Inefficacy of standard vital signs for predicting mortality and the need for prehospital life-saving interventions in blunt trauma patients transported via helicopter

ABSTRACTObjective:to compare the frequency and the severity of diagnosed injuries between pedestrians struck by motor vehicles and victims of other blunt trauma mechanisms.Methods:retrospective analysis of data from the Trauma Registry, including adult blunt trauma patients admitted from 2008 to 2010. We reviewed the mechanism of trauma, vital signs on admission and the injuries identified. Severity stratification was carried using RTS, AIS-90, ISS e TRISS. Patients were assigned into group A (pedestrians struck by motor vehicle) or B (victims of other mechanisms of blunt trauma). Variables were compared between groups. We considered p<0.05 as significant.Results:a total of 5785 cases were included, and 1217 (21,0%) of which were in group A. Pedestrians struck by vehicles presented (p<0.05) higher mean age, mean heart rate upon admission, mean ISS and mean AIS in head, thorax, abdomen and extremities, as well as lower mean Glasgow coma scale, arterial blood pressure upon admission, RTS and TRISS. They also had a higher frequency of epidural hematomas, subdural hematomas, subarachnoid hemorrhage, brain swelling, cerebral contusions, costal fractures, pneumothorax, flail chest, pulmonary contusions, as well as pelvic, superior limbs and inferior limbs fractures.Conclusion:pedestrian struck by vehicles sustained intracranial, thoracic, abdominal and extremity injuries more frequently than victims of other blunt trauma mechanism as a group. They also presented worse physiologic and anatomic severity of the trauma.

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Torso computed tomography in blunt trauma patients with normal vital signs can be avoided using non-invasive tests and close clinical evaluation

Emergency Radiology ◽

10.1007/s10140-019-01712-0 ◽

2019 ◽

Vol 26 (6) ◽

pp. 655-661 ◽

Cited By ~ 1

Author(s):

Elisa Reitano ◽

Laura Briani ◽

Fabrizio Sammartano ◽

Stefania Cimbanassi ◽

Margherita Luperto ◽

...

Keyword(s):

Computed Tomography ◽

Blunt Trauma ◽

Clinical Evaluation ◽

Vital Signs ◽

Trauma Patients ◽

Non Invasive

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Predictors of Mortality and Prehospital Monitoring Limitations in Blunt Trauma Patients

BioMed Research International ◽

10.1155/2015/983409 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 16

Author(s):

Matej Strnad ◽

Vesna Borovnik Lesjak ◽

Vitka Vujanović ◽

Tine Pelcl ◽

Miljenko Križmarić

Keyword(s):

Hospital Mortality ◽

Regression Model ◽

Blunt Trauma ◽

Injury Severity ◽

Vital Signs ◽

Study Groups ◽

Multivariate Logistic Regression Model ◽

Hospital Death ◽

Trauma Patients ◽

Arterial Blood

This study aimed at determining predictors of in-hospital mortality and prehospital monitoring limitations in severely injured intubated blunt trauma patients. We retrospectively reviewed patients’ charts. Prehospital vital signs, Injury Severity Score (ISS), initial Glasgow Coma Scale (GCS), Revised Trauma Score (RTS), arterial blood gases, and lactate were compared in two study groups: survivors (n=40) and nonsurvivors (n=30). There were no significant differences in prehospital vital signs between compared groups. Nonsurvivors were older (P=0.006), with lower initial GCS (P<0.001) and higher ISS (P<0.001), along with higher lactate (P<0.001) and larger base deficit (BD;P=0.006), whereas RTS (P=0.001) was lower in nonsurvivors. For predicting mortality, area under the curve (AUC) was calculated: for lactate 0.82 (P<0.001), for ISS 0.82 (P<0.001), and for BD 0.69 (P=0.006). Lactate level of 3.4 mmol/L or more was 82% sensitive and 75% specific for predicting in-hospital death. In a multivariate logistic regression model, ISS (P=0.037), GCS (P=0.033), and age (P=0.002) were found to be independent predictors of in-hospital mortality. The AUC for regression model was 0.93 (P<0.001). Increased levels of lactate and BD on admission indicate more severe occult hypoperfusion in nonsurvivors whereas vital signs did not differ between the groups.

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Prehospital Variables Alone Can Predict Mortality After Blunt Trauma: A Novel Scoring Tool

The American Surgeon ◽

10.1177/00031348211024192 ◽

2021 ◽

pp. 000313482110241

Author(s):

Stephen Stopenski ◽

Areg Grigorian ◽

Kenji Inaba ◽

Michael Lekawa ◽

Kazuhide Matsushima ◽

...

Keyword(s):

Blunt Trauma ◽

Vital Signs ◽

Weighted Average ◽

Trauma Patients ◽

Operating Characteristics ◽

Improvement Program ◽

Logistic Regression Models ◽

Revised Trauma Score ◽

Activation Criterion ◽

Scoring Tool

Background We sought to develop a novel Prehospital Injury Mortality Score (PIMS) to predict blunt trauma mortality using only prehospital variables. Study Design The 2017 Trauma Quality Improvement Program database was queried and divided into two equal sized sets at random (derivation and validation sets). Multiple logistic regression models were created to determine the risk of mortality using age, sex, mechanism, and trauma activation criterion. The PIMS was derived using the weighted average of each independent predictor. The discriminative power of the scoring tool was assessed by calculating the area under the receiver operating characteristics (AUROC) curve. The PIMS ability to predict mortality was then assessed by using the validation cohort. The score was compared to the Revised Trauma Score (RTS) using the AUROC curve, including a subgroup of patients with normal vital signs. Results The derivation and validation groups each consisted of 163 694 patients. Seven independent predictors of mortality were identified, and the PIMS was derived with scores ranging from 0 to 20. The mortality rate increased from 1.4% to 43.9% and then 100% at scores of 1, 10, and 19, respectively. The model had very good discrimination with an AUROC of .79 in both the derivation and validation groups. When compared to the RTS, the AUROC were similar (.79 vs. .78). On subgroup analysis of patients with normal prehospital vital signs, the PIMS was superior to the RTS (.73 vs. .56). Conclusion The PIMS is a novel scoring tool to predict mortality in blunt trauma patients using prehospital variables. It had improved discriminatory power in blunt trauma patients with normal vital signs compared to the RTS.

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Improving the Prediction of Mortality and the Need for Life-Saving Interventions in Trauma Patients Using Standard Vital Signs With Heart-Rate Variability and Complexity

Shock ◽

10.1097/shk.0000000000000356 ◽

2015 ◽

Vol 43 (6) ◽

pp. 549-555 ◽

Cited By ~ 15

Author(s):

Nehemiah T. Liu ◽

John B. Holcomb ◽

Charles E. Wade ◽

Jose Salinas

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Vital Signs ◽

Trauma Patients ◽

Prediction Of Mortality ◽

Life Saving

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Estimation of individual-specific progression to impending cardiovascular instability using arterial waveforms

Journal of Applied Physiology ◽

10.1152/japplphysiol.00668.2013 ◽

2013 ◽

Vol 115 (8) ◽

pp. 1196-1202 ◽

Cited By ~ 63

Author(s):

Victor A. Convertino ◽

Greg Grudic ◽

Jane Mulligan ◽

Steve Moulton

Keyword(s):

Lower Body Negative Pressure ◽

Vital Signs ◽

Hemodynamic Instability ◽

Trauma Patients ◽

Lower Body ◽

Healthy Humans ◽

Machine Modeling ◽

Machine Learning Model ◽

Life Saving ◽

Compensatory Reserve

Trauma patients with “compensated” internal hemorrhage may not be identified with standard medical monitors until signs of shock appear, at which point it may be difficult or too late to pursue life-saving interventions. We tested the hypothesis that a novel machine-learning model called the compensatory reserve index (CRI) could differentiate tolerance to acute volume loss of individuals well in advance of changes in stroke volume (SV) or standard vital signs. Two hundred one healthy humans underwent progressive lower body negative pressure (LBNP) until the onset of hemodynamic instability (decompensation). Continuously measured photoplethysmogram signals were used to estimate SV and develop a model for estimating CRI. Validation of the CRI was tested on 101 subjects who were classified into two groups: low tolerance (LT; n = 33) and high tolerance (HT; n = 68) to LBNP (mean LBNP time: LT = 16.23 min vs. HT = 25.86 min). On an arbitrary scale of 1 to 0, the LT group CRI reached 0.6 at an average time of 5.27 ± 1.18 (95% confidence interval) min followed by 0.3 at 11.39 ± 1.14 min. In comparison, the HT group reached CRI of 0.6 at 7.62 ± 0.94 min followed by 0.3 at 15.35 ± 1.03 min. Changes in heart rate, blood pressure, and SV did not differentiate HT from LT groups. Machine modeling of the photoplethysmogram response to reduced central blood volume can accurately trend individual-specific progression to hemodynamic decompensation. These findings foretell early identification of blood loss, anticipating hemodynamic instability, and timely application of life-saving interventions.

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Development of a heart rate variability and complexity model in predicting the need for life-saving interventions amongst trauma patients

Burns & Trauma ◽

10.1186/s41038-019-0147-2 ◽

2019 ◽

Vol 7 ◽

Author(s):

Aravin Kumar ◽

Nan Liu ◽

Zhi Xiong Koh ◽

Jayne Jie Yi Chiang ◽

Yuda Soh ◽

...

Keyword(s):

Emergency Department ◽

Heart Rate ◽

Heart Rate Variability ◽

Vital Signs ◽

Fluctuation Analysis ◽

Trauma Patients ◽

Ecg Analysis ◽

Patient Disposition ◽

Life Saving ◽

Trauma Scores

Abstract Background Triage trauma scores are utilised to determine patient disposition, interventions and prognostication in the care of trauma patients. Heart rate variability (HRV) and heart rate complexity (HRC) reflect the autonomic nervous system and are derived from electrocardiogram (ECG) analysis. In this study, we aimed to develop a model incorporating HRV and HRC, to predict the need for life-saving interventions (LSI) in trauma patients, within 24 h of emergency department presentation. Methods We included adult trauma patients (≥ 18 years of age) presenting at the emergency department of Singapore General Hospital between October 2014 and October 2015. We excluded patients who had non-sinus rhythms and larger proportions of artefacts and/or ectopics in ECG analysis. We obtained patient demographics, laboratory results, vital signs and outcomes from electronic health records. We conducted univariate and multivariate analyses for predictive model building. Results Two hundred and twenty-five patients met inclusion criteria, in which 49 patients required LSIs. The LSI group had a higher proportion of deaths (10, 20.41% vs 1, 0.57%, p < 0.001). In the LSI group, the mean of detrended fluctuation analysis (DFA)-α1 (1.24 vs 1.12, p = 0.045) and the median of DFA-α2 (1.09 vs 1.00, p = 0.027) were significantly higher. Multivariate stepwise logistic regression analysis determined that a lower Glasgow Coma Scale, a higher DFA-α1 and higher DFA-α2 were independent predictors of requiring LSIs. The area under the curve (AUC) for our model (0.75, 95% confidence interval, 0.66–0.83) was higher than other scoring systems and selected vital signs. Conclusions An HRV/HRC model outperforms other triage trauma scores and selected vital signs in predicting the need for LSIs but needs to be validated in larger patient populations.

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