Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients
Abstract. Background:. A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer
2021-10-01
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| Series: | Chinese Medical Journal |
| Online Access: | http://journals.lww.com/10.1097/CM9.0000000000001650 |
| _version_ | 1818726750084923392 |
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| author | Chen-Xi Wang Yi-Chu Zhang Qi-Lin Kong Zu-Xiang Wu Ping-Ping Yang Cai-Hua Zhu Shou-Lin Chen Tao Wu Qing-Hua Wu Qi Chen Peng Lyu |
| author_facet | Chen-Xi Wang Yi-Chu Zhang Qi-Lin Kong Zu-Xiang Wu Ping-Ping Yang Cai-Hua Zhu Shou-Lin Chen Tao Wu Qing-Hua Wu Qi Chen Peng Lyu |
| author_sort | Chen-Xi Wang |
| collection | DOAJ |
| description | Abstract. Background:. A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.
Methods:. We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1–6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.
Results:. We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1–6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.
Conclusions:. In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia. |
| first_indexed | 2024-12-17T22:03:10Z |
| format | Article |
| id | doaj.art-3483102adc6b437189cc4dc72b43bed6 |
| institution | Directory Open Access Journal |
| issn | 0366-6999 2542-5641 |
| language | English |
| last_indexed | 2024-12-17T22:03:10Z |
| publishDate | 2021-10-01 |
| publisher | Wolters Kluwer |
| record_format | Article |
| series | Chinese Medical Journal |
| spelling | doaj.art-3483102adc6b437189cc4dc72b43bed62022-12-21T21:30:55ZengWolters KluwerChinese Medical Journal0366-69992542-56412021-10-01134192333233910.1097/CM9.0000000000001650202110050-00011Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patientsChen-Xi WangYi-Chu ZhangQi-Lin KongZu-Xiang WuPing-Ping YangCai-Hua ZhuShou-Lin ChenTao WuQing-Hua WuQi ChenPeng LyuAbstract. Background:. A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients. Methods:. We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1–6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period. Results:. We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1–6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77–0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75–0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%. Conclusions:. In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.http://journals.lww.com/10.1097/CM9.0000000000001650 |
| spellingShingle | Chen-Xi Wang Yi-Chu Zhang Qi-Lin Kong Zu-Xiang Wu Ping-Ping Yang Cai-Hua Zhu Shou-Lin Chen Tao Wu Qing-Hua Wu Qi Chen Peng Lyu Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients Chinese Medical Journal |
| title | Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| title_full | Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| title_fullStr | Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| title_full_unstemmed | Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| title_short | Development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| title_sort | development and validation of a deep learning model to screen hypokalemia from electrocardiogram in emergency patients |
| url | http://journals.lww.com/10.1097/CM9.0000000000001650 |
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