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.

Suboptimal prehospital decision- making for referral to alternative levels of care - frequency, measurement, acceptance rate and room for improvement.

BACKGROUND: The emergency medical services (EMS) have undergone dramatic changes during the past few decades. Increased utilisation, changes in care-seeking behaviour and competence among EMS clinicians have given rise to a shift in EMS strategies in many countries. From transport to the emergency department to at the scene deciding on the most appropriate level of care and mode of transport. Among the non-conveyed patients some may suffer from "time-sensitive conditions" delaying diagnosis and treatment. Thus, four questions arise: 1) How often are time-sensitive cases referred to primary care or self-care advice? 2) How can we measure and define the level of inappropriate clinical decision-making? 3) What is acceptable? 4) How to increase patient safety? MAIN TEXT: To what extent time-sensitive cases are non-conveyed varies. About 5-25% of referred patients visit the emergency department within 72 hours, 5% are hospitalised, 1-3% are reported to have a time-sensitive condition and seven-day mortality rates range from 0.3 to 6%. The level of inappropriate clinical decision-making can be measured using surrogate measures such as emergency department attendances, hospitalisation and short-term mortality. These measures do not reveal time-sensitive conditions. Defining a scoring system may be one alternative, where misclassifications of time-sensitive cases are rated based on how severely they affected patient outcome. In terms of what is acceptable there is no general agreement. Although a zero-vision approach does not seem to be realistic unless under-triage is split into different levels of severity with zero-vision in the most severe categories. There are several ways to reduce the risk of misclassifications. Implementation of support systems for decision-making using machine learning to improve the initial assessment is one approach. Using a trigger tool to identify adverse events is another. CONCLUSION: A substantial number of patients are non-conveyed, including a small portion with time-sensitive conditions. This poses a threat to patient safety. No general agreement on how to define and measure the extent of such EMS referrals and no agreement of what is acceptable exists, but we conclude an overall zero-vision is not realistic. Developing specific tools supporting decision making regarding EMS referral may be one way to reduce misclassification rates.

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.

Development of a trigger tool to identify harmful incidents, no harm incidents, and near misses in prehospital emergency care.

BACKGROUND: Emergency Medical Services (EMS) are a unique setting because care for the chief complaint is given across all ages in a complex and high-risk environment that may pose a threat to patient safety. Traditionally, a reporting system is commonly used to raise awareness of adverse events (AEs); however, it could fail to detect an AE. Several methods are needed to evaluate patient safety in EMS. In this light, this study was conducted to (1) develop a national ambulance trigger tool (ATT) with a guide containing descriptions of triggers, examples of use, and categorization of near misses (NMs), no harm incidents (NHIs), and harmful incidents (HIs) and (2) use the ATT on randomly selected ambulance records. METHODS: The ambulance trigger tool was developed in a stepwise manner through (1) a literature review; (2) three sessions of structured group discussions with an expert panel having knowledge of emergency medical service, patient safety, and development of trigger tools; (3) a retrospective record review of 900 randomly selected journals with three review teams from different geographical locations; and (4) inter-rater reliability testing between reviewers. RESULTS: From the literature review, 34 triggers were derived. After removing clinically irrelevant ones and combining others through three sessions of structured discussions, 19 remained. The most common triggers identified in the 900 randomly selected records were deviation from treatment guidelines (30.4%), the patient is non conveyed after EMS assessment (20.8%), and incomplete documentation (14.4%). The positive triggers were categorized as a near miss (40.9%), no harm (3.7%), and harmful incident (0.2%). Inter-rater reliability testing showed good agreement in both sessions. CONCLUSION: This study shows that a trigger tool together with a retrospective record review can be used as a method to measure the frequency of harmful incidents, no harm incidents, and near misses in the EMS, thus complementing the traditional reporting system to realize increased patient safety.

[Documentation of prehospital emergency care]. / Över 100 doktorsavhandlingar inom den prehospitala akutsjukvården i Sverige.

The scientific documentation of prehospital emergency care in Sweden is slowly expanding. The first thesis on the value of a mobile coronary care unit was defended in 1982. Since then, at least 106 theses have been defended at 15 educational institutes in Sweden. The theses can be divided into nine different themes, of which acute disease and prognostic factors (n = 30) is the most common, followed in order of frequency by caring, assessment and decision (n = 18), patient and next of kin perspective (n = 14), trauma (n = 1 1), competence, learning, and education (n = 10), care needs, cooperation, and prioritization (n = 10), disaster (n = 7), workers' health and environment (n = 3), and ethics and values (n = 3). The University of Gothenburg had the highest number of theses defended (n = 28), followed by the Karolinska Institute (n = 24) and the University of Umeå (n = 10). The theses were written by 64 nurses, 36 physicians, two public health specialists, one physiotherapist, one priest, one social worker, and one statistician.

The potential of new prediction models for emergency medical dispatch prioritisation of patients with chest pain: a cohort study.

OBJECTIVES: To develop emergency medical dispatch (EMD) centre prediction models with high sensitivity and satisfying specificity to identify high-priority patients and patients suitable for non-emergency care respectively, when assessing patients with chest pain. METHODS: Observational cohort study of 2917 unselected patients with chest pain who contacted an EMD centre in Sweden due to chest pain during 2018. Multivariate logistic regression was applied to develop models predicting low-risk or high-risk condition, that is, occurrence of time-sensitive diagnosis on hospital discharge. RESULTS: Prediction models were developed for the identification of patients suitable for high- and low-priority dispatch, using 11 and 10 variables respectively. The area under the receiver-operating characteristic curve (AUROC) for the high-risk prediction model was 0.79 and for the low-risk model it was 0.74. When applying the high-risk prediction model, 56% of the EMS missions were given highest priority, compared with 65% with the current standard. When applying the low-risk model, 7% were given the lowest priority compared to 1% for the current standard. The new prediction models outperformed today's dispatch priority accuracy in terms of sensitivity as well as positive and negative predictive value in both high- and low-risk prediction. The low-risk model predicted almost six times as many patients as having low-risk conditions compared with today's standard. This was done without increasing the number of high-risk patients wrongly assessed as low-risk. CONCLUSIONS: By introducing prediction models, based on logistic regression analyses, using variables obtained by standard EMD-questions on age, sex, medical history and symptomology, EMD prioritisation can be improved compared with using current criteria index-based ones. This will allow a more efficient emergency medical services resource allocation.

Clinical presentation in EMS patients with acute chest pain in relation to sex, age and medical history: prospective cohort study.

OBJECTIVE: To assess symptom presentation related to age, sex and previous medical history in patients with chest pain. DESIGN: Prospective observational cohort study. SETTING: Two-centre study in a Swedish county emergency medical service (EMS) organisation. PARTICIPANTS: Unselected inclusion of 2917 patients with chest pain cared for by the EMS during 2018. DATA ANALYSIS: Multivariate analysis on the association between symptom characteristics, patients' sex, age, previous acute coronary syndrome (ACS) or diabetes and the final outcome of acute myocardial infarction (AMI). RESULTS: Symptomology in patients assessed by the EMS due to acute chest pain varied with sex and age and also with previous ACS or diabetes. Women suffered more often from nausea (OR 1.6) and pain in throat (OR 2.1) or back (OR 2.1). Their pain was more often affected by palpation (1.7) or movement (OR 1.4). Older patients more often described pain onset while sleeping (OR 1.5) and that the onset of symptoms was slow, over hours rather than minutes (OR 1.4). They were less likely to report pain in other parts of their body than their chest (OR 1.4). They were to a lesser extent clammy (OR 0.6) or nauseous (OR 0.6). These differences were present regardless of whether the symptoms were caused by AMI or not. CONCLUSIONS: A number of aspects of the symptom of chest pain appear to differ in unselected prehospital patients with chest pain in relation to age, sex and medical history, regardless of whether the chest pain was caused by a myocardial infarction or not. This complicates the possibility in prehospital care of using symptoms to predict the underlying aetiology of acute chest pain.

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.

Guideline adherence among prehospital emergency nurses when caring for patients with chest pain: a prospective cohort study.

BACKGROUND: The emergency medical services (EMS) use guidelines to describe optimal patient care for a wide range of clinical conditions and symptoms. The intent is to guide personnel to provide patient care in line with best practice. The aim of this study is to describe adherence to such guidelines among prehospital emergency nurses (PENs) when caring for patients with chest pain. OBJECTIVE: To describe guideline adherence among PENs when caring for patients with chest pain. To investigate whether guideline adherence is associated with patient age, sex or final diagnosis of acute myocardial infarction on hospital discharge. METHODS: Guideline adherence in terms of patient examination and pharmaceutical treatment was analysed in a cohort of 2092 EMS missions carried out in 2018 in Region Halland, Sweden. Multivariate regression was used to describe how guideline adherence is associated with patient age, sex and diagnosis on hospital discharge. RESULTS: Guideline adherence was high regarding examination of vital signs (93%) and electrocardiogram (ECG) registration (96%) but lower in terms of pharmaceutical treatment (ranging from 28 to 90%). Adherence was increased in cases in which the patient ended up with acute myocardial infarction (AMI) as diagnosis on discharge. Patients with AMI were given acetylsalicylic acid by PENs in 50% of cases. Women were less likely than men to receive treatment with acetylsalicylic acid and oxycodone. CONCLUSIONS: Guideline adherence among PENs when caring for patients with chest pain is satisfactory in terms vital signs and ECG registration. Regarding pharmaceutical treatment guideline adherence is defective. Improved adherence is mainly associated with male sex in patients and a diagnosis of AMI on hospital discharge. Defective adherence excludes measures known to improve patients' prognoses such as treatment with acetylsalicylic acid.

Towards definitions of time-sensitive conditions in prehospital care.

BACKGROUND: Prehospital care has changed in recent decades. Advanced assessments and decisions are made early in the care chain. Patient assessments form the basis of a decision relating to prehospital treatment and the level of care. This development imposes heavy demands on the ability of emergency medical service (EMS) clinicians properly to assess the patient. EMS clinicians have a number of assessment instruments and triage systems available to support their decisions. Many of these instruments are based on vital signs and can sometimes miss time-sensitive conditions. With this commentary, we would like to start a discussion to agree on definitions of temporal states in the prehospital setting and ways of recognising patients with time-sensitive conditions in the most optimal way. MAIN BODY: There are several articles discussing the identification and management of time-sensitive conditions. In these articles, neither definitions nor terminology have been uniform. There are a number of problems associated with the definition of time-sensitive conditions. For example, intoxication can be minor but also life threatening, depending on the type of poison and dose. Similarly, diseases like stroke and myocardial infarction can differ markedly in terms of severity and the risk of life-threatening complications. Another problem is how to support EMS clinicians in the early recognition of these conditions. It is well known that many of them can present without a deviation from normal in vital signs. It will most probably be impossible to introduce specific decision support tools for every individual time-sensitive condition. However, there may be information in the type and intensity of the symptoms patients present. In future, biochemical markers and machine learning support tools may help to identify patients with time-sensitive conditions and predict mortality at an earlier stage. CONCLUSION: It may be of great value for prehospital clinicians to be able to describe time-sensitive conditions. Today, neither definitions nor terminology are uniform. Our hope is that this commentary will initiate a discussion on the issue aiming at definitions of time-sensitive conditions in prehospital care and how they should be recognised in the most optimal fashion.

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

BACKGROUND: Chest pain is a common symptom among patients contacting the emergency medical services (EMS). Risk stratification of these patients is warranted before arrival in hospital, regarding likelihood of an acute life-threatening condition (LTC). AIM: To identify factors associated with an increased risk of acute LTC among patients who call the EMS due to non-traumatic chest pain. METHODS: Several databases were searched for relevant articles. Identified articles were quality-assessed using the Scottish Intercollegiate Guidelines Network checklists. Extracted data was analysed using a semi-quantitative synthesis evaluating the level of evidence of each identified factor. RESULTS: In total, 10 of 1245 identified studies were included. These studies provided strong evidence for an increased risk of an acute LTC with increasing age, male gender, elevated heart rate, low systolic blood pressure and ST elevation or ST depression on a 12-lead ECG. The level of evidence regarding the history of myocardial infarction, angina pectoris or presence of a Q wave or a Left Bundle Branch Block on the ECG was moderate. The evidence was inconclusive regarding dyspnoea, cold sweat/paleness, nausea/vomiting, history of chronic heart failure, smoking, Right Bundle Branch Block or T-inversions on the ECG. CONCLUSIONS: Factors reflecting age, gender, myocardial ischemia and a compromised cardiovascular system predicted an increased risk of an acute life-threatening condition in the prehospital setting in cases of acute chest pain. These factors may form the basis for prehospital risk stratification in acute chest pain.