hyperkalemia, for whom the envisioned screening test would require a confirmatory blood test that typically includes renal function in addition to potassium level.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Hyperkalemia is often present in the setting of acute CKD.
In the paper
“Hyperkalemia causes cardiotoxic effects and has been associated with a defined series of ECG abnormalities, including peaking of T waves, QRS prolongation, and PR shortening.9”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Abstract
What the sources say
- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“In patients with heart failure, MRAs reduce cardiovascular mortality, but only a small fraction (less than one-third of eligible patients) receive them at hospital discharge.30”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [30]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“which may cause anxiety and inconvenience for patients.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Although treatments for hyperkalemia are effective and readily available, 2 5 including the US Food and Drug Administration-approved potassium binders patiromer 26 and zirconium cyclosilicate, 27”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [25]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [26]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites [27]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A DLM that enables noninvasive potassium monitoring could assuage the fears of health care clinicians and enable more patients with CKD with heart failure to receive target doses of MRAs and other guideline-recommended medications, potentially improving care and outcomes.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“Owing to the effects of kidney dysfunction on potassium homeostasis, as well as the recommendation that these patients be treated with renin-angiotensin-aldosterone system (RAAS) inhibitors that preserve renal and cardiac function but also inhibit renal potassium secretion, the risk of hyperkalemia in CKD is substantially elevated.2”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Abstract
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To our knowledge, this is the first deep-learning approach to evaluation of potassium levels from the ECG.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The risk of falsepositive results will also increase over time with repeated testing.”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Limitations
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The 1-day odds of mortality are up to 13 times higher for patients with CKD with outpatient serum potassium levels of at least 6.0 mEq/L compared with patients without CKD with potassium levels of less than 5.5 mEq/L.3”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“On average, patients with CKD spend up to 9% of their time with serum potassium levels 5.5 mEq/L or greater. 2”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Discussion
What the sources say
- Not matchedcites [2]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“When present, hyperkalemia is often asymptomatic and associated with cardiac arrhythmias and death.3”
§ Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram > Abstract
What the sources say
- Not matchedcites [3]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The paper relies on this without support.
The serum potassium level measured within 4 hours after the ECG reflects the potassium level at the time of the ECG.
“All ECGs within 4 hours before a serum potassium draw were identified.16”
The paper relies on this without support.
The Gaussian process used to estimate potassium level at the time of ECG in the development data set accurately estimates the true potassium level.
“Gaussian process to estimate potassium level at time of ECG”
The paper relies on this without support.
Hyperkalemia is often present in the setting of acute CKD.
“Hyperkalemia is often present in the setting of acute CKD.”
The paper relies on this without support.
ECG-based tests are not susceptible to mechanical, temperature, contamination, or other potential errors associated with processing blood.
“The ECGbased tests are not susceptible to mechanical, temperature, contamination, or the other potential errors associated with processing blood.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“potassium models may prioritize urgent laboratory testing without replacing serum measurement [6,15].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [6]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Emerging evidence suggests that ECG waveforms contain latent signatures of ventricular dysfunction [4], structural heart disease [5], metabolic abnormalities [6], and broader physiologic characteristics such as biological age [7] and future mortality risk [8].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“This study expanded AI-ECG by suggesting that ECG waveforms could support noninvasive screening for a metabolic abnormality ordinarily diagnosed through laboratory testing.”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“The model was trained on more than 1.5 million ECGs from over 449,000 patients and, using only leads I and II, detected hyperkalemia with AUC values ranging from 0.853 to 0.883 across validation sites [6].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [6]
The cited source says what the claim attributes to it.
In the citing paper
“The reduced-lead hyperkalemia model discussed earlier demonstrated that clinically relevant metabolic information can be retained using only two or four ECG leads [6].”
From Deep Learning Applied to 12-Lead ECGs for Detection of Structural, Metabolic and Systemic Disease (2026) · cites this paper as [6]
In the citing paper
“Hyperkalaemia detection via wearable ECG is feasible but has high false-positive rates”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [82]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Reserved cohort is not 10% of patients
MinorThe stated 109 860 reserved patients are 13.9%, not 10%, of the 787 661 patients.
Reported 10 · recomputed 13.947625
“The remaining 10% of patients (n = 109 860 of 787 661) were reserved for future analysis.”
Development cohort is not 60% of patients
MinorThe stated 449 380 patients are 57.1%, not 60%, of the 787 661 patients.
Reported 60 · recomputed 57.052463
“with 60% of patients used for DLM development (n = 449 380 of 787 661; development dataset)”
Abstract reverses the hyperkalemia threshold
MinorThe abstract says the model detects potassium levels of 5.5 mEq/L or less, whereas the Methods define hyperkalemia as at least 5.5 mEq/L.
“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”
“We defined hyperkalemia as a serum potassium value of at least 5.5 mEq/L”
Arizona White percentage is inconsistent with its count
MinorThe table reports 5271 White patients among 5855 Arizona patients as 9.0%; the proportion is 90.0%.
Reported 9 · recomputed 90.025619
“| White | NA | 5077 (84.5) | 5271 (9.0) | NA |”
Florida Black percentage is inconsistent with its count
MinorThe table reports 650 Black patients among 6011 Florida patients as 1.8%; the proportion is 10.8%.
Reported 1.8 · recomputed 10.813509
“| Black | NA | 650 (1.8) | 193 (3.3) | NA |”
Florida and Arizona hyperkalemia rates are assigned to the wrong sites
MinorThe Results assign 4.6% to Florida and 4.8% to Arizona, respectively. The table assigns 4.8% to Florida and 4.6% to Arizona.
“4.6% (n = 270 of 5855) to 4.8% (n = 287 of 6011) in Florida and Arizona, respectively.”
“| Potassium ≥5.7 mEq/L, No. (%) g | 1282 (2.6) | 287 (4.8) | 270 (4.6) | NA |”
Citations
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- Erhan, 2009, Visualizing Higher-Layer Features of a Deep Network: Technical Report 1341
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Items 101–200 of 317
A deep learning-based system capable of detecting pneumothorax via electrocardiogram
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“Initially, artificial intelligence (AI) was trained to detect well-known ECG changes, such as arrhythmia, hyperkalemia, and hypokalemia, and it showed noninferior performance compared with experienced physicians [18–20].”
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“In light of these facts and the reported delays for the initiation of hyperkalemia therapy, technological advancements should be considered in high-risk patients, such as using finger-stick testing [78] and incorporating artificial intelligence into the ECGs [78].”
BackgroundArtificial Intelligence-Enabled ECG Algorithm for the Prediction of Coronary Artery Calcification
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“The application of a deep-learning model using ECG may enable screening for hyperkalaemia [30].”
MethodA comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)
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“Several deep-learning models were performed well to predict hyperkalemia using electrocardiography in CKD patients and at the emergency department [7, 8].”
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“However, as no ECG features are extracted, it may lead to reduced classification accuracy 24–26 .”
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Evaluating regression and probabilistic methods for ECG-based electrolyte prediction
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Self-citationApplying Artificial Intelligence to ECG Analysis
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Artificial intelligence to detect abnormal heart rhythm from scanned electrocardiogram tracings
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“This finding could potentially be extended to applications that continuously monitor one’s ECGs, thus serving as a surveillance system for patients at high risk of hyperkalemia and avoiding unnecessary blood tests.”
InfluentialWearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?
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“As already discussed, current smartwatches can provide a single-lead ECG trace; therefore, it is conceivable that this technology could be integrated into existing wearable technology.”
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“…for automated interpretation of ECGs is supported by recently published algorithms for prediction of various cardiac and non-cardiac conditions including left ventricular dysfunction, 23 atrial fi brillation, 24 hypertrophic cardiomyopathy, 25 hyperkalemia, 26 as well as sex and age 27 on ECGs.”
Self-citationLu, S.-C., Chen, G. H., Liu, A.-S., Sun, J., Gao, J.-W., Huang, C., Tsai, C., & Fu, L. (2025) · Journal of Medical Internet Research · doi:10.2196/67576 · Cited by 16
“Electrocardiogram (ECG) is an old tool in clinical medicine but has re-emerged for the prediction of low left ventricular ejection fraction [17], arrhythmia [18], dyskalemia [19], or even longer-term mortality [20].”
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“Recent studies in critical care have demonstrated the potential of digital biomarker signatures including the use of convolutional deep neural networks to process electrocardiogram waveforms to screen for hyperkalemia (39) and detect arrhythmias (40), and the use of continuous electroencephalography waveforms and deep…”
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“Hyperkalemia is common in CKD patients and is associated with fatal arrhythmias [26].”
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“Galloway et al [11] developed and validated a deep learning model to screen for hyperkalemia using ECG.”
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“16,17 18 – 21 In patient groups prone to hyperkalemia, this type of bloodless, point-of-care testing would be of signi fi cant bene fi t in guideline compliance and reduction of overall morbidity.”
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“3,4 As ECGs are embedded in clinical practice and AI is software-based, implementation is facilitated.”
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Digital health innovation in cardiology
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Self-citationClassification of Respiratory Conditions using Auscultation Sound
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“Repeatedly new highly predictive associations have been discovered in addition to and sometimes contradicting the human perception-derived features previously thought to carry unequivocal predictive potential [20].”
BackgroundBukhari, S. H. A., Sánchez, C., Srinivasan, S., Palmieri, F., Potse, M., Laguna, P., & Pueyo, E. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105304 · Cited by 9
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“Furthermore, attempts are being made for the extracardiac domain, such as anemia [17] or hyperkalemia [19].”
BackgroundAdvancements in Artificial Intelligence for ECG Signal Analysis and Arrhythmia Detection: A Review
Lichaee, F. K., Salari, A., Jalili, J., Dalivand, S. B., Rad, M. R., & Mojarad, M. (2024) · International Journal of Cardiovascular Practice · doi:10.5812/intjcardiovascpract-143437 · Cited by 9
Development of deep learning algorithm for detecting dyskalemia based on electrocardiogram
An, J. N., Park, M., Joo, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-71562-5 · Cited by 8
Cho, K. H., Ji, Y. H., Joo, S., Chang, M., Oh, S. K., Lim, Y., Ahn, J. H., Lee, S. H., Hyun, D. Y., Lee, N., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf049 · Cited by 10
Xue, J.-B., Xia, S., Wang, X., et al. (2023) · Infectious Diseases of Poverty · doi:10.1186/s40249-023-01060-9 · Cited by 8
“The number of bovines in schistosomiasis-endemic foci in China has decreased dramatically since the integrated strategy was implemented, with an emphasis on eliminating the source of S. japonicum infection [41].”
BackgroundDigital health innovation and artificial intelligence in cardiovascular care: a case-based review
Grant, J. K., Javaid, A., Carrick, R. T., Koester, M., Kassamali, A. A., Kim, C. H., Isakadze, N., Wu, K. C., Blaha, M. J., Whelton, S. P., et al. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00020-y · Cited by 9
“…or ST-segment, or uncover new patterns associated with clinical outcomes such as myocardial infarction 41 , aortic stenosis 42 , or hyperkalemia 43 , or portending high risk for some subsequent clinical outcome, e.g., development of atrial fi brillation 42 , death 44 , or ventricular…”
The Role of Artificial Intelligence in Arrhythmia Monitoring
Siontis, K. C., & Friedman, P. A. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2021.04.011 · Cited by 8
Self-citationModeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia
Park, B., Choi, B., Lee, H., Jang, J.-H., Roh, H. W., Kim, E. Y., Hong, C. H., Son, S. J., & Yoon, D. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.795171 · Cited by 8
“Recently, several studies have been successful in estimating clinical information that was not explicitly provided, such as in the prediction of valve disease or hyperkalemia using deep learning in electrocardiogram studies (Galloway et al., 2019; Kwon et al., 2020).”
BackgroundToluene toxicity presenting with hypokalemia, profound weakness and U waves in the electrocardiogram
Carrizales‐Sepúlveda, E. F., Vera‐Pineda, R., Jiménez‐Castillo, R. A., et al. (2019) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2019.158417 · Cited by 8
“Studies have demonstrated the usefulness of the electrocardiogram as a window for the detection of potassium derangements even without blood tests [5,6].”
BackgroundEngelstein, H., Ramon‐Gonen, R., Sabbag, A., Klang, E., Sudri, K., Cohen, M. S., & Barbash, I. M. (2025) · JMIR AI · doi:10.2196/74426 · Cited by 8
“…in predicting clinically significant abnormalities in electro-cardiograms (ECGs), such as potassium levels and adverse reactions to medications, while also extracting valuable insights beyond human capabilities, such as estimating sex, age, and identifying specific cardiac conditions [6-10].”
Yoon, H., Ahn, J. H., Kim, B. Y., Lee, H., Jo, W.-Y., Yoon, S., Park, H., & Lee, H. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003143 · Cited by 9
ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction
Tsai, P., Lee, S. S., Wei, C.-L. H., Hsu, Y., & Lin, C. S. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03278-z · Cited by 7
“…shown potential in extracting disease-relevant features from ECGs, enabling prediction of conditions such as vulnerable patients [9], dyskalemia [10–12], anemia [13], arrhythmia [14], atrial fibrillation [15, 16], long QT syndrome [17], heart dysfunction [18–20], and even all-cause mortality…”
Ramirez-Bautista, J. A., Chaparro-Cárdenas, S. L., Esmer, C., & Huerta-Ruelas, J. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105463 · Cited by 7
Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications
Islam, M. S., Kalmady, S. V., Hindle, A., Sandhu, R. K., Sun, W., Sepehrvand, N., Greiner, R., & Kaul, P. (2024) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2024.07.003 · Cited by 9
Aldosterone and Potassium in Heart Failure: Overcoming This Major Impediment in Clinical Practice
Khan, L. A., Jamil, A., Greene, S. J., Khan, M. S., & Butler, J. (2024) · Cardiac failure review · doi:10.15420/cfr.2024.09 · Cited by 7
Yeh, C., Tsai, T.-H., Chen, C., Chou, Y.-J., Mao, C., Su, T.-P., Yang, N., Lai, C., Chen, C.-T., Sytwu, H., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.032 · Cited by 9
“Electrocardiograms (ECG) provide a window that allows the detection of cardiac arrhythmia [1] and dysfunction [2 – 4]; they can also provide valuable diagnostic clues in relation to electrolyte derangement [5] and toxic drug reactions [6].”
Artificial Intelligence Applications to Improve Risk Prediction Tools in Electrophysiology
Kowlgi, G. N., Ezzeddine, F. M., & Kapa, S. (2020) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-020-00649-1 · Cited by 6
Nephrology Lagging Behind in Machine Learning Utilization
Cassol, C. A., & Sharma, S. G. (2021) · Kidney Medicine · doi:10.1016/j.xkme.2021.08.004 · Cited by 7
“identification of pathogen-specific immune fingerprints in peritoneal dialysis patients,(22) and even noninvasive high potassium detection through deep learning of electrocardiogram (ECG) patterns on a smartwatch.(23,24) Similar smartphone-based technology has also been used to detect atrial fibrillation, and it is cu…”
BackgroundBos, J. M., Liu, K., Attia, Z. I., Noseworthy, P. A., Friedman, P. A., & Ackerman, M. J. (2025) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2024.07.016 · Cited by 6
Self-citationLin, C., Lin, C., Chen, S., Tsai, S.-H., Sung, C., Chen, C.-C., Hsu, Y., Hung, Y.-J., & Lin, S.-H. (2026) · Nature Communications · doi:10.1038/s41467-025-66394-4 · Cited by 9
Post-discharge Care and Monitoring: What’s new, What’s Controversial
Ardon, A. E., Chadha, R., & George, J. (2024) · Current anesthesiology reports · doi:10.1007/s40140-024-00627-y · Cited by 6
“Deep learning models have also been successful at identifying patients with hyperkalemia solely by analyzing two-lead ECGs [4].”
Barros, A. J., Mesner, I. G., Nguyen, N. R., & Moorman, J. R. (2024) · Physiological Measurement · doi:10.1088/1361-6579/ad6746 · Cited by 6
“Beyond features immediately identifiable to human interpreters, deep neural networks have been shown to accurately identify hyperkalemia (Galloway et al 2019), reduced left ventricular ejection fraction (Attia et al 2019b), risk of incident atrial fibrillation (Raghunath et al 2021), and age (Attia…”
BackgroundExploring the Impact of Artificial Intelligence on Healthcare
Settia, N., Bhutani, M., & Saini, V. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-9641-4.ch004 · Cited by 6
Liu, Y., Chen, J., & Wang, M. (2025) · Electronics · doi:10.3390/electronics14122471 · Cited by 6
Soundoulounaki, S., Sylligardos, E., Akoumianaki, E., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13020347 · Cited by 6
A Scoping Review of Varying Mobile Electrocardiographic Devices
Dzikowicz, D. J. (2023) · Biological Research For Nursing · doi:10.1177/10998004231216923 · Cited by 6
“Just in this manuscript, researchers have tested mobile ECG devices to record potassium levels among patients undergoing hemodialysis, detecting of myocardial infarction, and recording of transient arrhythmias (Attia et al., 2016; Barbagelata et al., 2018; Galloway et al., 2019).”
BackgroundDevelopment and Validation of a Deep-Learning Model to Detect CRP Level from the Electrocardiogram
Jiang, J., Deng, H., Liao, H., Fang, X., Zhan, X., Wu, S., & Xue, Y. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.864747 · Cited by 7
“After training using more than 1.5 million standard 12 lead ECGs from nearly 450,000 patients, the model was effective in detecting serum potassium levels in patients (AUC 0.853 – 0.883) (Galloway et al., 2019).”
MethodInfluentialHyperkalaemia and Hypokalaemia Outpatient Management: A Survey of 500 French General Practitioners
Vuillaume, L. A., Rossignol, P., Lamiral, Z., et al. (2020) · ESC Heart Failure · doi:10.1002/ehf2.12834 · Cited by 6
Artificial Intelligence‐Enabled ECG : Physiologic and Pathophysiologic Insights and Implications
Kashou, A. H., Adedinsewo, D. A., Siontis, K. C., & Noseworthy, P. A. (2022) · Comprehensive physiology · doi:10.1002/cphy.c210001 · Cited by 10
“A deep learning model trained on over 1.5 million ECGs from the Mayo Clinic effectively detected hyperkalemia using only two leads from a standard 12-lead ECG (AUC 0.85-0.88) (23).”
InfluentialSelf-citationPredicting Dysglycemia in Patients with Diabetes Using Electrocardiogram
Song, H.-J., Han, J., Cho, S. P., Im, S. I., Kim, Y., & Park, J.-U. (2024) · Diagnostics · doi:10.3390/diagnostics14222489 · Cited by 5
Confronting the Ethical Issues with Artificial Intelligence Use in Nephrology
Weightman, A. L., Clayton, P. A., & Coghlan, S. (2025) · Kidney360 · doi:10.34067/kid.0000001074 · Cited by 5
“31 Compounding this problem, AI can use unconventional data and approaches to derive its predictions, potentially undermining its accuracy and increasing the risk of signi fi cant and unpredictable errors, including mistakes that would never be made by a trained human.”
Baykaner, T., Noseworthy, P. A., & Wan, E. Y. (2024) · Heart Rhythm · doi:10.1016/j.hrthm.2024.08.003 · Cited by 5
Self-citationWang, J., Wang, Y. P., Yao, C., & Huang, P.-J. (2020) · IEEE Access · doi:10.1109/access.2020.3001212 · Cited by 5
“INTRODUCTION Sodium, potassium, chlorine, calcium, phosphorus, magnesium and other electrolytes are mainly present in various tissues of the human body in the form of ions [1]–[5].”
BackgroundHaverkamp, W., Strodthoff, N., & Israel, C. W. (2022) · Herzschrittmachertherapie + Elektrophysiologie · doi:10.1007/s00399-022-00855-x · Cited by 5
“…Maschinenlernen und Kardiologen Bessere Performance; pulmonale Hypertension: AUC: 0,94; hypertrophe Kardiomyopathie: 0,86; Mitralklappenprolaps: 0,77 Galloway et al. 2019 [11] Erkennung einer SerumHyperkaliämie (Serumkalium ≥5,5mmol/l) EKGs und Serum-Kaliumwerte verfügbar Trainingsdatensatz:…”
InfluentialIncorporating structural abnormalities in equivalent dipole layer based ECG simulations
Boonstra, M., Oostendorp, T. F., Roudijk, R. W., Kloosterman, M., Asselbergs, F. W., Loh, P. K. K., & van Dam, P. M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.1089343 · Cited by 5
“, 2021) and electrolyte imbalance (Galloway et al., 2019; Pilia et al., 2020) can be identified from the apparently normal 12-lead ECG.”
BackgroundCausal Deep Neural Network-Based Model for First-Line Hypertension Management
Herzog, L., Ber, R. I., Kugler, Z., Rabi, Y., Brufman, I., Paz, Y. E., & López-Jiménez, F. A. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.10.001 · Cited by 6
“4 Recent use in the medical fi eld includes recognition of hyperkalemia on electro-cardiograms 5 and creation of a differential diagnosis for common skin conditions.”
Home monitoring of patients with chronic kidney disease
Josemans, S. H., Lindeboom, L., Gerritsen, K. G. F., Wieringa, F. P., Kooman, J. P., & Rotmans, J. I. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00858-6 · Cited by 5
“Non-invasive screening for hyper-kalaemia based on two-lead ECG recordings is possible by applying deep-learning algorithms 7 .”
A Review of Machine Learning and Deep Learning algorithms for Chronic Kidney Disease Prediction
Nagre, P., Verma, P., Kumar, P., & Gavankar, N. L. (2024) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaiei61867.2024.10842880 · Cited by 5
““Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram” [14] • Developed a DL model utilizing Mayo Clinic ECG data to identify hyperkalaemia in CKD patients.”
Park, H., Kwon, O., Shim, J., Kim, D., Park, J., Kim, Y.-G., Yu, H. T., Kim, T., Uhm, J., Choi, J., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf054 · Cited by 8
Nedios, S., Iliodromitis, K. E., Kowalewski, C., Bollmann, A., Hindricks, G., Dagres, N., & Bogossian, H. (2022) · Herzschrittmachertherapie + Elektrophysiologie · doi:10.1007/s00399-022-00837-z · Cited by 6
A Novel Deep Learning Based Framework for Cardiac Arrest Prediction
Fatima, N., Irtaza, A., & Ali, R. (2023) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai57502.2023.10089604 · Cited by 5
“The researchers in Galloway et al. [16] presented a DNN based approach that achieved 90% accuracy on Mayo Clinic Database.”
MethodComparison of quantifiable electrocardiographic changes associated with severe hyperkalemia
Tsai, C., Patel, H. R., Horbal, P. J., et al. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.131257 · Cited by 5
Chen, C., Lin, C. S., Lee, D.-J., Lin, C., Chen, S., Sung, C., Hsu, Y., & Lin, S.-H. (2025) · Clinical Kidney Journal · doi:10.1093/ckj/sfaf092 · Cited by 4
“With the advent of deep learning models ( DLM) , artificial intelligence ( AI) -enabled ECG systems ( AI-ECG) have been successfully developed and validated [15 –21 ].”
Desroche, L., Trimaille, A., Garban, T., & Bouleti, C. (2025) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2025.07.012 · Cited by 5
Can we trust ECG for diagnosing hyperkalemia? A challenging question for clinicians and bioengineers
Regolisti, G., Rossi, G. M., & Genovesi, S. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.131380 · Cited by 4
Kim, J. H., Kim, J. H., Jung, J. Y., et al. (2024) · Yonsei Medical Journal · doi:10.3349/ymj.2024.0148 · Cited by 4
The Role of Artificial Intelligence in Cardiology
Belenkov, Y. N., Kozhevnikova, M. V., Khabarova, N. V., Ilgisonis, I. S., & Korobkova, E. O. (2025) · Kardiologiia · doi:10.18087/cardio.2025.2.n2879 · Cited by 5
Ferguson, C., Chen, Y., Davidson, P. M., & Wynne, R. M. (2025) · Contemporary Nurse · doi:10.1080/10376178.2025.2566664 · Cited by 4
“In 2020, Galloway and colleagues, developed and validated a deep-learning model to screen for hyperkalaemia from the Apple Watch wristband sensor ECG, claiming to detect potassium levels without the need to draw or analyse blood (Galloway et al., 2019).”
Intelligent Detection of Hypokalemia Based on 12-Lead ECG Using Two-stream Deep Learning Model
Wang, Y., Zhong, G., Wang, Y., Zhu, Q., Xie, J., Deng, X., Wang, A., & Yang, C. (2022) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei56279.2022.9980196 · Cited by 4
“This study still has some limitations and looks forward to further improvement.”
BackgroundDigital health utilities in acute kidney injury management
KASHANI, K. B., & Koyner, J. L. (2023) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000001105 · Cited by 4
Artificial intelligence to detect noise events in remote monitoring data
Nishii, N., Baba, K., Morooka, K., Shirae, H., Mizuno, T., Masuda, T., Ueoka, A., Asada, S., Miyamoto, M., Ejiri, K., et al. (2024) · Journal of Arrhythmia · doi:10.1002/joa3.13037 · Cited by 4
Metadata
- Authors
- Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., Ackerman, M. J., Noseworthy, P. A., Dillon, J. J., & Friedman, P. A.
- Affiliations
- AliveCor Inc, Mountain View, California
- Mayo Clinic (US)
- Corresponding author
- Friedman, Paul A.
- ORCID
- Venue
- JAMA Cardiology
- Publisher
- American Medical Association (AMA)
- Published
- 2019-05-01
- Volume
- 4
- Issue
- 5
- Pages
- 428
- ISSN
- 2380-6583 (ISSN-L)
- PMC
- PMC6537816
- OpenAlex
- W2928542791
- Cited by
- 318
- References
- 37
- Citations by year
- 2026: 23 · 2025: 45 · 2024: 53 · 2023: 53 · 2022: 54 · 2021: 45 · 2020: 34 · 2019: 10
- FWCI
- 23.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Nephrology › Potassium and Related Disorders
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- hyperkalemia, electrocardiogram, deep convolutional neural networks, chronic kidney disease, serum potassium, non-invasive screening, arrhythmia risk, model validation, sensitivity and specificity, clinical decision support, arrhythmia prevention
- MeSH terms
- Deep Learning, Aged, Aged, 80 and over, Algorithms, Arrhythmias, Cardiac, Artificial Intelligence, Electrocardiography, Female, Humans, Hyperkalemia, Male, Mass Screening, Middle Aged, Retrospective Studies, Sensitivity and Specificity, Prevalence, Neural Networks, Computer, Renal Insufficiency, Chronic
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