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Deep-learning model for screening sepsis using electrocardiography.
Kwon, Joon-Myoung; Lee, Ye Rang; Jung, Min-Seung; Lee, Yoon-Ji; Jo, Yong-Yeon; Kang, Da-Young; Lee, Soo Youn; Cho, Yong-Hyeon; Shin, Jae-Hyun; Ban, Jang-Hyeon; Kim, Kyung-Hee.
  • Kwon JM; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea. kwonjm@sejongh.co.kr.
  • Lee YR; Medical Research Team, Medical AI, Co., Seoul, Republic of Korea. kwonjm@sejongh.co.kr.
  • Jung MS; Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, 20, Gyeyangmunhwa-ro, Gyeyang-gu, Incheon, Republic of Korea. kwonjm@sejongh.co.kr.
  • Lee YJ; Medical R&D Center, Body Friend, Co., Seoul, Republic of Korea. kwonjm@sejongh.co.kr.
  • Jo YY; Medical Research Team, Medical AI, Co., Seoul, Republic of Korea.
  • Kang DY; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea.
  • Lee SY; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea.
  • Cho YH; Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, Republic of Korea.
  • Shin JH; Medical Research Team, Medical AI, Co., Seoul, Republic of Korea.
  • Ban JH; Medical Research Team, Medical AI, Co., Seoul, Republic of Korea.
  • Kim KH; Division of Cardiology Cardiovascular Center, Mediplex Sejong Hospital, Incheon, Republic of Korea.
Scand J Trauma Resusc Emerg Med ; 29(1): 145, 2021 Oct 03.
Article in English | MEDLINE | ID: covidwho-2098399
ABSTRACT

BACKGROUND:

Sepsis is a life-threatening organ dysfunction and a major healthcare burden worldwide. Although sepsis is a medical emergency that requires immediate management, screening for the occurrence of sepsis is difficult. Herein, we propose a deep learning-based model (DLM) for screening sepsis using electrocardiography (ECG).

METHODS:

This retrospective cohort study included 46,017 patients who were admitted to two hospitals. A total of 1,548 and 639 patients had sepsis and septic shock, respectively. The DLM was developed using 73,727 ECGs from 18,142 patients, and internal validation was conducted using 7774 ECGs from 7,774 patients. Furthermore, we conducted an external validation with 20,101 ECGs from 20,101 patients from another hospital to verify the applicability of the DLM across centers.

RESULTS:

During the internal and external validations, the area under the receiver operating characteristic curve (AUC) of the DLM using 12-lead ECG was 0.901 (95% confidence interval, 0.882-0.920) and 0.863 (0.846-0.879), respectively, for screening sepsis and 0.906 (95% confidence interval (CI), 0.877-0.936) and 0.899 (95% CI, 0.872-0.925), respectively, for detecting septic shock. The AUC of the DLM for detecting sepsis using 6-lead and single-lead ECGs was 0.845-0.882. A sensitivity map revealed that the QRS complex and T waves were associated with sepsis. Subgroup analysis was conducted using ECGs from 4,609 patients who were admitted with an infectious disease, and the AUC of the DLM for predicting in-hospital mortality was 0.817 (0.793-0.840). There was a significant difference in the prediction score of DLM using ECG according to the presence of infection in the validation dataset (0.277 vs. 0.574, p < 0.001), including severe acute respiratory syndrome coronavirus 2 (0.260 vs. 0.725, p = 0.018).

CONCLUSIONS:

The DLM delivered reasonable performance for sepsis screening using 12-, 6-, and single-lead ECGs. The results suggest that sepsis can be screened using not only conventional ECG devices but also diverse life-type ECG machines employing the DLM, thereby preventing irreversible disease progression and mortality.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Deep Learning / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Scand J Trauma Resusc Emerg Med Journal subject: Emergency Medicine / Traumatology Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Sepsis / Deep Learning / COVID-19 Type of study: Cohort study / Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Scand J Trauma Resusc Emerg Med Journal subject: Emergency Medicine / Traumatology Year: 2021 Document Type: Article