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一种利用心电图预测多次就诊患者电解质紊乱的动态深度学习算法的开发与验证

Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits.

作者信息

Lou Yu-Sheng, Lin Chin-Sheng, Fang Wen-Hui, Lee Chia-Cheng, Wang Chih-Hung, Lin Chin

机构信息

Graduate Institutes of Life Sciences, National Defense Medical Center, No.161, Min-Chun E. Rd., Sec. 6, Neihu, Taipei 114, Taiwan, Republic of China.

School of Public Health, National Defense Medical Center, No. 161, Min-Chun E. Rd., Section 6, Neihu, Taipei 114, Taiwan, Republic of China.

出版信息

Eur Heart J Digit Health. 2022 Nov 22;4(1):22-32. doi: 10.1093/ehjdh/ztac072. eCollection 2023 Jan.

DOI:10.1093/ehjdh/ztac072

PMID:36743876

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9890087/

Abstract

AIMS

Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.

METHODS AND RESULTS

We retrospectively collected 168 450 ECGs with corresponding serum potassium (K) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits.

CONCLUSION

Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.

摘要

目的

深度学习模型（DLMs）在心电图（ECG）分析中已显示出优越性，并已应用于诊断电解质紊乱。然而，尚无研究探讨在连续随访场景中基于DLM的心电图的性能。因此，我们提出了一种基于DLM的心电图动态修订方法，以使用个人预先标注的心电图来提高多次就诊患者的诊断准确性。

方法与结果

我们回顾性收集了103091例患者的168450份心电图及相应的血清钾（K）水平作为开发样本。在内部/外部验证集中，有相应K值的心电图数量分别为来自13555/20058例患者的37246/47604份。我们的动态修订方法在诊断多次就诊患者的低钾血症[受试者操作特征曲线下面积（AUC）=0.730/0.720-0.788/0.778]和高钾血症（AUC=0.884/0.888-0.915/0.908）方面比传统直接预测方法表现更好。