A machine learning model using heart rate, Glasgow Coma Scale, and heart rate complexity (AUC 0.99) outperformed logistic regression for identifying lifesaving intervention needs in trauma patients.
Observational (n=104)
Does a machine learning model using HR, GCS, and HRC improve the identification of the need for lifesaving interventions in trauma patients compared to logistic regression models?
A machine learning model incorporating heart rate, Glasgow Coma Scale, and heart rate complexity provides highly accurate identification of the need for lifesaving interventions in trauma patients, outperforming traditional logistic regression.
Effect estimate: AUC 0.99
To date, no studies have attempted to utilize data from a combination of vital signs, heart rate variability and complexity (HRV, HRC), as well as machine learning (ML), for identifying the need for lifesaving interventions (LSIs) in trauma patients. The objectives of this study were to examine the utility of the above for identifying LSI needs and compare different LSI-associated models, with the hypothesis that an ML model would be superior in performance over multivariate logistic regression models. One hundred four patients transported from the injury scene via helicopter were selected for the study. A wireless vital signs monitor was attached to the patient's arm and used to capture physiologic data, including HRV and HRC. The power of vital sign measurements, HRV, HRC, and Glasgow Coma Scale score (GCS) to identify patients requiring LSIs was estimated using multivariate logistic regression and ML. Receiver operating characteristic (ROC) curves were also obtained. Thirty-two patients underwent 75 LSIs. After logistic regression, ROC curves demonstrated better identification for LSIs using heart rate (HR) and HRC (area under the curve AUC of 0.81) than using HR alone (AUC of 0.73). Likewise, ROC curves demonstrated better identification for LSIs using GCS and HRC (AUC of 0.94) than using GCS and HR (AUC of 0.92). Importantly, ROC curves demonstrated that an ML model using HR, GCS, and HRC (AUC of 0.99) had superior performance over multivariate logistic regression models for identifying the need for LSIs in trauma patients. Development of computer decision support systems should utilize vital signs, HRC, and ML in order to achieve more accurate diagnostic capabilities, such as identification of needs for LSIs in trauma patients.
Liu et al. (Fri,) conducted a observational in Trauma (n=104). Machine learning model using HR, GCS, and HRC vs. Multivariate logistic regression models was evaluated on Identification of the need for lifesaving interventions (LSIs) (AUC 0.99). A machine learning model using heart rate, Glasgow Coma Scale, and heart rate complexity (AUC 0.99) outperformed logistic regression for identifying lifesaving intervention needs in trauma patients.