Key result
A personalized machine-learning algorithm accurately detected dysglycemia from a single-lead ECG with an AUC of 0.97 ± 0.06 after applying 10-second majority voting.
Why the study?
Blood glucose monitoring is important in critically ill patients, but constant monitoring can be resource-intensive and pose a healthcare burden in clinical practice.
Does a personalized machine-learning model using single-lead ECG accurately predict dysglycemia in critically ill patients?
Observational
Does a personalized machine-learning model using single-lead ECG accurately predict dysglycemia in critically ill patients?
Effect estimate: AUC 0.97 ± 0.06
A personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG, providing a potential noninvasive method for blood glucose monitoring.
May support noninvasive dysglycemia monitoring via single-lead ECG in the ICU; hypothesis-generating and requires prospective validation.
Blood glucose (BG) monitoring is important for critically ill patients, as poor sugar control has been associated with increased mortality in hospitalized patients. However, constant BG monitoring can be resource-intensive and pose a healthcare burden in clinical practice. In this study, we aimed to develop a personalized machine-learning model to predict dysglycemia from electrocardiogram (ECG) data. We used the Medical Information Mart for Intensive Care III database as our source of data and obtained more than 20 ECG records from each included patient during a single hospital admission. We focused on lead II recordings, along with corresponding blood sugar data. We processed the data and used ECG features from each heartbeat as inputs to develop a one-class support vector machine algorithm to predict dysglycemia. The model was able to predict dysglycemia using a single heartbeat with an AUC of 0.92 ± 0.09, a sensitivity of 0.92 ± 0.10, and specificity of 0.84 ± 0.04. After applying 10 s majority voting, the AUC of the model's dysglycemia prediction increased to 0.97 ± 0.06. This study showed that a personalized machine-learning algorithm can accurately detect dysglycemia from a single-lead ECG.
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Chiu et al. (2022) conducted an observational in Dysglycemia in critically ill patients. Personalized machine-learning model (one-class support vector machine) using single-lead ECG was evaluated on Prediction of dysglycemia (AUC 0.97 ± 0.06). A personalized machine-learning algorithm accurately detected dysglycemia from a single-lead ECG with an AUC of 0.97 ± 0.06 after applying 10-second majority voting.
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