Point-of-Care Potassium Measurement vs Artificial Intelligence–Enabled Electrocardiography for Hyperkalemia Detection.
Background: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence– enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared. Objective: To determine the accuracy of POC and ECG potassium measurements for hyperkalem...
| Publicado en: | American Journal of Critical Care Vol. 34; no. 1; pp. 41 - 52 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
American Association of Critical-Care Nurses
Jan2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181973308&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181973308 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10623264 44L jtl: American Journal of Critical Care issn: 10623264 maglogo: N pubinfo: dt: Jan2025 vid: 34 iid: 1 pid: 2559 pub: American Association of Critical-Care Nurses place: Alisa Veijo, California artinfo: ui: 181973308 181973308 181973308 10.4037/ajcc2025597 181973308 ppf: 41 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Point-of-Care Potassium Measurement vs Artificial Intelligence–Enabled Electrocardiography for Hyperkalemia Detection. aug: au: Lin, Chin Chen, Chien-Chou Lin, Chin-Sheng Shang, Hung-Sheng Lee, Chia-Cheng Chau, Tom Lin, Shih-Hua affil: an associate professor, School of Medicine, National Defense Medical Center, Taipei, Taiwan, Republic of China sug: subj: Hyperkalemia Diagnosis Electrocardiography Methods Artificial Intelligence Utilization Potassium Blood Point-of-Care Testing Evaluation Critically Ill Patients Human Funding Source Male Female Middle Age Aged Aged, 80 and Over Retrospective Design Record Review Prospective Studies Academic Medical Centers Intensive Care Units Blood Gas Analysis Deep Learning Descriptive Statistics ROC Curve Sensitivity and Specificity Chi Square Test T-Tests Wilcoxon Rank Sum Test Data Analysis Software Pearson's Correlation Coefficient Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Background: Hyperkalemia can be detected by point-of-care (POC) blood testing and by artificial intelligence– enabled electrocardiography (ECG). These 2 methods of detecting hyperkalemia have not been compared. Objective: To determine the accuracy of POC and ECG potassium measurements for hyperkalemia detection in patients with critical illness. Methods: This retrospective study involved intensive care patients in an academic medical center from October 2020 to September 2021. Patients who had 12-lead ECG, POC potassium measurement, and central laboratory potassium measurement within 1 hour were included. The POC potassium measurements were obtained from arterial blood gas analysis; ECG potassium measurements were calculated by a previously developed deep learning model. Hyperkalemia was defined as a central laboratory potassium measurement of 5.5 mEq/L or greater. Results: Fifteen patients with hyperkalemia and 252 patients without hyperkalemia were included. The POC and ECG potassium measurements were available about 35 minutes earlier than central laboratory results. Correlation with central laboratory potassium measurement was better for POC testing than for ECG (mean absolute errors of 0.211 mEq/L and 0.684 mEq/L, respectively). For POC potassium measurement, area under the receiver operating characteristic curve (AUC) to detect hyperkalemia was 0.933, sensitivity was 73.3%, and specificity was 98.4%. For ECG potassium measurement, AUC was 0.884, sensitivity was 93.3%, and specificity was 63.5%. Conclusions: The ECG potassium measurement, with its high sensitivity and coverage rate, may be used initially and followed by POC potassium measurement for rapid detection of life-threatening hyperkalemia. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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