Deep-learning model for screening sepsis using electrocardiography

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.

Keywords: Artificial intelligence; Deep learning; Electrocardiography; Infections; Sepsis; Shock, Septic.

Fig. 1

Study flowchart

Fig. 2

Architecture of DLM to screen sepsis using ECG. conv convolutional neural network layer; DLM deep-learning model; ECG electrocardiography; FC fully connected layer

Fig. 3

Performance of DLM for screening sepsis and septic shock using electrocardiography. AUC area under the receiver operating characteristic curve; ECG electrocardiography; NPV negative predictive value; PPV positive predictive value; SEN sensitivity; SPE specificity

Fig. 4

Sensitivity map of septic shock patients

Fig. 5

Performance of DLM for predicting in-hospital mortality of patients with infectious disease. AUC area under the receiver operating characteristic curve; ECG electrocardiography; MEWS Modified Early Warning Score; NEWS National Early Warning Score; NPV negative predictive value; PPV positive predictive value; SEN sensitivity; SOFA sequential organ failure assessment; SPE specificity

Fig. 6

Change of DLM’s prediction score according to infection. SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2