Utility of Vital Signs, Heart-rate Variability and Complexity, and Machine Learning for Identifying the Need for Life-saving Interventions in Trauma Patients
ARMY INST OF SURGICAL RESEARCH FORT SAM HOUSTON TX
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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 patients 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.
- Anatomy and Physiology
- Medicine and Medical Research