David M Harmon, Kan Liu, Jennifer Dugan, Jacob C Jentzer, Zachi I Attia, Paul A Friedman, John J Dillon
{"title":"人工智能增强型心电图对高危情况下无创检测高钾血症的验证。","authors":"David M Harmon, Kan Liu, Jennifer Dugan, Jacob C Jentzer, Zachi I Attia, Paul A Friedman, John J Dillon","doi":"10.2215/CJN.0000000000000483","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.</p><p><strong>Methods: </strong>An emergency department (ED) cohort (February-August 2021) and a mixed intensive care unit (ICU) cohort (August 2017-February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within four hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads I and II of the 12 lead ECGs to screen for hyperkalemia (potassium > 6.0 mEq/L).</p><p><strong>Results: </strong>The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-ECG to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of, 80%, 80%, 3%, 99.8% and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.83, 86%, 60%, 15%, 98% and 2.2, respectively, in the ED cohort. The ICU cohort (N=2,636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 82%, 82%, 14%, 99% and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.85, 88%, 67%, 29%, 97% and 2.7, respectively in the ICU cohort.</p><p><strong>Conclusion: </strong>The AI-ECG algorithm demonstrated a high negative predictive value, suggesting that it is useful for ruling out hyperkalemia, but a low positive predictive value, suggesting that it is insufficient for treating hyperkalemia.</p>","PeriodicalId":50681,"journal":{"name":"Clinical Journal of the American Society of Nephrology","volume":null,"pages":null},"PeriodicalIF":8.5000,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validation of Non-invasive Detection of Hyperkalemia by Artificial Intelligence Enhanced Electrocardiography in High Acuity Settings.\",\"authors\":\"David M Harmon, Kan Liu, Jennifer Dugan, Jacob C Jentzer, Zachi I Attia, Paul A Friedman, John J Dillon\",\"doi\":\"10.2215/CJN.0000000000000483\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.</p><p><strong>Methods: </strong>An emergency department (ED) cohort (February-August 2021) and a mixed intensive care unit (ICU) cohort (August 2017-February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within four hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads I and II of the 12 lead ECGs to screen for hyperkalemia (potassium > 6.0 mEq/L).</p><p><strong>Results: </strong>The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-ECG to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of, 80%, 80%, 3%, 99.8% and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.83, 86%, 60%, 15%, 98% and 2.2, respectively, in the ED cohort. The ICU cohort (N=2,636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 82%, 82%, 14%, 99% and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.85, 88%, 67%, 29%, 97% and 2.7, respectively in the ICU cohort.</p><p><strong>Conclusion: </strong>The AI-ECG algorithm demonstrated a high negative predictive value, suggesting that it is useful for ruling out hyperkalemia, but a low positive predictive value, suggesting that it is insufficient for treating hyperkalemia.</p>\",\"PeriodicalId\":50681,\"journal\":{\"name\":\"Clinical Journal of the American Society of Nephrology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.5000,\"publicationDate\":\"2024-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Journal of the American Society of Nephrology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2215/CJN.0000000000000483\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Journal of the American Society of Nephrology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2215/CJN.0000000000000483","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
Validation of Non-invasive Detection of Hyperkalemia by Artificial Intelligence Enhanced Electrocardiography in High Acuity Settings.
Background: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings.
Methods: An emergency department (ED) cohort (February-August 2021) and a mixed intensive care unit (ICU) cohort (August 2017-February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within four hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads I and II of the 12 lead ECGs to screen for hyperkalemia (potassium > 6.0 mEq/L).
Results: The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-ECG to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of, 80%, 80%, 3%, 99.8% and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.83, 86%, 60%, 15%, 98% and 2.2, respectively, in the ED cohort. The ICU cohort (N=2,636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 82%, 82%, 14%, 99% and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, positive predictive value, negative predictive value and positive likelihood ratio of 0.85, 88%, 67%, 29%, 97% and 2.7, respectively in the ICU cohort.
Conclusion: The AI-ECG algorithm demonstrated a high negative predictive value, suggesting that it is useful for ruling out hyperkalemia, but a low positive predictive value, suggesting that it is insufficient for treating hyperkalemia.
期刊介绍：
The Clinical Journal of the American Society of Nephrology strives to establish itself as the foremost authority in communicating and influencing advances in clinical nephrology by (1) swiftly and effectively disseminating pivotal developments in clinical and translational research in nephrology, encompassing innovations in research methods and care delivery; (2) providing context for these advances in relation to future research directions and patient care; and (3) becoming a key voice on issues with potential implications for the clinical practice of nephrology, particularly within the United States. Original manuscript topics cover a range of areas, including Acid/Base and Electrolyte Disorders, Acute Kidney Injury and ICU Nephrology, Chronic Kidney Disease, Clinical Nephrology, Cystic Kidney Disease, Diabetes and the Kidney, Genetics, Geriatric and Palliative Nephrology, Glomerular and Tubulointerstitial Diseases, Hypertension, Maintenance Dialysis, Mineral Metabolism, Nephrolithiasis, and Transplantation.
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