Prehospital National Early Warning Score predicts early mortality

Diagnostic accuracy of early warning system scores in the prehospital setting

The utilisation of pre-hospital early warning scores in ambulance services is widely endorsed to promptly identify patients at risk of clinical deterioration. Early warning scores enable clinicians to estimate risk based on clinical observations and vital signs, with higher scores indicating an elevated risk of adverse outcomes. Local healthcare systems establish threshold values for these scores to guide clinical decision-making, triage, and response, necessitating a careful balance between identifying critically unwell patients and managing the challenge of prioritisation. Given the limited evidence for optimal early warning scores in emergency department and pre-hospital care settings, a systematic review by Guan et al. (2022) was undertaken to assess the diagnostic accuracy of early warning scores for predicting in-hospital deterioration when applied in the emergency department or pre-hospital setting. This commentary aims to critically appraise the methods used within the review Guan et al (2022) and expand upon the findings in the context of clinical practice.

Derivation and validation of a blood biomarker score for 2-day mortality prediction from prehospital care: a multicenter, cohort, EMS-based study

Identifying potentially life-threatening diseases is a key challenge for emergency medical services. This study aims at examining the role of different prehospital biomarkers from point-of-care testing to derive and validate a score to detect 2-day in-hospital mortality. We conducted a prospective, observational, prehospital, ongoing, and derivation-validation study in three Spanish provinces, in adults evacuated by ambulance and admitted to the emergency department. A total of 23 ambulance-based biomarkers were collected from each patient. A biomarker score based on logistic regression was fitted to predict 2-day mortality from an optimum subset of variables from prehospital blood analysis, obtained through an automated feature selection stage. 2806 cases were analyzed, with a median age of 68 (interquartile range 51-81), 42.3% of women, and a 2-day mortality rate of 5.5% (154 non-survivors). The blood biomarker score was constituted by the partial pressure of carbon dioxide, lactate, and creatinine. The score fitted with logistic regression using these biomarkers reached a high performance to predict 2-day mortality, with an AUC of 0.933 (95% CI 0.841-0.973). The following risk levels for 2-day mortality were identified from the score: low risk (score < 1), where only 8.2% of non-survivors were assigned to; medium risk (1 ≤ score < 4); and high risk (score ≥ 4), where the 2-day mortality rate was 57.6%. The novel blood biomarker score provides an excellent association with 2-day in-hospital mortality, as well as real-time feedback on the metabolic-respiratory patient status. Thus, this score can help in the decision-making process at critical moments in life-threatening situations.

Tracking the National Early Warning Score 2 from Prehospital Care to the Emergency Department: A Prospective, Ambulance-Based, Observational Study

Aim of the study: To assess the prognostic ability of the National Early Warning Score 2 (NEWS2) at three time points of care -at the emergency scene (NEWS2-1), just before starting the transfer by ambulance to the hospital (NEWS2- 2), and at the hospital triage box (NEWS2-3)- to estimate in-hospital mortality after two days since the index event.Methods: Prospective, multicenter, ambulance-based, cohort ongoing study in adults (>18 years) consecutively attended by advanced life support (ALS) and evacuated with high-priority to the emergency departments (ED) between October 2018 and May 2021. Vital sign measures were used to calculate the NEWS2 score at each time point, then this score was entered in a logistic regression model as the single predictor. Two outcomes were considered: first, all-cause mortality of the patients within 2 days of presentation to EMS, and second, unplanned ICU admission. The calibration and scores comparison was performed by representing the predicted vs the observed risk curves according to NEWS score value.Results: 4943 patients were enrolled. Median age was 69 years (interquartile range 53- 81). The NEWS2-3 presented the better performance for all-cause two-day in-hospital mortality with an AUC of 0.941 (95% CI: 0.917-0.964), showing statistical differences with both the NEWS2-1 (0.872 (95% CI: 0.833-0.911); p < 0.003) and with the NEWS2- 2 (0.895 (95% CI: 0.866-0.925; p < 0.05). The calibration and scores comparison results showed that the NEWS2-3 was the best predictive score followed by the NEWS2-2 and the NEWS2-1, respectively.Conclusions: The NEWS2 has an excellent predictive performance. The score showed a very consistent response over time with the difference between "at the emergency scene" and "pre-evacuation" presenting the sharpest change with decreased threshold values, thus displaying a drop in the risk of acute clinical impairment.

The impact of pharmacist early active consultation (PEAC) on multidrug resistance organism treatment outcomes: A prospective historically controlled study

Background and aim: Infectious disease (ID) consultation can improve multidrug-resistant organism (MDRO) treatment outcomes. However, the impact of clinical pharmacists' ID consultation on MDRO therapy, especially early initiation, has not been reported. In this study, we try to explore the impact of the pharmacist early active consultation (PEAC) on MDRO patient management. Methods: We conducted a prospective historical controlled study based on PEAC in MDRO patients. The retrospective control group was patients hospitalized 18 months before the PEAC initiation, and the prospective PEAC group was patients hospitalized 18 months after the PEAC initiation. Primary endpoint was 30-day all-cause mortality. Secondary outcomes were MDRO clinical outcome, duration of antibiotic use, length of stay, antibiotic consumption and antibiotic costs. Further subgroup analysis of secondary outcomes was performed by the condition at admission, MDRO pathogenicity and MDRO clinical outcome. Results: 188 MDRO patients were included. After adjusting for potential predictors, PEAC reduced the 30-day all-cause mortality by 70% (HR 0.30, 95% CI 0.09-0.96, p = 0.042). PEAC group had clinical improvement than control group (89.47% vs. 65.59%, p < 0.001), especially in patients with non-severe clinical conditions at admission (98.41% vs. 70.18%, p < 0.001). However, no significant differences were found between groups in length of stay, antibiotics consumption, and antibiotics costs. Conclusion: Early active pharmacy ID consultation can reduce 30-day all-cause mortality and improve clinical outcomes in MDRO patients.

Prognostic value of National Early Warning Score and Modified Early Warning Score on intensive care unit readmission and mortality: A prospective observational study

Background

Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) are widely used in predicting the mortality and intensive care unit (ICU) admission of critically ill patients. This study was conducted to evaluate and compare the prognostic value of NEWS and MEWS for predicting ICU readmission, mortality, and related outcomes in critically ill patients at the time of ICU discharge.

Methods

This multicenter, prospective, observational study was conducted over a year, from April 2019 to March 2020, in the general ICUs of two university-affiliated hospitals in Northwest Iran. MEWS and NEWS were compared based on the patients’ outcomes (including mortality, ICU readmission, time to readmission, discharge type, mechanical ventilation (MV), MV duration, and multiple organ failure after readmission) using the univariable and multivariable binary logistic regression. The receiver operating characteristic (ROC) curve was used to determine the outcome predictability of MEWS and NEWS.

Results

A total of 410 ICU patients were enrolled in this study. According to multivariable logistic regression analysis, both MEWS and NEWS were predictors of ICU readmission, time to readmission, MV status after readmission, MV duration, and multiple organ failure after readmission. The area under the ROC curve (AUC) for predicting mortality was 0.91 (95% CI = 0.88–0.94, P < 0.0001) for the NEWS and 0.88 (95% CI = 0.84–0.91, P < 0.0001) for the MEWS. There was no significant difference between the AUC of the NEWS and the MEWS for predicting mortality (P = 0.082). However, for ICU readmission (0.84 vs. 0.71), time to readmission (0.82 vs. 0.67), MV after readmission (0.83 vs. 0.72), MV duration (0.81 vs. 0.67), and multiple organ failure (0.833 vs. 0.710), the AUCs of MEWS were significantly greater (P < 0.001).

Conclusion

National Early Warning Score and MEWS values of >4 demonstrated high sensitivity and specificity in identifying the risk of mortality for the patients’ discharge from ICU. However, we found that the MEWS showed superiority over the NEWS score in predicting other outcomes. Eventually, MEWS could be considered an efficient prediction score for morbidity and mortality of critically ill patients.

Quality emergency care (QEC) in resource limited settings: A concept analysis

INTRODUCTION

Providing appropriate high-quality emergency care (QEC) commensurate with patients' needs is critical for continuity of care, patient safety, optimal clinical outcomes, reduced mortality, and patient satisfaction. This concept analysis aims to define and assist in understanding the concept of QEC in resource-limited settings.

METHODS

Quality emergency care concept analysis was conducted using Walker and Avant's approach. Several literature review methods and dictionaries were used to explore the QEC concept.

RESULTS

Immediate assessment, rapid diagnosis, and critical interventions are the attributes of QEC for life-threatening and time-sensitive conditions, leading to timely and safe care provision.

DISCUSSION

Nurses serve as the backbone for most emergency care centers such as primary care, emergency department, and even prehospital care. The first few hours following a potential life- or limb-threatening condition are vital. The emergency care rendered to patients can significantly affect treatment's overall outcome; therefore, quality emergency care is critical.

CONCLUSION

Predictive value of the New Zealand Early Warning Score for early mortality in low-acuity patients discharged at scene by paramedics: an observational study

OBJECTIVES

The utility of New Zealand Early Warning Score (NZEWS) for prediction of adversity in low-acuity patients discharged at scene by paramedics has not been investigated. The objective of this study was to evaluate the association between the NZEWS risk-assessment tool and adverse outcomes of early mortality or ambulance reattendance within 48 hours in low-acuity, prehospital patients not transported by ambulance.

DESIGN

A retrospective cohort study.

SETTING

Prehospital emergency medical service provided by St John New Zealand over a 2-year period (1 July 2016 through 30 June 2018).

PARTICIPANTS

83 171 low-acuity, adult patients who were attended by an ambulance and discharged at scene. Of these, 41 406 had sufficient recorded data to calculate an NZEWS.

PRIMARY AND SECONDARY OUTCOMES AND MEASURES

Binary logistic regression modelling was used to investigate the association between the NZEWS and adverse outcomes of reattendance within 48 hours, mortality within 2 days, mortality within 7 days and mortality within 30 days.

RESULTS

An NZEWS greater than 0 was significantly associated with all adverse outcomes studied (p<0.01), compared with the reference group (NZEWS=0). There was a startling correlation between 2-day, 7-day and 30-day mortality and higher early warning scores; the odds of 2-day mortality in patients with an early warning score>10 was 70 times that of those scoring 0 (adjusted OR 70.64, 95% CI: 30.73 to 162.36). The best predictability for adverse outcome was observed for 2-day and 7-day mortality, with moderate area under the receiver operating characteristic curve scores of 0.78 (95% CI: 0.73 to 0.82) and 0.74 (95% CI: 0.71 to 0.77), respectively.

CONCLUSIONS

Adverse outcomes in low-acuity non-transported patients show a significant association with risk prediction by the NZEWS. There was a very high association between large early warning scores and 2-day mortality in this patient group. These findings suggest that NZEWS has significant utility for decision support and improving safety when determining the appropriateness of discharging low-acuity patients at the scene.

Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review

Background and Objectives

Patient assessment and possible deterioration prediction are a healthcare priority. Increasing demand for outpatient emergency care services requires the implementation of simple, quick, and effective systems of patient evaluation and stratification. The purpose of this review is to identify the most effective Early Warning Score (EWS) for the early detection of the risk of complications when screening emergency outpatients for a potentially serious condition.

Materials and Methods

Systematic review of the bibliography made in 2022. Scientific articles in Spanish and English were collected from the databases and search engines of Pubmed, Cochrane, and Dialnet, which were published between 2017 and 2021 about EWSs and their capacity to predict complications.

Results

For analysis eleven articles were selected. Eight dealt with the application of different early warning scores in outpatient situations, concluding that all the scoring systems they studied were applicable. Three evaluated the predictive ability of various scoring systems and found no significant differences in their results. The eight articles evaluated the suitability of NEWS/NEWS2 to outpatient conditions and concluded it was the most suitable in pre-hospital emergency settings.

Conclusions

The early warning scores that were studied can be applied at the pre-hospital level, as they can predict patient mortality in the short term (24 or 48 h) and support clinical patient evaluation and medical decision making. Among them, NEWS2 is the most suitable for screening potentially deteriorating medical emergency outpatients.

Ambulance crew-initiated non-conveyance in the Helsinki EMS system-A retrospective cohort study

Background

Ambulance patients are usually transported to the hospital in the emergency medical service (EMS) system. The aim of this study was to describe the non‐conveyance practice in the Helsinki EMS system and to report mortality following non‐conveyance decisions.

Methods

All prehospital patients ≥16 years attended by the EMS but not transported to a hospital during 2013–2017 were included in the study. EMS mission‐ and patient‐related factors were collected and examined in relation to patient death within 30 days of the EMS non‐conveyance decision.

Results

The EMS performed 324,207 missions with a patient during the study period. The patient was not transported in 95,909 (29.6%) missions; 72,233 missions met the study criteria. The patient mean age (standard deviation) was 59.5 (22.5) years; 55.5% of patients were female. The most common dispatch codes were malaise (15.0%), suspected decline in vital signs (14.0%), and falling over (12.9%). A total of 960 (1.3%) patients died within 30 days after the non‐conveyance decision. Multivariate logistic regression analysis revealed that mortality was associated with the patient's inability to walk (odds ratio 3.19, 95% confidence interval 2.67–3.80), ambulance dispatch due to shortness of breath (2.73, 2.27–3.27), decreased level of consciousness (2.72, 1.75–4.10), decreased blood oxygen saturation (2.64, 2.27–3.06), and abnormal systolic blood pressure (2.48, 1.79–3.37).

Conclusion

One‐third of EMS missions did not result in patient transport to the hospital. Thirty‐day mortality was 1.3%. Abnormalities in multiple respiratory‐related vital signs were associated with an increased likelihood of death within 30 days.

A systematic review of the discrimination and absolute mortality predicted by the National Early Warning Scores according to different cut-off values and prediction windows

BACKGROUND

Although early warning scores were intended to simply identify patients in need of life-saving interventions, prediction has become their commonest metric. This review examined variation in the ability of the National Early Warning Scores (NEWS) in adult patients to predict absolute mortality at different times and cut-offs values.

METHOD

Following PRISMA guidelines, all studies reporting NEWS and NEWS2 providing enough information to fulfil the review's aims were included.

RESULTS

From 121 papers identified, the average area under the Receiver Operating Characteristic curve (AUC) for mortality declined from 0.90 at 24-hours to 0.76 at 30-days. Studies with a low overall mortality had a higher AUC for 24-hour mortality, as did general ward patients compared to patients seen earlier in their treatment. 24-hour mortality increased from 1.8% for a NEWS ≥3 to 7.8% for NEWS ≥7. Although 24-hour mortality for NEWS <3 was only 0.07% these deaths accounted for 9% of all deaths within 24-hours; for NEWS <7 24-hour mortality was 0.23%, which accounted for 44% of all 24-hour deaths. Within 30-days of a NEWS recording 22% of all deaths occurred in patients with a NEWS <3, 52% in patients with a NEWS <5, and 75% in patient with a NEWS <7.

CONCLUSION

NEWS reliably identifies patients most and least likely to die within 24-hours, which is what it was designed to do. However, many patients identified to have a low risk of imminent death die within 30-days. NEWS mortality predictions beyond 24-hours are unreliable.

The use of early warning system scores in prehospital and emergency department settings to predict clinical deterioration: A systematic review and meta-analysis

Background

It is unclear which Early Warning System (EWS) score best predicts in-hospital deterioration of patients when applied in the Emergency Department (ED) or prehospital setting.

Methods

This systematic review (SR) and meta-analysis assessed the predictive abilities of five commonly used EWS scores (National Early Warning Score (NEWS) and its updated version NEWS2, Modified Early Warning Score (MEWS), Rapid Acute Physiological Score (RAPS), and Cardiac Arrest Risk Triage (CART)). Outcomes of interest included admission to intensive care unit (ICU), and 3-to-30-day mortality following hospital admission. Using DerSimonian and Laird random-effects models, pooled estimates were calculated according to the EWS score cut-off points, outcomes, and study setting. Risk of bias was evaluated using the Newcastle-Ottawa scale. Meta-regressions investigated between-study heterogeneity. Funnel plots tested for publication bias. The SR is registered in PROSPERO (CRD42020191254).

Results

Overall, 11,565 articles were identified, of which 20 were included. In the ED setting, MEWS, and NEWS at cut-off points of 3, 4, or 6 had similar pooled diagnostic odds ratios (DOR) to predict 30-day mortality, ranging from 4.05 (95% Confidence Interval (CI) 2.35–6.99) to 6.48 (95% CI 1.83–22.89), p = 0.757. MEWS at a cut-off point ≥3 had a similar DOR when predicting ICU admission (5.54 (95% CI 2.02–15.21)). MEWS ≥5 and NEWS ≥7 had DORs of 3.05 (95% CI 2.00–4.65) and 4.74 (95% CI 4.08–5.50), respectively, when predicting 30-day mortality in patients presenting with sepsis in the ED. In the prehospital setting, the EWS scores significantly predicted 3-day mortality but failed to predict 30-day mortality.

Conclusion

EWS scores’ predictability of clinical deterioration is improved when the score is applied to patients treated in the hospital setting. However, the high thresholds used and the failure of the scores to predict 30-day mortality make them less suited for use in the prehospital setting.

Can Prehospital Data Improve Early Identification of Sepsis in Emergency Department? An Integrative Review of Machine Learning Approaches

BACKGROUND

Sepsis is associated with high mortality, especially during the novel coronavirus disease 2019 (COVID-19) pandemic. Along with high monetary health care costs for sepsis treatment, there is a lasting impact on lives of sepsis survivors and their caregivers. Early identification is necessary to reduce the negative impact of sepsis and to improve patient outcomes. Prehospital data are among the earliest information collected by health care systems. Using these untapped sources of data in machine learning (ML)-based approaches can identify patients with sepsis earlier in emergency department (ED).

OBJECTIVES

This integrative literature review aims to discuss the importance of utilizing prehospital data elements in ED, summarize their current use in developing ML-based prediction models, and specifically identify those data elements that can potentially contribute to early identification of sepsis in ED when used in ML-based approaches.

METHOD

Literature search strategy includes following two separate searches: (1) use of prehospital data in ML models in ED; and (2) ML models that are developed specifically to predict/detect sepsis in ED. In total, 24 articles are used in this review.

RESULTS

A summary of prehospital data used to identify time-sensitive conditions earlier in ED is provided. Literature related to use of ML models for early identification of sepsis in ED is limited and no studies were found related to ML models using prehospital data in prediction/early identification of sepsis in ED. Among those using ED data, ML models outperform traditional statistical models. In addition, the use of the free-text elements and natural language processing (NLP) methods could result in better prediction of sepsis in ED.

CONCLUSION

This study reviews the use of prehospital data in early decision-making in ED and suggests that researchers utilize such data elements for prediction/early identification of sepsis in ML-based approaches.

Lactate improves the predictive ability of the National Early Warning Score 2 in the emergency department

AIMS

The aim of this study was to compare the ability to predict 2-, 7-, 14-, and 30-day in-hospital mortality of lactate vs the National Early Warning Score 2 (NEWS2) vs the arithmetic sum of the NEWS2 plus the numerical value of lactate (NEWS2-L).

METHODS

This was a prospective, multicentric, emergency department delivery, pragmatic cohort study. To determine the predictive capacity of lactate, we calculated the NEWS2 and NEWS2-L in adult patients (aged >18 years) transferred with high priority by ambulance to the emergency department in five hospitals of Castilla y Leon (Spain) between November 1, 2019, and September 30, 2020. The area under the receiver operating characteristic curve of each of the scales was calculated in terms of mortality for every time frame (2, 7, 14, and 30 days). We determined the cut-off point of each scale that offered highest sensitivity and specificity using the Youden index.

RESULTS

A total of 1716 participants were included, and the in-hospital mortality rates at 2, 7, 14, and 30 days were of 7.8% (134 cases), 11.6% (200 cases), 14.2% (243 cases), and 17.2% (295 cases), respectively. The best cut-off point determined in the NEWS2 was 6.5 points (sensitivity of 97% and specificity of 59%), and for lactate, the cut-off point was 3.3 mmol/L (sensitivity of 79% and specificity of 72%). Finally, the combined NEWS2-L showed a cut-off point of 11.7 (sensitivity of 86% and a specificity of 85%). The area under the receiver operating characteristic curve of the NEWS2, lactate, and NEWS2-L in the validation cohort for 2-day mortality was 0.889, 0.856, and 0.923, respectively (p<0.001 in all cases).

CONCLUSIONS

The new score generated, NEWS2-L, obtained better statistical results than its components (NEWS2 and lactate) separately.

National Early Warning Score 2: transcultural adaptation to Brazilian Portuguese

OBJECTIVE

Cross-cultural adaptation of the National Early Warning Score 2 to Brazilian Portuguese.

METHODS

A methodological study of a cross-cultural adaptation of a scale, based on the Beaton et al. framework, authorized by the Royal College of Physicians. Judges from nine Brazilian states, nurses and physicians evaluated the semantic, idiomatic, cultural, and conceptual equivalence between the original instrument and the translated versions. The nurses, working in inpatient or emergency units, conducted the pilot test, applying the final version to three case studies. Psychometric tests were used for data analysis: Content Validity Index (CVI), Kappa Coefficient, and Cronbach's Alpha.

RESULTS

The adaptation showed a mean CVI of 0.98 and perfect/almost perfect inter-rater agreement, with scores above 0.80. The consistency of the scale was 0.712.

CONCLUSION

The process of cross-cultural adaptation of the scale to Brazilian Portuguese was successful, providing Brazilian professionals with an instrument aligned with patient safety.

Effect of the First Wave of the Belgian COVID-19 Pandemic on Physician-Provided Prehospital Critical Care in the City of Antwerp (Belgium)

INTRODUCTION

There is evidence to suggest that patients delayed seeking urgent medical care during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. A delay in health-seeking behavior could increase the disease severity of patients in the prehospital setting. The combination of COVID-19-related missions and augmented disease severity in the prehospital environment could result in an increase in the number and severity of physician-staffed prehospital interventions, potentially putting a strain on this highly specialized service.

STUDY OBJECTIVE

The aim was to investigate if the COVID-19 pandemic influences the frequency of physician-staffed prehospital interventions, prehospital mortality, illness severity during prehospital interventions, and the distribution in the prehospital diagnoses.

METHODS

A retrospective, multicenter cohort study was conducted on prehospital charts from March 14, 2020 through April 30, 2020, compared to the same period in 2019, in an urban area. Recorded data included demographics, prehospital diagnosis, physiological parameters, mortality, and COVID-status. A modified National Health Service (NHS) National Early Warning Score (NEWS) was calculated for each intervention to assess for disease severity. Data were analyzed with univariate and descriptive statistics.

RESULTS

There was a 31% decrease in physician-staffed prehospital interventions during the period under investigation in 2020 as compared to 2019 (2019: 644 missions and 2020: 446 missions), with an increase in prehospital mortality (OR = 0.646; 95% CI, 0.435 - 0.959). During the study period, there was a marked decrease in the low and medium NEWS groups, respectively, with an OR of 1.366 (95% CI, 1.036 - 1.802) and 1.376 (0.987 - 1.920). A small increase was seen in the high NEWS group, with an OR of 0.804 (95% CI, 0.566 - 1.140); 2019: 80 (13.67%) and 2020: 69 (16.46%). With an overall decrease in cases in all diagnostic categories, a significant increase was observed for respiratory illness (31%; P = .004) and cardiac arrest (54%; P < .001), combined with a significant decrease for intoxications (-58%; P = .007). Due to the national test strategy at that time, a COVID-19 polymerase chain reaction (PCR) result was available in only 125 (30%) patients, of which 20 (16%) were positive.

CONCLUSION

The frequency of physician-staffed prehospital interventions decreased significantly. There was a marked reduction in interventions for lower illness severity and an increase in higher illness severity and mortality. Further investigation is needed to fully understand the reasons for these changes.

Pre-hospital suPAR, lactate and CRP measurements for decision-making: a prospective, observational study of patients presenting non-specific complaints

Background

In the pre-hospital setting, non-urgent patients with non-specific chief complaints pose assessment challenges for the emergency medical systems (EMS). Severely ill patients should be identified among these patients, and unnecessary transport to the emergency department (ED) should be avoided. Unnecessary admissions burden EDs, deplete EMS resources and can even be harmful to patients, especially elderly patients. Therefore, tools for facilitating pre-hospital decision-making are needed. They could be based on vital signs or point-of-care laboratory biomarkers. In this study, we examined whether the biomarker soluble urokinase plasminogen activator receptor (suPAR), either alone or combined with C-reactive protein (CRP) and/or lactate, could predict discharge from the ED and act as a pre-hospital support tool for non-conveyance decision-making.

Methods

This was a prospective, observational study of adult patients with normal or near-normal vital signs transported by an EMS to an ED with a code referring to deteriorated general condition. The levels of suPAR, CRP and lactate in the patients’ pre-hospital blood samples were analysed. The values of hospitalized patients were compared to those of discharged patients to determine whether these biomarkers could predict direct discharge from the ED.

Results

A total of 109 patients (median age: 81 years) were included in the study. Of those, 52% were hospitalized and 48% were discharged from the ED. No statistically significant association was found between suPAR and the ED discharge vs hospitalization outcome (OR: 1.04, 95% CI 0.97–1.13, AUROC: 0.58, 95% CI 0.47–0.69). Adding CRP (AUROC: 0.64, 95% CI 0.54–0.75) or lactate (AUROC: 0.60, 95% CI 0.49–0.71) to the regression models did not improve their diagnostic accuracy. None of the patients with a suPAR value of less than 2 ng/ml were admitted to hospital, while 64% of the patients with a suPAR value of more than 6 ng/ml were hospitalized.

Conclusion

Pre-hospital suPAR measurements alone or combined with CRP and/or lactate measurements could not predict the ED discharge or hospital admission of 109 non-urgent EMS patients with non-specific chief complaints and normal or near-normal vital signs.

Time for a prehospital-modified sequential organ failure assessment score: An ambulance-Based cohort study

BACKGROUND

To adapt the Sequential Organ Failure Assessment (SOFA) score to fit the prehospital care needs; to do that, the SOFA was modified by replacing platelets and bilirubin, by lactate, and tested this modified SOFA (mSOFA) score in its prognostic capacity to assess the mortality-risk at 2 days since the first Emergency Medical Service (EMS) contact.

METHODS

Prospective, multicentric, EMS-delivery, ambulance-based, pragmatic cohort study of adults with acute diseases, referred to two tertiary care hospitals (Spain), between January 1st and December 31st, 2020. The discriminative power of the predictive variable was assessed through a prediction model trained using the derivation cohort and evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) on the validation cohort.

RESULTS

A total of 1114 participants comprised two separated cohorts recruited from 15 ambulance stations. The 2-day mortality rate (from any cause) was 5.9% (66 cases). The predictive validity of the mSOFA score was assessed by the calculation of the AUC of ROC in the validation cohort, resulting in an AUC of 0.946 (95% CI, 0.913-0.978, p < .001), with a positive likelihood ratio was 23.3 (95% CI, 0.32-46.2).

CONCLUSIONS

Scoring systems are now a reality in prehospital care, and the mSOFA score assesses multiorgan dysfunction in a simple and agile manner either bedside or en route. Patients with acute disease and an mSOFA score greater than 6 points transferred with high priority by EMS represent a high early mortality group.

TRIAL REGISTRATION

ISRCTN48326533, Registered Octuber 312,019, Prospectively registered (doi:https://doi.org/10.1186/ISRCTN48326533).

New Early Warning Score: EMS Off-Label Use in Out-of-Hospital Patients

Background: The National Early Warning Score (NEWS) is an assessment scale of in-hospital patients’ conditions. The purpose of this study was to assess the appropriateness of a potential off-label use of NEWS by the emergency medical system (EMS) to facilitate the identification of critical patients and to trigger appropriate care in the pre-hospital setting. Methods: A single centre, longitudinal, prospective study was carried out between July and August 2020 in the EMS service of Bologna. Home patients with age ≥ 18 years old were included in the study. The exclusion criterion was the impossibility to collect all the parameters needed to measure NEWS. Results: A total of 654 patients were enrolled in the study. The recorded NEWS values increased along with the severity of dispatch priority code, the EMS return code, the emergency department triage code, and with patients’ age (r = 0.135; p = 0.001). The aggregated value of NEWS was associated with an increased risk of hospitalization (OR = 1.30 (1.17; 1.34); p < 0.0001). Conclusion: This study showed that the use of NEWS in the urgent and emergency care services can help patient assessment while not affecting EMS crew operation and might assist decision making in terms of severity-code assignment and resources utilization.

Ability of prehospital NEWS to predict 1-day and 7-day mortality is reduced in the older adult patients

BACKGROUND

National Early Warning Score (NEWS) does not include age as a parameter despite age is a significant independent risk factor of death. The aim of this study was to examine whether age has an effect on predictive performance of short-term mortality of NEWS in a prehospital setting. We also evaluated whether adding age as an additional parameter to NEWS improved its short-term mortality prediction.

METHODS

We calculated NEWS scores from retrospective prehospital electronic patient record data for patients 18 years or older with sufficient prehospital data to calculate NEWS. We used area under receiver operating characteristic (AUROC) to analyse the predictive performance of NEWS for 1 and 7 day mortalities with increasing age in three different age groups: <65 years, 65-79 years and ≥80 years. We also explored the ORs for mortality of different NEWS parameters in these age groups. We added age to NEWS as an additional parameter and evaluated its effect on predictive performance.

RESULTS

We analysed data from 35 800 ambulance calls. Predictive performance for 7-day mortality of NEWS decreased with increasing age: AUROC (95% CI) for 1-day mortality was 0.876 (0.848 to 0.904), 0.824 (0.794 to 0.854) and 0.820 (0.788 to 0.852) for first, second and third age groups, respectively. AUROC for 7-day mortality had a similar trend. Addition of age as an additional parameter to NEWS improved its ability to predict short-term mortality when assessed with continuous Net Reclassification Improvement.

CONCLUSIONS

Age should be considered as an additional parameter to NEWS, as it improved its performance in predicting short-term mortality in this prehospital cohort.

Emergency department admission and mortality of the non-transported emergency medical service patients: a cohort study from Northern Finland

OBJECTIVES

A high number of emergency medical service (EMS) patients are not transported to hospital by ambulance. Various non-transport protocols and guidelines have been implemented by different EMS providers. The present study examines subsequent tertiary care ED and hospital admission and mortality of the patients assessed and not transported by EMS in Northern Finland and evaluates the factors predicting these outcomes.

METHODS

Data from EMS missions with a registered non-transportation code during 1 January 2018-31 December 2018 were screened retrospectively. EMS charts were retrieved from a local EMS database and data concerning hospital admission and mortality were collected from the medical records of Oulu University Hospital, Oulu, Finland.

RESULTS

A total of 12 530 EMS non-transport missions were included. Of those, a total of 344 (2.7%) patients were admitted to tertiary care ED in 48 hours after the EMS contact, and 229 (1.8%) of them were further admitted to the hospital. Patients with the dispatch code 'abdominal pain', clinical presentation with fever or hyperglycaemia, physician phone consultation and a decision not to transport during night hours were associated with a higher risk of ED admission within 48 hours after EMS contact. Overall 48-hour and 30-day mortalities of non-transported patients were 0.2% (n=25) and 1.0% (n=128), respectively.

CONCLUSION

In this cohort, the rate of subsequent tertiary care ED admission and mortality in the non-transported EMS patients was low. Dispatch code abdominal pain, clinical presentation with fever or hyperglycaemia, physician phone consultation and night-hours increased the risk of ED admission within 48 hours after EMS contact.

Machine learning model predicts short-term mortality among prehospital patients: A prospective development study from Finland

Aim

To show whether adding blood glucose to the National Early Warning Score (NEWS) parameters in a machine learning model predicts 30-day mortality more precisely than the standard NEWS in a prehospital setting.

Methods

In this study, vital sign data prospectively collected from 3632 unselected prehospital patients in June 2015 were used to compare the standard NEWS to random forest models for predicting 30-day mortality. The NEWS parameters and blood glucose levels were used to develop the random forest models. Predictive performance on an unknown patient population was estimated with a ten-fold stratified cross-validation method.

Results

All NEWS parameters and blood glucose levels were reported in 2853 (79%) eligible patients. Within 30 days after contact with ambulance staff, 97 (3.4%) of the analysed patients had died. The area under the receiver operating characteristic curve for the 30-day mortality of the evaluated models was 0.682 (95% confidence interval [CI], 0.619-0.744) for the standard NEWS, 0.735 (95% CI, 0.679-0.787) for the random forest-trained NEWS parameters only and 0.758 (95% CI, 0.705-0.807) for the random forest-trained NEWS parameters and blood glucose. The models predicted secondary outcomes similarly, but adding blood glucose into the random forest model slightly improved its performance in predicting short-term mortality.

Conclusions

Among unselected prehospital patients, a machine learning model including blood glucose and NEWS parameters had a fair performance in predicting 30-day mortality.

Consensus on predictors of clinical deterioration in emergency departments: A Delphi process study

AIM

The study aim was to determine relevance and applicability of generic predictors of clinical deterioration in emergency departments based on consensus among clinicians.

METHODS

Thirty-three predictors of clinical deterioration identified from literature were assessed in a modified two-stage Delphi-process. Sixty-eight clinicians (physicians and nurses) participated in the first round and 48 in the second round; all treating hospitalized patients in Danish emergency departments, some with pre-hospital experience. The panel rated the predictors for relevance (relevant marker of clinical deterioration) and applicability (change in clinical presentation over time, generic in nature and possible to detect bedside). They rated their level of agreement on a 9-point Likert scale and were also invited to propose additional generic predictors between the rounds. New predictors suggested by more than one clinician were included in the second round along with non-consensus predictors from the first round. Final decisions of non-consensus predictors after second round were made by a research group and an impartial physician.

RESULTS

The Delphi-process resulted in 19 clinically relevant and applicable predictors based on vital signs and parameters (respiratory rate, saturation, dyspnoea, systolic blood pressure, pulse rate, abnormal electrocardiogram, altered mental state and temperature), biochemical tests (serum c-reactive protein, serum bicarbonate, serum lactate, serum pH, serum potassium, glucose, leucocyte counts and serum haemoglobin), objective clinical observations (skin conditions) and subjective clinical observations (pain reported as new or escalating, and relatives' concerns).

CONCLUSION

The Delphi-process led to consensus of 19 potential predictors of clinical deterioration widely accepted as relevant and applicable in emergency departments.

Initial Prehospital Rapid Emergency Medicine Score (REMS) as a Predictor of Patient Outcomes

Background: A standardized objective measure of prehospital patient risk of hospitalization or death is needed. The Rapid Emergency Medicine Score (REMS), a validated risk-stratification tool, has not been widely tested for prehospital use. This study's objective was to assess predictive characteristics of initial prehospital REMS for ED disposition and overall patient mortality. Methods: This retrospective analysis used linked prehospital and hospital data from the national ESO Data Collaborative. All 911 responses from 1/1/2019-12/31/2019 were included. REMS (0-26) was calculated using age and first prehospital values for: pulse rate, mean arterial pressure, respiratory rate, oxygen saturation, and Glasgow Coma Scale. Non-transports, patients <18 and cardiac arrests prior to EMS arrival were excluded. The primary outcome was ED disposition, dichotomized to discharge versus admission, transfer, or death. The secondary outcome was overall survival to discharge (ED or inpatient). Transfers and records without inpatient disposition were excluded from the secondary analysis. Predictive ability was assessed using area under the receiver operating curve (AUROC). Optimal REMS cut points were determined using test characteristic curves. Univariable logistic regression modeling was used to quantify the association between initial prehospital REMS and each outcome. Results: Of 579,505 eligible records, 94,640 (16%) were excluded due to missing data needed to calculate REMS. Overall, 62% (n = 298,223) of patients were discharged from the ED, 36% (n = 175,212) were admitted, 2% (n = 10,499) were transferred, and 0.2% (n = 931) died in the ED. A REMS of 5 or lower demonstrated optimal statistical prediction for ED discharge versus not discharged (admission/transfer/death) (AUROC: 0.68). Patients with initial prehospital REMS of 5 or lower showed a three-fold increase in odds of ED discharge (OR: 3.28, 95%CI: 3.24-3.32). Of the 457,226 patients included in overall mortality analysis, >98% (n = 450,112) survived. AUROC of initial prehospital REMS for overall mortality was 0.79. A score 7 or lower was statistically optimal for predicting survival. Initial prehospital REMS of 7 or lower was associated with a five-fold increase in odds of overall survival (OR:5.41, 95%CI:5.15-5.69). Conclusion: Initial prehospital REMS was predictive of ED disposition and overall patient mortality, suggesting value as a risk-stratification measure for EMS agencies, systems and researchers.

Random forest machine learning method outperforms prehospital National Early Warning Score for predicting one-day mortality: A retrospective study

Aim of the study

The National Early Warning Score (NEWS) is a validated method for predicting clinical deterioration in hospital wards, but its performance in prehospital settings remains controversial. Modern machine learning models may outperform traditional statistical analyses for predicting short-term mortality. Thus, we aimed to compare the mortality prediction accuracy of NEWS and random forest machine learning using prehospital vital signs.

Methods

In this retrospective study, all electronic ambulance mission reports between 2008 and 2015 in a single EMS system were collected. Adult patients (≥ 18 years) were included in the analysis. Random forest models with and without blood glucose were compared to the traditional NEWS for predicting one-day mortality. A ten-fold cross-validation method was applied to train and validate the random forest models.

Results

A total of 26,458 patients were included in the study of whom 278 (1.0%) died within one day of ambulance mission. The area under the receiver operating characteristic curve for one-day mortality was 0.836 (95% CI, 0.810−0.860) for NEWS, 0.858 (95% CI, 0.832−0.883) for a random forest trained with NEWS variables only and 0.868 (0.843−0.892) for a random forest trained with NEWS variables and blood glucose.

Conclusion

A random forest algorithm trained with NEWS variables was superior to traditional NEWS for predicting one-day mortality in adult prehospital patients, although the risk of selection bias must be acknowledged. The inclusion of blood glucose in the model further improved its predictive performance.

Pre-hospital lowest recorded oxygen saturation independently predicts death in patients with COVID-19

BACKGROUND

The coronavirus disease 2019 (COVID-19) results in hypoxia in around a fifth of adult patients. Severe hypoxia in the absence of visible respiratory distress ('silent hypoxia') is increasingly recognised in these patients. There are no published data evaluating lowest recorded pre-hospital oxygen saturation or pre-hospital National Early Warning Score 2 (NEWS2) as a predictor of outcome in patients with COVID-19.

METHODS

In this retrospective service evaluation, we included adult inpatients with laboratory confirmed COVID-19 who were discharged from hospital or who died in hospital between 12 March and 28 April 2020 (n = 143). Pre-hospital and in-hospital data were extracted and analysed to explore risk factors associated with in-hospital mortality to inform local triage and emergency management.

RESULTS

The lowest recorded pre-hospital oxygen saturation was an independent predictor of mortality when controlling for age, gender and history of COPD. A 1% reduction in pre-hospital oxygen saturation increased the odds of death by 13% (OR 1.13, p < 0.001). Lower pre-hospital oxygen saturation predicted mortality after adjusting for the pre-hospital NEWS2 (OR for a 1% reduction in pre-hospital oxygen saturation 1.09, p = 0.02). The pre-hospital NEWS2 was higher in those who died (Median 9; IQR 7-10; n = 24) than in those who survived to discharge (Median 6; IQR 5-8; n = 63).

CONCLUSION

This service evaluation suggests that the lowest recorded pre-hospital oxygen saturation may be an independent predictor of mortality in COVID-19 patients. Lowest pre-hospital oxygen saturation should be recorded and used in the assessment of patients with suspected COVID-19 in pre-hospital and emergency department triage settings.

Institution-Specific Machine Learning Models for Prehospital Assessment to Predict Hospital Admission: Prediction Model Development Study

Background

Although multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.

Objective

We aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.

Methods

Patients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.

Results

Of 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.

Conclusions

For patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

Can a screening tool safely identify low risk cardiac patients to be transported with primary care flight paramedics?

OBJECTIVES

We aimed to determine the rate of adverse events during interfacility transport of cardiac patients identified as low risk by a consensus-derived screening tool and transported by primary care flight paramedics (PCP(f)).

METHODS

We conducted a health records review of adult patients diagnosed with a cardiac condition who were identified as low risk by the screening tool and transported by PCP(f). We excluded patients transported by an advanced care crew, those accompanied by a clinical escort from hospital, and those transported from a scene call, by rotary wing or ground vehicle. We recorded patient and transportation parameters using a piloted-standardized collection tool. We defined adverse events during transport a priori. We report descriptive statistics using mean (standard deviation), [range], (percentage).

RESULTS

We included 400 patients: mean age 66.9 years old, 66.5% male. Mean transport duration was 136.2 (74.9) minutes. Most common comorbidities were hypertension (50.3%) and coronary artery disease (39.5%). Most transports originated out of Northern Ontario and were for cardiac catheterization (61.8%) or coronary artery bypass grafting (26.8%). Overall, the adverse event rate was low (0.3%), with no serious event such as cardiac arrest, death, or airway intervention.

CONCLUSIONS

A screening tool can identify cardiac patients at low risk for clinical deterioration during air-medical transport. We believe patients screened with this tool can be transported safely by a PCP(f) crew, leading to potentially significant resource savings.

The Value of Prehospital Early Warning Scores to Predict in - Hospital Clinical Deterioration: A Multicenter, Observational Base-Ambulance Study

OBJECTIVES

Early warning scores are clinical tools capable of identifying prehospital patients with high risk of deterioration. We sought here to contrast the validity of seven early warning scores in the prehospital setting and specifically, to evaluate the predictive value of each score to determine early deterioration-risk during the hospital stay, including mortality at one, two, three and seven- days since the index event. Methods: A prospective multicenter observational based-ambulance study of patients treated by six advanced life support emergency services and transferred to five Spanish hospitals between October 1, 2018 and December 31, 2019. We collected demographic, clinical, and laboratory variables. Seven risk score were constructed based on the analysis of prehospital variables associated with death within one, two, three and seven days since the index event. The area under the receiver operating characteristics was used to determine the discriminant validity of each early warning score. Results: A total of 3,273 participants with acute diseases were accurately linked. The median age was 69 years (IQR, 54-81 years), 1,348 (41.1%) were females. The overall mortality rate for patients in the study cohort ranged from 3.5% for first-day mortality (114 cases), to 7% for seven-day mortality (228 cases). The scores with the best performances for one-day mortality were Vitalpac Early Warning Score with an area under the receiver operating characteristic (AUROC) of 0.873 (95% CI: 0.81-0.9), for two-day mortality, Triage Early Warning Score with an AUROC of 0.868 (95% CI: 0.83-0.9), for three and seven-days mortality the Modified Rapid Emergency Medicine Score with an AUROC of 0.857 (0.82-0.89) and 0.833 (95% CI: 0.8-0.86). In general, there were no significant differences between the scores analyzed. Conclusions: All the analyzed scores have a good predictive capacity for early mortality, and no statistically significant differences between them were found. The National Early Warning Score 2, at the clinical level, has certain advantages. Early warning scores are clinical tools that can help in the complex decision-making processes during critical moments, so their use should be generalized in all emergency medical services.

Pre-hospital triage performance and emergency medical services nurse's field assessment in an unselected patient population attended to by the emergency medical services: a prospective observational study

BACKGROUND

In Sweden, the rapid emergency triage and treatment system (RETTS-A) is used in the pre-hospital setting. With RETTS-A, patients triaged to the lowest level could safely be referred to a lower level of care. The national early warning score (NEWS) has also shown promising results internationally. However, a knowledge gap in optimal triage in the pre-hospital setting persists. This study aimed to evaluate RETTS-A performance, compare RETTS-A with NEWS and NEWS 2, and evaluate the emergency medical service (EMS) nurse's field assessment with the physician's final hospital diagnosis.

METHODS

A prospective, observational study including patients (≥16 years old) transported to hospital by the Gothenburg EMS in 2016. Three comparisons were made: 1) Combined RETTS-A levels orange and red (high acuity) compared to a predefined reference emergency, 2) RETTS-A high acuity compared to NEWS and NEWS 2 score ≥ 5, and 3) Classification of pre-hospital nurse's field assessment compared to hospital physician's diagnosis. Outcomes of the time-sensitive conditions, mortality and hospitalisation were examined. The statistical tests included Mann-Whitney U test and Fisher's exact test, and several binary classification tests were determined.

RESULTS

Overall, 4465 patients were included (median age 69 years; 52% women). High acuity RETTS-A triage showed a sensitivity of 81% in prediction of the reference patient with a specificity of 64%. Sensitivity in detecting a time-sensitive condition was highest with RETTS-A (73%), compared with NEWS (37%) and NEWS 2 (35%), and specificity was highest with NEWS 2 (83%) when compared with RETTS-A (54%). The negative predictive value was higher in RETTS-A (94%) compared to NEWS (91%) and NEWS 2 (92%). Eleven per cent of the final diagnoses were classified as time-sensitive while the nurse's field assessment was appropriate in 84% of these cases.

CONCLUSIONS

In the pre-hospital triage of EMS patients, RETTS-A showed sensitivity that was twice as high as that of both NEWS and NEWS 2 in detecting time-sensitive conditions, at the expense of lower specificity. However, the proportion of correctly classified low risk triaged patients (green/yellow) was higher in RETTS-A. The nurse's field assessment of time-sensitive conditions was appropriate in the majority of cases.

Accuracy of early warning scores for predicting serious adverse events in pre-hospital traumatic injury

BACKGROUND

Traumatically injured patients are at higher risk of serious adverse events. Numerous physiological scoring systems are employed as diagnostic and/or prognostic tools. The objective of this study was to evaluate the scales most commonly used by emergency medical services for the early detection of prehospital serious adverse events.

METHODS

Design. Preliminary longitudinal prospective observational study without intervention study in adults with prehospital traumatic injury.

SETTING

The study was carried out in the public health system of Castile and León (Spain), from April 1, 2018 to October 31, 2019, involving seven advanced life support units and five hospitals.

PARTICIPANTS

Traumatically injured patients over 18 years of age who were stabilized and transferred in advanced life support units to their referral hospital.

MAIN OUTCOME MEASURES

Appearance of serious adverse events at the prehospital level at the scene or during the transfer to the emergency department.

RESULTS

A total of 346 patients were included in the study. The median age was 50 years (IQR: 38-65). 32 cases (7.8%) presented serious adverse events at the prehospital level. Areas under the curve for the detection of serious adverse events were obtained with the Prehospital Index (0.979; 95% CI: 0.94-1.00) and National Early Warning Score 2 (0.956; 95% CI: 0.90-1.00); p <0.001 for all scores. The Prehospital Index had a positive probability coefficient of 78.4 (95% CI: 62.8-68.6) and the National Early Warning Score 2 obtained 52.9 (95% CI: 39.7-65.6). A comparison of the curves was not significant for any of the scores studied (p> 0.05).

CONCLUSIONS

All scoring systems were able to detect prehospital serious adverse events early in traumatic injury; therefore, any of the scoring systems could be useful and represent an ideal tool for routine use by emergency medical services in cases of traumatic injury.

Changing role of EMS -analyses of non-conveyed and conveyed patients in Finland

Background

Emergency Medical Services (EMS) and Emergency Departments (ED) have seen increasing attendance rates in the last decades. Currently, EMS are increasingly assessing and treating patients without the need to convey patients to health care facility. The aim of this study was to describe and compare the patient case-mix between conveyed and non-conveyed patients and to analyze factors related to non-conveyance decision making.

Methods

This was a prospective study design of EMS patients in Finland, and data was collected between 1st June and 30th November 2018. Adjusted ICPC2-classification was used as the reason for care. NEWS2-points were collected and analyzed both statistically and with a semi-supervised information extraction method. EMS patients’ geographic location and distance to health care facilities were analyzed by urban–rural classification.

Results

Of the EMS patients (40,263), 59.8% were over 65 years of age and 46.0% of the patients had zero NEWS2 points. The most common ICPC2 code was weakness/tiredness, general (A04), as seen in 13.5% of all patients. When comparing patients between the non-conveyance and conveyance group, a total of 35,454 EMS patients met the inclusion criteria and 14,874 patients (42.0%) were not conveyed to health care facilities. According the multivariable logistic regression model, the non-conveyance decision was more likely made by ALS units, when the EMS arrival time was in the evening or night and when the distance to the health care facility was 21-40 km. Furthermore, younger patients, female gender, whether the patient had used alcohol and a rural area were also related to the non-conveyance decision. If the patient’s NEWS2 score increased by one or two points, the likelihood of conveyance increased. When there was less than 1 h to complete a shift, this did not associate with either non-conveyance or conveyance decisions.

Conclusions

The role of EMS might be changing. This warrants to redesign the chain-of-survival in EMS to include not only high-risk patient groups but also non-critical and general acute patients with non-specific reasons for care. Assessment and on-scene treatment without conveyance can be called the “stretched arm of the emergency department”, but should be planned carefully to ensure patient safety.

Association between National Early Warning Scores in primary care and clinical outcomes: an observational study in UK primary and secondary care

Background

NHS England has mandated use of the National Early Warning Score (NEWS), more recently NEWS2, in acute settings, and suggested its use in primary care. However, there is reluctance from GPs to adopt NEWS/NEWS2.

Aim

To assess whether NEWS calculated at the point of GP referral into hospital is associated with outcomes in secondary care.

Design and setting

An observational study using routinely collected data from primary and secondary care.

Method

NEWS values were prospectively collected for 13 047 GP referrals into acute care between July 2017 and December 2018. NEWS values were examined and multivariate linear and logistic regression used to assess associations with process measures and clinical outcomes.

Results

Higher NEWS values were associated with faster conveyance for patients travelling by ambulance, for example, median 94 minutes (interquartile range [IQR] 69–139) for NEWS ≥7; median 132 minutes, (IQR 84–236) for NEWS = 0 to 2); faster time from hospital arrival to medical review (54 minutes [IQR 25–114] for NEWS ≥7; 78 minutes [IQR 34–158] for NEWS = 0 to 2); as well as increased length of stay (5 days [IQR 2–11] versus 1 day [IQR 0–5]); intensive care unit admissions (2.0% versus 0.5%); sepsis diagnosis (11.7% versus 2.5%); and mortality, for example, 30-day mortality 12.0% versus 4.1% for NEWS ≥7 versus NEWS = 0 to 2, respectively. On average, for patients referred without a NEWS value (NEWS = NR), most clinical outcomes were comparable with patients with NEWS = 3 to 4, but ambulance conveyance time and time to medical review were comparable with patients with NEWS = 0 to 2.

Conclusion

This study has demonstrated that higher NEWS values calculated at GP referral into hospital are associated with a faster medical review and poorer clinical outcomes.

Implementation of the National Early Warning Score in patients with suspicion of sepsis: evaluation of a system-wide quality improvement project

Background

The National Early Warning Score (NEWS) was introduced to standardise early warning scores (EWS) in England. It has been recommended that NEWS should be used in pre-hospital care but there is no published evidence that this improves outcomes. In 2015, the West of England Academic Health Science Network region standardised to NEWS across all healthcare settings. Calculation of NEWS was recommended for acutely unwell patients at referral into secondary care.

Aim

To evaluate whether implementation of NEWS across a healthcare system affects outcomes, specifically addressing the effect on mortality in patients with suspicion of sepsis (SOS).

Design and setting

A quality improvement project undertaken across the West of England from March 2015 to March 2019, with the aim of standardising to NEWS in secondary care and introducing NEWS into community and primary care.

Method

Data from the national dashboard for SOS for the West of England were examined over time and compared to the rest of England. Quality improvement methodology and statistical process control charts were used to measure improvement.

Results

There was a reduction in mortality in the SOS cohort in the West of England, which was not seen in the rest of England over the time period of the project. Admissions did not increase. By March 2019, the West of England had the lowest mortality in the SOS cohort in England.

Conclusion

To the authors’ knowledge, this is the first study demonstrating that use of NEWS in pre-hospital care is associated with improved outcomes in patients with SOS.

Prehospital Point-Of-Care Lactate Increases the Prognostic Accuracy of National Early Warning Score 2 for Early Risk Stratification of Mortality: Results of a Multicenter, Observational Study

The objective of this study was to assess whether the use of prehospital lactate (pLA) can increase the prognostic accuracy of the National Early Warning Score 2 (NEWS2) to detect the risk of death within 48 h. A prospective, multicenter study in adults treated consecutively by the emergency medical services (EMS) included six advanced life support (ALS) services and five hospitals. Patients were assigned to one of four groups according to their risk of mortality (low, low-medium, medium, and high), as determined by the NEWS2 score. For each group, the validity of pLA in our cohort was assessed by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. In this study, 3081 participants with a median age of 69 years (Interquartile range (IQR): 54-81) were included. The two-day mortality was 4.4% (137 cases). The scale derived from the implementation of the pLA improved the capacity of the NEWS2 to discriminate low risk of mortality, with an AUC of 0.910 (95% CI: 0.87-0.94; p < 0.001). The risk stratification provided by the NEWS2 can be improved by incorporating pLA measurement to more accurately predict the risk of mortality in patients with low risk.

Can the prehospital National Early Warning Score 2 identify patients at risk of in-hospital early mortality? A prospective, multicenter cohort study

BACKGROUND

The National Early Warning Score 2 (NEWS2) scores can help identify clinical deterioration.

OBJECTIVE

To assess the predictive capacity of the NEWS2 at prehospital level for the detection of early mortality in the hospital.

METHODS

Prospective multicenter cohort study, in which we compiled a database of observed vital signs between March 1, 2018 and May 30, 2019. We collected demographic data, vital signs (respiration rate, oxygen saturation, supplemental oxygen, temperature, systolic blood pressure, heart rate and level of consciousness), prehospital diagnosis and hospital mortality data. We calculated the AUROC of the NEWS2 for early mortality.

RESULTS

We included a total of 2335 participants. Median age was 69 years (IQR 54-81 years). The AUC for mortality within one day was 0.862 (95%CI:0.78-0.93), within two days 0.885 (95%CI:0.84-0.92) and within seven days 0.835 (95%CI:0.79-0.87) (in all cases, p<0.001).

CONCLUSIONS

The NEWS2 performed at prehospital level is a bedside tool for predicting early hospital mortality.

Identification of Serious Adverse Events in Patients with Traumatic Brain Injuries, from Prehospital Care to Intensive-Care Unit, Using Early Warning Scores

Traumatic brain injuries are complex situations in which the emergency medical services must quickly determine the risk of deterioration using minimal diagnostic methods. The aim of this study is to analyze whether the use of early warning scores can help with decision-making in these dynamic situations by determining the patients who need the intensive care unit. A prospective, multicentric cohort study without intervention was carried out on traumatic brain injury patients aged over 18 given advanced life support and taken to the hospital. Our study included a total of 209 cases. The total number of intensive-care unit admissions was 50 cases (23.9%). Of the scores analyzed, the National Early Warning Score2 was the best result presented with an area under the curve of 0.888 (0.81–0.94; p < 0.001) and an odds ratio of 25.4 (95% confidence interval (CI):11.2–57.5). The use of early warning scores (and specifically National Early Warning Score2) can help the emergency medical services to differentiate traumatic brain injury patients with a high risk of deterioration. The emergency medical services should use the early warning scores routinely in all cases for the early detection of high-risk situations.

A validation of machine learning-based risk scores in the prehospital setting

BACKGROUND

The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

METHODS

Dispatch, ambulance, and hospital data were collected in one Swedish region from 2016-2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.

RESULTS

A total of 38203 patients were included from 2016-2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51-0.66, while those for NEWS ranged from 0.66-0.85. Concordance ranged from 0.70-0.79 for risk scores based only on dispatch data, and 0.79-0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.

CONCLUSIONS

Machine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.

Comparison of the National Early Warning Score (NEWS) and the Modified Early Warning Score (MEWS) for predicting admission and in-hospital mortality in elderly patients in the pre-hospital setting and in the emergency department

The aim of this study is to evaluate the usefulness of the pre-hospital National Early Warning Score (pNEWS) and the pre-hospital Modified Early Warning Score (pMEWS) for predicting admission and in-hospital mortality in elderly patients presenting to the emergency department (ED). We also compare the value of the pNEWS with that of the ED NEWS (eNEWS) and ED MEWS (eMEWS) for predicting admission and in-hospital mortality. This retrospective, single-centre observational study was carried out in the ED of Jikei University Kashiwa Hospital, in Chiba, Japan, from 1st April 2017 to 31st March 2018. All patients aged 65 years or older were included in this study. The pNEWS/eNEWS were derived from seven common physiological vital signs: respiratory rate, peripheral oxygen saturation, the presence of inhaled oxygen parameters, body temperature, systolic blood pressure, pulse rate and Alert, responds to Voice, responds to Pain, Unresponsive (AVPU) score, whereas the pMEWS/eMEWS were derived from six common physiological vital signs: respiratory rate, peripheral oxygen saturation, body temperature, systolic blood pressure, pulse rate and AVPU score. Discrimination was assessed by plotting the receiver operating characteristic (ROC) curve and calculating the area under the ROC curve (AUC). The median pNEWS, pMEWS, eNEWS and eMEWS were significantly higher at admission than at discharge (p < 0.001). The median pNEWS, pMEWS, eNEWS and eMEWS of non-survivors were significantly higher than those of the survivors (p < 0.001). The AUC for predicting admission was 0.559 for the pNEWS and 0.547 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting admission (p = 0.102). The AUCs for predicting in-hospital mortality were 0.678 for the pNEWS and 0.652 for the pMEWS. There was no significant difference between the AUCs of the pNEWS and the pMEWS for predicting in-hospital mortality (p = 0.081). The AUC for predicting admission was 0.628 for the eNEWS and 0.591 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting admission (p < 0.001). The AUC for predicting in-hospital mortality was 0.789 for the eNEWS and 0.720 for the eMEWS. The AUC of the eNEWS was significantly greater than that of the eMEWS for predicting in-hospital mortality (p < 0.001). For admission and in-hospital mortality, the AUC of the eNEWS was significantly greater than that of the pNEWS (p < 0.001, p < 0.001), and the AUC of the eMEWS was significantly greater than that of the pMEWS (p < 0.01, p < 0.05). Our single-centre study has demonstrated the low utility of the pNEWS and the pMEWS as predictors of admission and in-hospital mortality in elderly patients, whereas the eNEWS and the eMEWS predicted admission and in-hospital mortality more accurately. Evidence from multicentre studies is needed before introducing pre-hospital versions of risk-scoring systems.