Prehospital factors associated with an acute life-threatening condition in non-traumatic chest pain patients - A systematic review

Prehospital acute life-threatening cardiovascular disease in elderly: an observational, prospective, multicentre, ambulance-based cohort study

OBJECTIVE

The aim was to explore the association of demographic and prehospital parameters with short-term and long-term mortality in acute life-threatening cardiovascular disease by using a hazard model, focusing on elderly individuals, by comparing patients under 75 years versus patients over 75 years of age.

DESIGN

Prospective, multicentre, observational study.

SETTING

Emergency medical services (EMS) delivery study gathering data from two back-to-back studies between 1 October 2019 and 30 November 2021. Six advanced life support (ALS), 43 basic life support and five hospitals in Spain were considered.

PARTICIPANTS

Adult patients suffering from acute life-threatening cardiovascular disease attended by the EMS.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome was in-hospital mortality from any cause within the first to the 365 days following EMS attendance. The main measures included prehospital demographics, biochemical variables, prehospital ALS techniques used and syndromic suspected conditions.

RESULTS

A total of 1744 patients fulfilled the inclusion criteria. The 365-day cumulative mortality in the elderly amounted to 26.1% (229 cases) versus 11.6% (11.6%) in patients under 75 years old. Elderly patients (≥75 years) presented a twofold risk of mortality compared with patients ≤74 years. Life-threatening interventions (mechanical ventilation, cardioversion and defibrillation) were also related to a twofold increased risk of mortality. Importantly, patients suffering from acute heart failure presented a more than twofold increased risk of mortality.

CONCLUSIONS

This study revealed the prehospital variables associated with the long-term mortality of patients suffering from acute cardiovascular disease. Our results provide important insights for the development of specific codes or scores for cardiovascular diseases to facilitate the risk of mortality characterisation.

Factors Associated with Symptom-to-Door Delay in Patients with ST-Segment Myocardial Infarction: A Systematic Review

BACKGROUND

Decreasing symptom-to-door (S2D) delay is of vital importance for reducing morbidity and mortality in patients with ST-segment elevation myocardial infarction (STEMI). The factors associated with S2D delay in STEMI patients have not been well-characterized.

OBJECTIVES

The aim of this study was to identify factors associated with S2D delay in patients with STEMI.

METHODS

The PubMed, CINAHL, and Embase databases were searched for data. References from the selected articles and relevant background papers were also manually searched to identify additional eligible studies. The included articles were reviewed and assessed for risk of bias. The level of evidence for each identified factor was evaluated using a semiquantitative synthesis.

RESULTS

Twelve (12) papers were included in the review. Factors associated with S2D delay were complex and could be divided into sociodemographic, clinical history, and onset characteristics. The level of evidence regarding female sex and diabetes was strong, and the evidence was moderate regarding older age, smoking, history of hypertension, self-transport, or referral.

CONCLUSIONS

Female sex, older age, previous diabetes, previous hypertension, smoking, and self-transport are all strong or moderate risk factors for S2D time delay in patients with ST-segment myocardial infarction. More efforts should be made to educate at-risk populations concerning symptoms of STEMI and the importance of seeking early medical assistance.

Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study

BACKGROUND

This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an integrated nationwide dataset, and to assess the performance of this model.

METHODS

We integrated three nationwide datasets and developed a two-step prediction model utilizing a machine learning algorithm. Ninety-eight clinical characteristics of patients identified at the pre-hospital stage and 13 hospital components were used as input data for the model. The primary endpoint of the model was the prediction of transfer to an inappropriate hospital.

RESULTS

A total of 94,256 transferred patients in the public pre-hospital care system matched the National Emergency Department Information System data of patients with a pre-hospital cardiovascular registry created in South Korea between July 2017 and December 2018. Of these, 1,770 (6.26%) patients failed to be transferred to a capable hospital. The area under the receiver operating characteristic curve of the final predictive model was 0.813 (0.800-0.825), and the area under the receiver precision-recall curve was 0.286 (0.265-0.308).

CONCLUSIONS

Our prediction model used machine learning to show favorable performance in transferring patients with suspected cardiovascular disease to a capable hospital. For our results to lead to changes in the pre-hospital care system, a digital platform for sharing real-time information should be developed.

Metabolomics of early myocardial ischemia

INTRODUCTION

Diagnosing myocardial infarction is difficult during the initial phase. As, acute myocardial ischemia is associated with changes in metabolic pathways, metabolomics may provide ways of identifying early stages of ischemia. We investigated the changes in metabolites after induced ischemia in humans using nuclear magnetic resonance spectroscopy (NMR).

METHODS

We included patients undergoing elective coronary angiography showing normal coronary arteries. These were randomized into 4 groups and underwent coronary artery occlusion for 0, 30, 60 or 90 s. Blood was collected over the next 3 h and analyzed using NMR. We used 2-way ANOVA of time from baseline- and treatment group to find metabolites that changed significantly following the intervention and principal component analysis (PCA) to investigate changes between the 90 s ischemia- and control groups at 15 and 60 min after intervention.

RESULTS

We included 34 patients. The most pronounced changes were observed in the lipid metabolism where 38 of 112 lipoprotein parameters (34%) showed a significant difference between the patients exposed to ischemia and the control group. There was a decrease in total plasma triglycerides over the first hour followed by a normalization. The principal component analysis showed a effects of the treatment after just 15 min. These effects were dominated by changes in high-density lipoprotein. An increase in lactic acid levels was detected surprisingly late, 1-2 h after the ischemia.

CONCLUSION

We investigated the earliest changes in metabolites of patients undergoing brief myocardial ischemia and found that ischemia led to changes throughout the lipid metabolism as early as 15 min post-intervention.

Development of a prehospital prediction model for risk stratification of patients with chest pain

INTRODUCTION

Chest pain is one of the most common reasons for contacting the emergency medical services (EMS). About 15% of these chest pain patients have a high-risk condition, while many of them have a low-risk condition with no need for acute hospital care. It is challenging to at an early stage distinguish whether patients have a low- or high-risk condition. The objective of this study has been to develop prediction models for optimising the identification of patients with low- respectively high-risk conditions in acute chest pain early in the EMS work flow.

METHODS

This prospective observational cohort study included 2578 EMS missions concerning patients who contacted the EMS in a Swedish region due to chest pain in 2018. All the patients were assessed as having a low-, intermediate- or high-risk condition, i.e. occurrence of a time-sensitive diagnosis at discharge from hospital. Multivariate regression analyses using data on symptoms and symptom onset, clinical findings including ECG, previous medical history and Troponin T were carried out to develop models for identification of patients with low- respectively high-risk conditions. Developed models where then tested hold-out data set for internal validation and assessing their accuracy.

RESULTS

Prediction models for risk-stratification based on variables mutual for both low- and high-risk prediction were developed. The variables included were: age, sex, previous medical history of kidney disease, atrial fibrillation or heart failure, Troponin T, ST-depression on ECG, paleness, pain debut during activity, constant pain, pain in right arm and pressuring pain quality. The high-risk model had an area under the receiving operating characteristic curve of 0.85 and the corresponding figure for the low-risk model was 0.78.

CONCLUSIONS

Models based on readily available information in the EMS setting can identify high- and low-risk conditions with acceptable accuracy. A clinical decision support tool based on developed models may provide valuable clinical guidance and facilitate referral to less resource-intensive venues.

Prehospital stratification in acute chest pain patient into high risk and low risk by emergency medical service: a prospective cohort study

OBJECTIVES

To describe contemporary characteristics and diagnoses in prehospital patients with chest pain and to identify factors suitable for the early recognition of high-risk and low-risk conditions.

DESIGN

Prospective observational cohort study.

SETTING

Two centre study in a Swedish county emergency medical services (EMS) organisation.

PARTICIPANTS

Unselected inclusion of 2917 patients with chest pain contacting the EMS due to chest pain during 2018.

PRIMARY OUTCOME MEASURES

Low-risk or high-risk condition, that is, occurrence of time-sensitive diagnosis on hospital discharge.

RESULTS

Of included EMS missions, 68% concerned patients with a low-risk condition without medical need of acute hospital treatment in hindsight. Sixteen per cent concerned patients with a high-risk condition in need of rapid transport to hospital care. Numerous variables with significant association with low-risk or high-risk conditions were found. In total high-risk and low-risk prediction models shared six predictive variables of which ST-depression on ECG and age were most important. Previously known risk factors such as history of acute coronary syndrome, diabetes and hypertension had no predictive value in the multivariate analyses. Some aspects of the symptoms such as pain intensity, pain in the right arm and paleness did on the other hand appear to be helpful. The area under the curve (AUC) for prediction of low-risk candidates was 0.786 and for high-risk candidates 0.796. The addition of troponin in a subset increased the AUC to >0.8 for both.

CONCLUSIONS

A majority of patients with chest pain cared for by the EMS suffer from a low-risk condition and have no prognostic reason for acute hospital care given their diagnosis on hospital discharge. A smaller proportion has a high-risk condition and is in need of prompt specialist care. Building models with good accuracy for prehospital identification of these groups is possible. The use of risk stratification models could make a more personalised care possible with increased patient safety.

The Prognostic Value of Prehospital Blood Lactate Levels to Predict Early Mortality in Acute Cardiovascular Disease

INTRODUCTION

The knowledge of the prognostic value of prehospital lactate (PLA) is limited. Our objective was to evaluate the predictive capacity of PLA to predict early mortality (within 48 h) from the index event in acute cardiovascular disease (ACVD).

METHODS

Prospective, longitudinal, multicenter, observational study in patients, attended by advanced life support units, transferred to the emergency department of their reference hospital and diagnosed with ACVD. We collected demographic, physiological, clinical, analytical variables, main cardiological diagnosis, and data on hospital admission and early mortality. The main outcome variable was mortality from any cause within 2 days.

RESULTS

Between March 1, 2018 and January 31, 2019, a total of 492 patients were included in our study. Early mortality after the index event within the first 48 h affected 27 patients (5.5%). The most frequent cause of care demand was chest pain with 223 cases (45.3%). The predictive power of PLA to discriminate mortality at 2 days obtained an area under the curve of 0.911 (95% confidence interval [CI]: 0.83-0.98, P < 0.001). A value equal or superior to 4.3 mmol/L globally attained a sensitivity of 92.6% (95% CI, 76.6-97.9) with a specificity of 82.8% (95% CI, 79.1-86.0), with a positive predictive value of 23.8 (16.7-32.8) and a negative predictive value of 99.5 (98.1-99.9).

CONCLUSION

PLA represents a prognostic biomarker with excellent predictive capacity for ACVD. Prehospital Emergency Services (PhEMS) should incorporate this test routinely in their usual procedures.

Pre-hospital predictors of an adverse outcome among patients with dyspnoea as the main symptom assessed by pre-hospital emergency nurses - a retrospective observational study

Background

Dyspnoea is one of the most common reasons for patients contacting emergency medical services (EMS). Pre-hospital Emergency Nurses (PENs) are independently responsible for advanced care and to meet these patients individual needs. Patients with dyspnoea constitute a complex group, with multiple different final diagnoses and with a high risk of death. This study aimed to describe on-scene factors associated with an increased risk of a time-sensitive final diagnosis and the risk of death.

Methods

A retrospective observational study including patients aged ≥16 years, presenting mainly with dyspnoea was conducted. Patients were identified thorough an EMS database, and were assessed by PENs in the south-western part of Sweden during January to December 2017. Of 7260 missions (9% of all primary missions), 6354 were included. Among those, 4587 patients were randomly selected in conjunction with adjusting for unique patients with single occasions. Data were manually collected through both EMS- and hospital records and final diagnoses were determined through the final diagnoses verified in hospital records. Analysis was performed using multiple logistic regression and multiple imputations.

Results

Among all unique patients with dyspnoea as the main symptom, 13% had a time-sensitive final diagnosis. The three most frequent final time-sensitive diagnoses were cardiac diseases (4.1% of all diagnoses), infectious/inflammatory diseases (2.6%), and vascular diseases (2.4%). A history of hypertension, renal disease, symptoms of pain, abnormal respiratory rate, impaired consciousness, a pathologic ECG and a short delay until call for EMS were associated with an increased risk of a time-sensitive final diagnosis. Among patients with time-sensitive diagnoses, approximately 27% died within 30 days. Increasing age, a history of renal disease, cancer, low systolic blood pressures, impaired consciousness and abnormal body temperature were associated with an increased risk of death.

Conclusions

Among patients with dyspnoea as the main symptom, age, previous medical history, deviating vital signs, ECG pattern, symptoms of pain, and a short delay until call for EMS are important factors to consider in the prehospital assessment of the combined risk of either having a time-sensitive diagnosis or death.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12873-020-00384-1.

Optimisation of telephone triage of callers with symptoms suggestive of acute cardiovascular disease in out-of-hours primary care: observational design of the Safety First study

INTRODUCTION

In the Netherlands, the 'Netherlands Triage Standard' (NTS) is frequently used as digital decision support system for telephone triage at out-of-hours services in primary care (OHS-PC). The aim of the NTS is to guarantee accessible, efficient and safe care. However, there are indications that current triage is inefficient, with overestimation of urgency, notably in suspected acute cardiovascular disease. In addition, in primary care settings the NTS has only been validated against surrogate markers, and diagnostic accuracy with clinical outcomes as the reference is unknown. In the Safety First study, we address this gap in knowledge by describing, understanding and improving the diagnostic process and urgency allocation in callers with symptoms suggestive of acute cardiovascular disease, in order to improve both efficiency and safety of telephone triage in this domain.

METHODS AND ANALYSIS

An observational study in which 3000 telephone triage recordings (period 2014-2016) will be analysed. Information is collected from the recordings including caller and symptom characteristics and urgency allocation. The callers' own general practitioners are contacted for the final diagnosis of each contact. We included recordings of callers with symptoms suggestive of acute coronary syndrome (ACS) or transient ischaemic attack (TIA)/stroke. With univariable and multivariable logistic regression analyses the diagnostic accuracy of caller and symptom characteristics will be analysed in terms of predictive values with urgency level, and ACS and TIA/stroke as outcomes, respectively. To further improve our understanding of the triage process at OHS-PC, we will carry out additional studies applying both quantitative and qualitative methods: (i) case-control study on serious adverse events (SAE), (ii) conversation analysis study and (iii) interview study with triage nurses.

ETHICS AND DISSEMINATION

The Medical Ethics Committee Utrecht, the Netherlands endorsed this study (National Trial Register identification: NTR7331). Results will be disseminated at scientific conferences, regional educational sessions and publication in peer-reviewed journals.

Symptom description in patients with chest pain-A qualitative analysis of emergency medical calls involving high-risk conditions

AIMS AND OBJECTIVES

To explore the symptoms descriptions and situational information provided by patients during ongoing chest pain events caused by a high-risk condition.

BACKGROUND

Chest pain is a common symptom in patients contacting emergency dispatch centres. Only 15% of these patients are later classified as suffering from a high-risk condition. Prehospital personnel are largely dependent on symptom characteristics when trying to identify these patients.

DESIGN

Qualitative descriptive.

METHODS

Manifest content analysis of 56 emergency medical calls involving patients with chest pain was carried out. A stratified purposive sampling was used to obtain calls concerning patients with high-risk conditions. These calls were then listened to and transcribed. Thereafter, meaning units were identified and coded and finally categorised. Consolidated criteria for reporting qualitative studies guidelines have been applied.

RESULTS

A wide range of situational information and symptoms descriptions was found. Pain and affected breathing were dominating aspects, but other situational information and several other symptoms were also reported. The situational information and these symptoms were classified into seven categories: Pain narrative, Affected breathing, Bodily reactions, Time, Bodily whereabouts, Fear and concern and Situation management. The seven categories consisted of 17 subcategories.

CONCLUSIONS

Patients with chest pain caused by a high-risk condition present a wide range of symptoms which are described in a variety of ways. They describe different kinds of chest pain accompanied by pain from other parts of the body. Breathing difficulties and bodily reactions such as muscle weakness are also reported. The variety of symptoms and the absence of a typical symptomatology make risk stratification on the basis of symptoms alone difficult.

RELEVANCE TO CLINICAL PRACTICE

This study highlights the importance of an open mind when assessing patients with chest pain and the requirement of a decision support tool in order to improve risk stratification in these patients.

Management of acute coronary syndrome in emergency departments: a cross sectional multicenter study (Tunisia)

Background

We aimed to describe diagnosed acute coronary syndrome (ACS) and its care management and outcomes in emergency departments (EDs) and to determine related cardiovascular risk factors (CVRFs).

Methods

We conducted a cross sectional multicenter study that included 1173 adults admitted to EDs for acute chest pain (ACP) in 2015 at 14 sites in Tunisia. Data included patients’ baseline characteristics, diagnosis, treatment and output.

Results

ACS represented 49.7% of non-traumatic chest pain [95% CI: 46.7–52.6]; 74.2% of ACS cases were unstable angina/non-ST-segment-elevation myocardial infarction (UA/NSTEMI). Males represented 67.4% of patients with ACS (p < 0.001). The median age was 60 years (IQR 52–70). Emergency medical service transportation was used in 11.9% of cases. The median duration between chest pain onset and ED arrival was two hours (Inter quartile ranges (IQR) 2–4 h). The age-standardized prevalence rate was 69.9/100,000 PY; the rate was 96.24 in men and 43.7 in women. In the multivariable analysis, CVRFs related to ST segment elevation myocardial infarction were age correlated to sex and active smoking. CVRFs related to UA/NSTEMI were age correlated to sex, familial and personal vascular history and type 2 diabetes. We reported 27 cases of major adverse cardiovascular events (20.0%) in patients with STEMI and 36 in patients with UA/NSTEMI (9.1%).

Conclusion

Half of the patients consulting EDs with ACP had ACS. Emergency medical service transportation calls were rare. Management delays were acceptable. The risk of developing an UA/NSTEMI was equal to the number of CVRFs + 1. To improve patient outcomes, it is necessary to increase adherence to international management guidelines.

Prehospital Modified HEART Score Predictive of 30-Day Adverse Cardiac Events

Introduction

The History, Electrocardiogram (ECG), Age, Risk Factors, and Troponin (HEART) score is a decision aid designed to risk stratify emergency department (ED) patients with acute chest pain. It has been validated for ED use, but it has yet to be evaluated in a prehospital setting.

Hypothesis

A prehospital modified HEART score can predict major adverse cardiac events (MACE) among undifferentiated chest pain patients transported to the ED.

Methods

A retrospective cohort study of patients with chest pain transported by two county-based Emergency Medical Service (EMS) agencies to a tertiary care center was conducted. Adults without ST-elevation myocardial infarction (STEMI) were included. Inter-facility transfers and those without a prehospital 12-lead ECG or an ED troponin measurement were excluded. Modified HEART scores were calculated by study investigators using a standardized data collection tool for each patient. All MACE (death, myocardial infarction [MI], or coronary revascularization) were determined by record review at 30 days. The sensitivity and negative predictive values (NPVs) for MACE at 30 days were calculated.

Results

Over the study period, 794 patients met inclusion criteria. A MACE at 30 days was present in 10.7% (85/794) of patients with 12 deaths (1.5%), 66 MIs (8.3%), and 12 coronary revascularizations without MI (1.5%). The modified HEART score identified 33.2% (264/794) of patients as low risk. Among low-risk patients, 1.9% (5/264) had MACE (two MIs and three revascularizations without MI). The sensitivity and NPV for 30-day MACE was 94.1% (95% CI, 86.8-98.1) and 98.1% (95% CI, 95.6-99.4), respectively.

Conclusions

Prehospital modified HEART scores have a high NPV for MACE at 30 days. A study in which prehospital providers prospectively apply this decision aid is warranted.

StopyraJP, HarperWS, HigginsTJ, ProkesovaJV, WinslowJE, NelsonRD, AlsonRL, DavisCA, RussellGB, MillerCD, MahlerSA. Prehospital modified HEART score predictive of 30-day adverse cardiac events. Prehosp Disaster Med. 2018;33(1):58–62.