Data quality of a wearable vital signs monitor in the pre-hospital and emergency departments for enhancing prediction of needs for life-saving interventions in trauma patients.

This study was designed to investigate the quality of data in the pre-hospital and emergency departments when using a wearable vital signs monitor and examine the efficacy of a combined model of standard vital signs and respective data quality indices (DQIs) for predicting the need for life-saving interventions (LSIs) in trauma patients. It was hypothesised that prediction of needs for LSIs in trauma patients is associated with data quality. Also, a model utilizing vital signs and DQIs to predict the needs for LSIs would be able to outperform models using vital signs alone. Data from 104 pre-hospital trauma patients transported by helicopter were analysed, including means and standard deviations of continuous vital signs, related DQIs and Glasgow coma scale (GCS) scores for LSI and non-LSI patient groups. DQIs involved percentages of valid measurements and mean deviation ratios. Various multivariate logistic regression models for predicting LSI needs were also obtained and compared through receiver-operating characteristic (ROC) curves. Demographics of patients were not statistically different between LSI and non-LSI patient groups. In addition, ROC curves demonstrated better prediction of LSI needs in patients using heart rate and DQIs (area under the curve [AUC] of 0.86) than using heart rate alone (AUC of 0.73). Likewise, ROC curves demonstrated better prediction using heart rate, total GCS score and DQIs (AUC of 0.99) than using heart rate and total GCS score (AUC of 0.92). AUCs were statistically different (p < 0.05). This study showed that data quality could be used in addition to continuous vital signs for predicting the need for LSIs in trauma patients. Importantly, trauma systems should incorporate processes to regulate data quality of physiologic data in the pre-hospital and emergency departments. By doing so, data quality could be improved and lead to better prediction of needs for LSIs in trauma patients.

Evaluation of standard versus nonstandard vital signs monitors in the prehospital and emergency departments: results and lessons learned from a trauma patient care protocol.

BACKGROUND: This study aimed to determine the effectiveness of using a wireless, portable vital signs monitor (WVSM) for predicting the need for lifesaving interventions (LSIs) in the emergency department (ED) and use a multivariate logistic regression model to determine whether the WVSM was an improved predictor of LSIs in the ED over the standard of care monitor currently being used. METHODS: This study analyzed 305 consecutive patients transported from the scene via helicopter to a Level I trauma center. For 104 patients in the study, a WVSM was also attached to the patient's arm and used to record and display prehospital and hospital physiologic data in real time on a handheld computer and in the trauma bay. Multivariate logistic regression analyses were performed for accuracy in predicting needs for LSIs in control and WVSM subjects. In addition, receiver operating characteristic curves were obtained to examine the discriminating power of the models for the outcome of one or more LSIs in the ED. RESULTS: Of the 305 patients, 73 underwent 109 LSIs in the ED. Of these, 21 patients wore the WVSM during transport in addition to the standard monitor. Logistic regression analysis revealed that heart rate, respiratory rate, and systolic blood pressure were significantly associated with an increased risk for LSIs in the ED (p < 0.05). Receiver operating characteristic curve analysis also demonstrated better prediction for LSIs performed in the ED in WVSM subjects than in control subjects (area under the curve, 0.86 vs. 0.81, respectively). CONCLUSION: The WVSM system leads to improved LSI accuracy in the ED. In addition, many important lessons have been learned in preparation for this study. Adoption of nonstandard vital signs monitors into critical care/trauma medicine may require a new paradigm of personnel education, training, and practice. LEVEL OF EVIDENCE: Therapeutic/care management, level IV.

Utility of vital signs, heart rate variability and complexity, and machine learning for identifying the need for lifesaving interventions in trauma patients.

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.

Development and validation of a machine learning algorithm and hybrid system to predict the need for life-saving interventions in trauma patients.

Accurate and effective diagnosis of actual injury severity can be problematic in trauma patients. Inherent physiologic compensatory mechanisms may prevent accurate diagnosis and mask true severity in many circumstances. The objective of this project was the development and validation of a multiparameter machine learning algorithm and system capable of predicting the need for life-saving interventions (LSIs) in trauma patients. Statistics based on means, slopes, and maxima of various vital sign measurements corresponding to 79 trauma patient records generated over 110,000 feature sets, which were used to develop, train, and implement the system. Comparisons among several machine learning models proved that a multilayer perceptron would best implement the algorithm in a hybrid system consisting of a machine learning component and basic detection rules. Additionally, 295,994 feature sets from 82 h of trauma patient data showed that the system can obtain 89.8 % accuracy within 5 min of recorded LSIs. Use of machine learning technologies combined with basic detection rules provides a potential approach for accurately assessing the need for LSIs in trauma patients. The performance of this system demonstrates that machine learning technology can be implemented in a real-time fashion and potentially used in a critical care environment.

Advanced monitoring and decision support for battlefield critical care environment.

Automation and decision support systems are vital for improving critical patient care in the battlefield environment. However, advances in data management, sensor fusion, and decision support algorithms must be developed and incorporated into existing patient monitoring systems for this technology to improve battlefield patient care. This paper examines issues related to research and development of advanced monitoring and decision support systems for use both on the battlefield and in the civilian trauma environment.