Abstract 343: Prediction of Lifesaving Interventions Using Changes in Vital Signs from Prehospital Scene to Emergency Department

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Jorge H Mena Munoz ◽  
Ashley Petersen ◽  
Francis X Guyette
Keyword(s):  
Emergency Department ◽  
Decision Trees ◽  
Recursive Partitioning ◽  
Vital Signs ◽  
Large Change ◽  
Predictive Performance ◽  
Classification Performance ◽  
Secondary Outcome ◽  
Classification Error ◽  
Trauma Patients

Objective: We investigate whether changes in vital signs between the prehospital scene and emergency department (ED) can be used to develop triage tools to predict the need for life-saving interventions (LSI) and survival in trauma patients. Methods: We analyzed a prospective cohort with any prehospital systolic blood pressure (SBP) ≤ 90 mmHg or Glasgow Coma Scale ≤ 8 who were admitted to an ED at 11 sites of the Resuscitation Outcomes Consortium. The primary outcome was the need for in-hospital LSI (e.g. invasive airway management, invasive bleeding control, blood transfusion, craniotomy, cardiopulmonary resuscitation). Secondary outcome was survival to hospital discharge. Changes in heart rate (HR), SBP, shock index (SI), and respiratory rate (RR) from first prehospital assessment to first ED assessment were considered as predictors in addition to sex, age, mechanism of injury, trauma center level, duration of transport, type of transport, and prehospital fluid volume. Decision trees for each outcome were developed using binary recursive partitioning with predictive performance measured using sensitivity, specificity, and classification error. Results: 5625 subjects were included in our analysis with 49% in need of LSI and 21% dying prior to discharge. Patients needing an LSI tended to either: (1) have an increasing SI (delta ≥ 0.22), (2) have a decreasing SI (delta < 0.22) and >500 mL prehospital fluids, or (3) have a decreasing SI (delta < 0.22), ≤500 mL prehospital fluids, and large change in RR (delta ≥ 9.5 or delta < -7.5). Those surviving to discharge tended to either: (1) have a decreasing SI (delta < 0.57) and a HR that did not decrease greatly (delta > -47) or (2) have an increase in SI (0.57 ≤ delta < 1) and a declining RR (delta < 5). LSI tree had a sensitivity of 58.7% and specificity of 63.3%. Survival tree had sensitivity of 96.2% and specificity of 21.3%. Conclusion: Though the decision trees were constructed with the best data in terms of initial triage and early secondary triage, the classification performance was limited. This highlights the difficulties of developing vital sign based triage tools to predict the need for LSI and survival.

Prehospital Vital Signs Accurately Predict Initial Emergency Department Vital Signs

2020 ◽  
Vol 35 (3) ◽  
pp. 254-259
Author(s):  
Marc D. Trust ◽  
Morgan Schellenberg ◽  
Subarna Biswas ◽  
Kenji Inaba ◽  
Vincent Cheng ◽  
...  
Keyword(s):  
Emergency Department ◽  
Intraclass Correlation ◽  
Vital Signs ◽  
Correlation Coefficients ◽  
Trauma Team ◽  
Urban Setting ◽  
Trauma Patients ◽  
Level I ◽  
Gcs Score ◽  
Good Agreement

AbstractIntroduction:Prehospital vital signs are used to triage trauma patients to mobilize appropriate resources and personnel prior to patient arrival in the emergency department (ED). Due to inherent challenges in obtaining prehospital vital signs, concerns exist regarding their accuracy and ability to predict first ED vitals.Hypothesis/Problem:The objective of this study was to determine the correlation between prehospital and initial ED vitals among patients meeting criteria for highest levels of trauma team activation (TTA). The hypothesis was that in a medical system with short transport times, prehospital and first ED vital signs would correlate well.Methods:Patients meeting criteria for highest levels of TTA at a Level I trauma center (2008-2018) were included. Those with absent or missing prehospital vital signs were excluded. Demographics, injury data, and prehospital and first ED vital signs were abstracted. Prehospital and initial ED vital signs were compared using Bland-Altman intraclass correlation coefficients (ICC) with good agreement as >0.60; fair as 0.40-0.60; and poor as <0.40).Results:After exclusions, 15,320 patients were included. Mean age was 39 years (range 0-105) and 11,622 patients (76%) were male. Mechanism of injury was blunt in 79% (n = 12,041) and mortality was three percent (n = 513). Mean transport time was 21 minutes (range 0-1,439). Prehospital and first ED vital signs demonstrated good agreement for Glasgow Coma Scale (GCS) score (ICC 0.79; 95% CI, 0.77-0.79); fair agreement for heart rate (HR; ICC 0.59; 95% CI, 0.56-0.61) and systolic blood pressure (SBP; ICC 0.48; 95% CI, 0.46-0.49); and poor agreement for pulse pressure (PP; ICC 0.32; 95% CI, 0.30-0.33) and respiratory rate (RR; ICC 0.13; 95% CI, 0.11-0.15).Conclusion:Despite challenges in prehospital assessments, field GCS, SBP, and HR correlate well with first ED vital signs. The data show that these prehospital measurements accurately predict initial ED vitals in an urban setting with short transport times. The generalizability of these data to settings with longer transport times is unknown.

scholarly journals Validating the RISE UP score for predicting prognosis in patients with COVID-19 in the emergency department, a retrospective study

2020 ◽  
Author(s):  
Paul M.E.L. van Dam ◽  
Noortje Zelis ◽  
Patricia M. Stassen ◽  
Daan J.L. van Twist ◽  
Peter W. de Leeuw ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Outcome ◽  
Vital Signs ◽  
Favourable Outcome ◽  
Secondary Outcome ◽  
Care Hospital ◽  
Short Term ◽  
Short Term Mortality ◽  
Patients At Risk ◽  
System Information

AbstractObjectiveTo mitigate the burden of COVID-19 on the healthcare system, information on the prognosis of the disease is needed. The recently developed RISE UP score has very good discriminatory value with respect to short-term mortality in older patients in the emergency department (ED). It consists of six items: age, abnormal vital signs, albumin, blood urea nitrogen (BUN), lactate dehydrogenase (LDH), and bilirubin. We hypothesized that the RISE UP score could have discriminatory value with regard to 30-day mortality in ED patients with COVID-19.SettingTwo EDs of the Zuyderland Medical Centre (MC), secondary care hospital in the Netherlands.ParticipantsThe study sample consisted of 642 adult ED patients diagnosed with COVID-19 between March 3rd until May 25th 2020. Inclusion criteria were: 1) admission to the hospital with symptoms suggestive of COVID-19, and 2) positive result of the polymerase chain reaction (PCR), or (very) high suspicion of COVID-19 according to the chest computed tomography (CT) scan.OutcomePrimary outcome was 30-day mortality, secondary outcome was a composite of 30-day mortality and admission to intensive care unit (ICU).ResultsWithin 30 days after presentation, 167 patients (26.0%) died and 102 patients (15.9%) were admitted to ICU. The RISE UP score showed good discriminatory value with respect to 30-day mortality (AUC 0.77, 95% CI 0.73-0.81), and to the composite outcome (AUC 0.72, 95% CI 0.68-0.76). Patients with RISE UP scores below 10% (121 patients) had favourable outcome (0% mortality and 5% ICU admissions). Patients with a RISE UP score above 30% (221 patients) were at high risk of adverse outcome (46.6% mortality and 19% ICU admissions).ConclusionThe RISE UP score is an accurate prognostic model for adverse outcome in ED patients with COVID-19. It can be used to identify patients at risk of short-term adverse outcome, and may help guiding decision-making and allocating healthcare resources.

scholarly journals Using modified early warning score to predict need of lifesaving intervention in adult non-trauma patients in a tertiary state hospital

2018 ◽  
Vol 25 (3) ◽  
pp. 146-151 ◽  
Author(s):  
Leong Shian Peng ◽  
Azhana Hassan ◽  
Aida Bustam ◽  
Muhaimin Noor Azhar ◽  
Rashidi Ahmad
Keyword(s):  
Emergency Department ◽  
Critically Ill ◽  
Early Warning ◽  
Demographic Data ◽  
Vital Signs ◽  
Multivariate Logistic Regression Analysis ◽  
Early Warning Score ◽  
Trauma Patients ◽  
Multivariate Logistic Regression ◽  
Level Of Consciousness

Background: Modified early warning score has been validated in many uses in the emergency department. We propose that the modified early warning score performs well in predicting the need of lifesaving interventions in the emergency department, as a predictor of patients who are critically ill. Objective: The study aims to evaluate the use of modified early warning score in sorting out critically ill patients in the emergency department. Methods: The patients’ demographic data and first vital signs (blood pressure, heart rate, temperature, respiratory rate, and level of consciousness) were collected prospectively. Individual modified early warning score was calculated. The outcome was a patient received one or more lifesaving interventions toward the end of stay in emergency department. Multivariate logistic regression analysis was utilized to assess the association between modified early warning score and other potential predictors with outcome. Results: There are a total of 259 patients enrolled into the study. The optimal modified early warning score in predicting lifesaving intervention was ≥4 with a sensitivity of 95% and specificity of 81%. Modified early warning score ≥4 (odds ratio = 96.97, 95% confidence interval = 11.82–795.23, p < 0.001) was found to significantly increase the risk of receiving lifesaving intervention in the emergency department. Conclusion: Modified early warning score is found to be a good predictor for patients in need of lifesaving intervention in the emergency department.

Level of agreement between prehospital and emergency department vital signs in trauma patients

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Michael M Dinh ◽  
Matthew Oliver ◽  
Kendall Bein ◽  
Sandy Muecke ◽  
Therese Carroll ◽  
...  
Keyword(s):  
Emergency Department ◽  
Vital Signs ◽  
Trauma Patients ◽  
Level Of Agreement

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

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 &lt; 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.

Impact of 24/7 Onsite Emergency Radiology Staff Coverage on Emergency Department Workflow

2021 ◽  
pp. 084653712110238
Author(s):  
Francesco Macri ◽  
Bonnie T. Niu ◽  
Shannon Erdelyi ◽  
John R. Mayo ◽  
Faisal Khosa ◽  
...  
Keyword(s):  
Emergency Department ◽  
Major Trauma ◽  
Patient Flow ◽  
Interrupted Time Series ◽  
Trauma Patients ◽  
Linear Regression Models ◽  
High Acuity ◽  
Time Series Approach ◽  
Comparison Series ◽  
The Impact

Purpose: Assess the impact of 24/7/365 emergency trauma radiology (ETR) coverage on Emergency Department (ED) patient flow in an urban, quaternary-care teaching hospital. Methods: Patient ED visit and imaging information were extracted from the hospital patient care information system for 2008 to 2018. An interrupted time-series approach with a comparison group was used to study the impact of 24/7/365 ETR on average monthly ED length of stay (ED-LOS) and Emergency Physician to disposition time (EP-DISP). Linear regression models were fit with abrupt and permanent interrupts for 24/7/365 ETR, a coefficient for comparison series and a SARIMA error term; subgroup analyses were performed by patient arrival time, imaging type and chief complaint. Results: During the study period, there were 949,029 ED visits and 739,796 diagnostic tests. Following implementation of 24/7/365 coverage, we found a significant decrease in EP-DISP time for patients requiring only radiographs (-29 min;95%CI:-52,-6) and a significant increase in EP-DISP time for major trauma patients (46 min;95%CI:13,79). No significant change in patient throughput was observed during evening hours for any patient subgroup. For overnight patients, there was a reduction in EP-DISP for patients with symptoms consistent with stroke (-78 min;95%CI:-131,-24) and for high acuity patients who required imaging (-33 min;95%CI:-57,-10). Changes in ED-LOS followed a similar pattern. Conclusions: At our institution, 24/7/365 in-house ETR staff radiology coverage was associated with improved ED flow for patients requiring only radiographs and for overnight stroke and high acuity patients. Major trauma patients spent more time in the ED, perhaps reflecting the required multidisciplinary management.

scholarly journals Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Louis Ehwerhemuepha ◽  
Theodore Heyming ◽  
Rachel Marano ◽  
Mary Jane Piroutek ◽  
Antonio C. Arrieta ◽  
...  
Keyword(s):  
Emergency Department ◽  
Severe Sepsis ◽  
Early Warning ◽  
Characteristic Curve ◽  
Vital Signs ◽  
Warning System ◽  
Receiver Operator Characteristic Curve ◽  
Warning Signs ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

AbstractThis study was designed to develop and validate an early warning system for sepsis based on a predictive model of critical decompensation. Data from the electronic medical records for 537,837 visits to a pediatric Emergency Department (ED) from March 2013 to December 2019 were collected. A multiclass stochastic gradient boosting model was built to identify early warning signs associated with death, severe sepsis, non-severe sepsis, and bacteremia. Model features included triage vital signs, previous diagnoses, medications, and healthcare utilizations within 6 months of the index ED visit. There were 483 patients who had severe sepsis and/or died, 1102 had non-severe sepsis, 1103 had positive bacteremia tests, and the remaining had none of the events. The most important predictors were age, heart rate, length of stay of previous hospitalizations, temperature, systolic blood pressure, and prior sepsis. The one-versus-all area under the receiver operator characteristic curve (AUROC) were 0.979 (0.967, 0.991), 0.990 (0.985, 0.995), 0.976 (0.972, 0.981), and 0.968 (0.962, 0.974) for death, severe sepsis, non-severe sepsis, and bacteremia without sepsis respectively. The multi-class macro average AUROC and area under the precision recall curve were 0.977 and 0.316 respectively. The study findings were used to develop an automated early warning decision tool for sepsis. Implementation of this model in pediatric EDs will allow sepsis-related critical decompensation to be predicted accurately after a few seconds of triage.

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