POINT-OF-CARE POTASSIUM MEASUREMENT VS ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY FOR HYPERKALEMIA DETECTION

被引:0
|
作者
Lin, Chin [1 ]
Chen, Chien-Chou [2 ]
Lin, Chin-Sheng [2 ]
Shang, Hung-Sheng [3 ]
Lee, Chia-Cheng [4 ,5 ]
Chau, Tom [6 ]
Lin, Shih-Hua [2 ]
机构
[1] Natl Def Med Ctr, Sch Med, Taipei, Taiwan
[2] Triserv Gen Hosp, Natl Def Med Ctr, Dept Med, Div Nephrol, Taipei, Taiwan
[3] Triserv Gen Hosp, Natl Def Med Ctr, Dept Pathol, Div Clin Pathol, Taipei, Taiwan
[4] Triserv Gen Hosp, Natl Def Med Ctr, Dept Med Informat, Taipei, Taiwan
[5] Triserv Gen Hosp, Dept Surg, Natl Def Med Ctr, Div Colorectal Surg, Taipei, Taiwan
[6] Providence St Vincent Med Ctr, Dept Med, Portland, OR USA
关键词
KIDNEY-DISEASE;
D O I
10.4037/ajcc2025597
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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. ( American Journal of Critical Care. 2025;34:41-51)
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Artificial Intelligence-Enabled Electrocardiography Detects Hypoalbuminemia and Identifies the Mechanism of Hepatorenal and Cardiovascular Events
    Lee, Yung-Tsai
    Lin, Chin-Sheng
    Fang, Wen-Hui
    Lee, Chia-Cheng
    Ho, Ching-Liang
    Wang, Chih-Hung
    Tsai, Dung-Jang
    Lin, Chin
    FRONTIERS IN CARDIOVASCULAR MEDICINE, 2022, 9
  • [22] Association Between Social Connection and Biological Age as Determined by Artificial Intelligence-Enabled Electrocardiography
    Rajai, Nazanin
    Medina-Inojosa, Jose R.
    Sheffeh, Mohammad Ali
    Suarez, Abraham Baez
    Nyman, Mark A.
    Attia, Zachi
    Lerman, Lilach O.
    Medina-Inojosa, Betsy
    Lopez-Jimenez, Francisco
    Lerman, Amir
    CIRCULATION, 2022, 146
  • [23] ASSOCIATION BETWEEN FAMILIAL HYPERCHOLESTEROLEMIA AND BIOLOGICAL AGE AS DETERMINED BY ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAPHY
    Estrada-Magana, Andres
    Sapir, Orly P. Ran
    Medina-Inojosa, Betsy J.
    Sheffeh, Mohammad Ali
    Ortega-Aviles, Laura M.
    Medina-Inojosa, Jose
    Kopecky, Stephen L.
    Lerman, Amir
    Lopez-Jimenez, Francisco
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2024, 83 (13) : 2082 - 2082
  • [24] DETECTION OF AORTIC STENOSIS USING AN ARTIFICIAL INTELLIGENCE-ENABLED ELECTROCARDIOGRAM
    Shelly, Michal
    Attia, Zachi Itzhak
    Ko, Wei-Yin
    Ito, Saki
    Essayagh, Benjamin
    Michelena, Hector I.
    Carter, Rickey
    Sarano, Maurice
    Friedman, Paul
    Oh, Jae K.
    JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2020, 75 (11) : 2115 - 2115
  • [25] Artificial Intelligence in Point-of-Care Testing
    Khan, Adil I.
    Khan, Mazeeya
    Khan, Raheeb
    ANNALS OF LABORATORY MEDICINE, 2023, 43 (05) : 401 - 407
  • [26] Artificial Intelligence in Point-of-care Ultrasound
    Wistrom, Riley
    Khait, Luda
    Nelson, Grant
    CURRENT EMERGENCY AND HOSPITAL MEDICINE REPORTS, 2024, 12 (03): : 89 - 94
  • [27] Artificial Intelligence-Enabled Electrocardiography Helps Identify Severe Dyscalcemia and Provide Additional Prognostic Value
    Lin, Shih-Hua P.
    Sung, Chih-Chien
    JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2022, 33 (11): : 479 - 479
  • [28] POINT-OF-CARE VS. CENTRAL LABORATORY TESTING FOR DETECTION AND TREATMENT OF HYPERKALEMIA IN THE ED
    Cook, Ryan
    Marler, Jacob
    Finch, Christopher
    Broyles, Joyce
    CRITICAL CARE MEDICINE, 2021, 49 (01) : 570 - 570
  • [29] Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes
    Wen-Yu Lin
    Chin Lin
    Wen-Cheng Liu
    Wei-Ting Liu
    Chiao-Hsiang Chang
    Hung-Yi Chen
    Chiao-Chin Lee
    Yu-Cheng Chen
    Chen-Shu Wu
    Chia-Cheng Lee
    Chih-Hung Wang
    Chun-Cheng Liao
    Chin-Sheng Lin
    Journal of Medical Systems, 49 (1)
  • [30] Realizing the Promise of Artificial Intelligence-Enabled Cardio-Oncology Care
    Ross, Elsie G.
    Hess, Paul L.
    CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES, 2025, 18 (01):