Evidence map›Paper›PMID 26811164›Full record

ArticleJournal of the American Heart Association2016

Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG.

Zachi I Attia, Christopher V DeSimone, John J Dillon, Yehu Sapir, Virend K Somers, Jennifer L Dugan, Charles J Bruce, Michael J Ackerman, Samuel J Asirvatham, Bryan L Striemer and 8 more

Registry-linked trialAbstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the American Heart Association, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07493798 (Serum Potassium Prediction Using Machine Learning and Single-lead ECG), which is not on this map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07493798 withdrawnnot on this mapstarted 2021, after this paper: background citation

Serum Potassium Prediction Using Machine Learning and Single-lead ECG

TypeobservationalSponsorBrigham and Women's HospitalRan2021 to 2021Enrolled0ConditionsInfection, Heart Failure, Chronic Obstructive Pulmonary Disease, AsthmaArmsPotassium estimation algorithm
3 · Its place in the literature

Who cites it

37 citing papers in PubMed.

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  17. Hyperkalemia and Electrocardiogram Manifestations in End-Stage Renal Disease.International journal of environmental research and public health · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Zachi I AttiaDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.) Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (Z.I.A., Y.S., D.S., A.B.G.).
Christopher V DeSimoneDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
John J DillonNephrology and Hypertension, Mayo Clinic, Rochester, MN (J.J.D.).
Yehu SapirElectrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (Z.I.A., Y.S., D.S., A.B.G.).
Virend K SomersDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Jennifer L DuganDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Charles J BruceDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Michael J AckermanDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Samuel J AsirvathamDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Bryan L StriemerCenter for Advanced Imaging, Mayo Clinic, Rochester, MN (B.L.S.).
Jan BukartykDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).
Christopher G ScottBiomedical Statistics and Informatics, Mayo Clinic, Rochester, MN (C.G.S.).
Kevin E BennetDepartment of Engineering, Mayo Clinic, Rochester, MN (K.E.B.).
Dorothy J LadewigMayo Clinic Ventures, Mayo Clinic, Rochester, MN (D.J.L., E.J.G.).
Emily J GillesMayo Clinic Ventures, Mayo Clinic, Rochester, MN (D.J.L., E.J.G.).
Dan SadotElectrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (Z.I.A., Y.S., D.S., A.B.G.).
Amir B GevaElectrical and Computer Engineering, Ben-Gurion University of the Negev, Beer Sheva, Israel (Z.I.A., Y.S., D.S., A.B.G.).
Paul A FriedmanDivision of Cardiovascular Diseases, Mayo Clinic, Rochester, MN (Z.I.A., C.V.D.S., V.K.S., J.L.D., C.J.B., M.J.A., S.J.A., J.B., P.A.F.).

Funding

CARDIOVASOLOGYT32HL007111 · NHLBI · MAYO CLINIC ROCHESTER · PI Barry A. Borlaug · 1985 to 2026
$8.7M
NHLBI NIH HHS T32 HL007111
6 · The paper itself

Abstract

backgroundHyper- and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood-free method for tracking potassium would be an important clinical advance. METHODS AND

resultsTwo groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high-resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single-channel ECG. In addition, by analyzing the entire development group's first-visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium).

conclusionsThe signal-processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost-effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.

Indexed as

Renal DialysisSignal Processing, Computer-AssistedAdultAgedAlgorithmsBiomarkersElectrocardiographyFemaleHumansHyperkalemiaHypokalemiaMaleMiddle AgedPotassiumPredictive Value of TestsProspective StudiesBiomarkersPotassiumelectrophysiologypotassiumwaves

Identifiers

PMID26811164
PMCPMC4859394

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.