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Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians
BACKGROUND: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that ac...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Korean Academy of Medical Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659922/ https://www.ncbi.nlm.nih.gov/pubmed/37987103 http://dx.doi.org/10.3346/jkms.2023.38.e322 |
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author | Kim, Donghoon Jeong, Joo Kim, Joonghee Cho, Youngjin Park, Inwon Lee, Sang-Min Oh, Young Taeck Baek, Sumin Kang, Dongin Lee, Eunkyoung Jeong, Bumi |
author_facet | Kim, Donghoon Jeong, Joo Kim, Joonghee Cho, Youngjin Park, Inwon Lee, Sang-Min Oh, Young Taeck Baek, Sumin Kang, Dongin Lee, Eunkyoung Jeong, Bumi |
author_sort | Kim, Donghoon |
collection | PubMed |
description | BACKGROUND: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. METHODS: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). RESULTS: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both). CONCLUSION: Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED. |
format | Online Article Text |
id | pubmed-10659922 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Korean Academy of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-106599222023-11-20 Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians Kim, Donghoon Jeong, Joo Kim, Joonghee Cho, Youngjin Park, Inwon Lee, Sang-Min Oh, Young Taeck Baek, Sumin Kang, Dongin Lee, Eunkyoung Jeong, Bumi J Korean Med Sci Original Article BACKGROUND: Hyperkalemia is a potentially fatal condition that mandates rapid identification in emergency departments (EDs). Although a 12-lead electrocardiogram (ECG) can indicate hyperkalemia, subtle changes in the ECG often pose detection challenges. An artificial intelligence application that accurately assesses hyperkalemia risk from ECGs could revolutionize patient screening and treatment. We aimed to evaluate the efficacy and reliability of a smartphone application, which utilizes camera-captured ECG images, in quantifying hyperkalemia risk compared to human experts. METHODS: We performed a retrospective analysis of ED hyperkalemic patients (serum potassium ≥ 6 mmol/L) and their age- and sex-matched non-hyperkalemic controls. The application was tested by five users and its performance was compared to five board-certified emergency physicians (EPs). RESULTS: Our study included 125 patients. The area under the curve (AUC)-receiver operating characteristic of the application’s output was nearly identical among the users, ranging from 0.898 to 0.904 (median: 0.902), indicating almost perfect interrater agreement (Fleiss’ kappa 0.948). The application demonstrated high sensitivity (0.797), specificity (0.934), negative predictive value (NPV) (0.815), and positive predictive value (PPV) (0.927). In contrast, the EPs showed moderate interrater agreement (Fleiss’ kappa 0.551), and their consensus score had a significantly lower AUC of 0.662. The physicians’ consensus demonstrated a sensitivity of 0.203, specificity of 0.934, NPV of 0.527, and PPV of 0.765. Notably, this performance difference remained significant regardless of patients’ sex and age (P < 0.001 for both). CONCLUSION: Our findings suggest that a smartphone application can accurately and reliably quantify hyperkalemia risk using initial ECGs in the ED. The Korean Academy of Medical Sciences 2023-10-24 /pmc/articles/PMC10659922/ /pubmed/37987103 http://dx.doi.org/10.3346/jkms.2023.38.e322 Text en © 2023 The Korean Academy of Medical Sciences. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Kim, Donghoon Jeong, Joo Kim, Joonghee Cho, Youngjin Park, Inwon Lee, Sang-Min Oh, Young Taeck Baek, Sumin Kang, Dongin Lee, Eunkyoung Jeong, Bumi Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title | Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title_full | Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title_fullStr | Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title_full_unstemmed | Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title_short | Hyperkalemia Detection in Emergency Departments Using Initial ECGs: A Smartphone AI ECG Analyzer vs. Board-Certified Physicians |
title_sort | hyperkalemia detection in emergency departments using initial ecgs: a smartphone ai ecg analyzer vs. board-certified physicians |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659922/ https://www.ncbi.nlm.nih.gov/pubmed/37987103 http://dx.doi.org/10.3346/jkms.2023.38.e322 |
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