Could a computer-based system including a prevalence function support emergency medical systems and improve the allocation of life support level?

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.

Accuracy of emergency medical services (EMS) telephone triage in identifying acute coronary syndrome (ACS) for patients with chest pain: a systematic literature review

OBJECTIVE

To systematically appraise the available evidence to determine the accuracy of decision aids for emergency medical services (EMS) telephone triage of patients with chest pain suspected to be caused by acute coronary syndrome (ACS) or life-threatening conditions.

DESIGN

Systematic review.

DATA SOURCES

Electronic searches were performed in Embase 1974, Medline 1946 and CINAHL 1937 databases from 3 March 2020 to 4 March 2020.

ELIGIBILITY CRITERIA

The review included all types of original studies that included adult patients (>18 years) who called EMS with a primary complaint of chest pain and evaluated dispatch triage priority by telephone. Outcomes of interest were a final diagnosis of ACS, acute myocardial infarction or other life-threatening conditions.

DATA EXTRACTION AND SYNTHESIS

Two authors independently extracted data on study design, population, study period, outcome and all data for assessment of accuracy, including cross-tabulation of triage priority against the outcomes of interest. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 assessment tool.

RESULTS

Searches identified 553 papers, of which 3 were eligible for inclusion. Those reports described the evaluation of three different prediction models with variation in the variables used to detect ACS. The overall results showed that dispatch triage tools have good sensitivity to detect ACS and life-threatening conditions, even though they are used to triage signs and symptoms rather than diagnosing the patients. On the other hand, prediction models were built to detect ACS and life-threatening conditions, and therefore, prediction models showed better sensitivity and negative predictive value than dispatch triage tools.

CONCLUSION

We have identified three prediction models for telephone triage of patients with chest pain. While they have been found to have greater accuracy than standard EMS dispatch systems, prospective external validation is essential before clinical use is considered.

PROSPERO REGISTRATION NUMBER

This systematic review was pre-registered on the International prospective register of systematic reviews (PROSPERO) database (reference CRD42020171184).

Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification

OBJECTIVES

Timely prehospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the prehospital environment. The review aim was to describe the accuracy of CDSS and individual components in the prehospital ACS management.

METHODS

This systematic review examined the current literature regarding the accuracy of CDSS for ACS in the prehospital setting, the influence of computer-aided decision-making and of 4 components: electrocardiogram, biomarkers, patient history, and examination findings. The impact of these components on sensitivity, specificity, and positive and negative predictive values was assessed.

RESULTS

A total of 11,439 articles were identified from a search of databases, of which 199 were screened against the eligibility criteria. Eight studies were found to meet the eligibility and quality criteria. There was marked heterogeneity between studies which precluded formal meta-analysis. However, individual components analysis found that patient history led to significant improvement in the sensitivity and negative predictive values. CDSS which incorporated all 4 components tended to show higher sensitivities and negative predictive values. CDSS incorporating computer-aided electrocardiogram diagnosis showed higher specificities and positive predictive values.

CONCLUSIONS

Although heterogeneity precluded meta-analysis, this review emphasizes the potential of ACS CDSS in prehospital environments that incorporate patient history in addition to integration of multiple components. The higher sensitivity of certain components, along with higher specificity of computer-aided decision-making, highlights the opportunity for developing an integrated algorithm with computer-aided decision support.

Could prioritisation by emergency medicine dispatchers be improved by using computer-based decision support? A cohort of patients with chest pain

BACKGROUND

To evaluate whether a computer-based decision support system could improve the allocation of patients with acute coronary syndrome (ACS) or a life-threatening condition (LTC). We hypothesised that a system of this kind would improve sensitivity without compromising specificity.

METHODS

A total of 2285 consecutive patients who dialed 112 due to chest pain were asked 10 specific questions and a prediction model was constructed based on the answers. We compared the sensitivity of the dispatchers' decisions with that of the model-based decision support model.

RESULTS

A total of 2048 patients answered all 10 questions. Among the 235 patients with ACS, 194 were allocated the highest prioritisation by dispatchers (sensitivity 82.6%) and 41 patients were given a lower prioritisation (17.4% false negatives). The allocation suggested by the model used the highest prioritisation in 212 of the patients with ACS (sensitivity of 90.2%), while 23 patients were underprioritised (9.8% false negatives). The results were similar when the two systems were compared with regard to LTC and 30-day mortality. This indicates that computer-based decision support could be used either for increasing sensitivity or for saving resources. Three questions proved to be most important in terms of predicting ACS/LTC, [1] the intensity of pain, [2] the localisation of pain and [3] a history of ACS.

CONCLUSION

Among patients with acute chest pain, computer-based decision support with a model based on a few fundamental questions could improve sensitivity and reduce the number of cases with the highest prioritisation without endangering the patients.

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.

Management of non-traumatic chest pain by the French Emergency Medical System: Insights from the DOLORES registry

BACKGROUND

The early recognition of acute coronary syndromes is a priority in health care systems, to reduce revascularization delays. In France, patients are encouraged to call emergency numbers (15, 112), which are routed to a Medical Dispatch Centre where physicians conduct an interview and decide on the appropriate response. However, the effectiveness of this system has not yet been assessed.

AIM

To describe and analyse the response of emergency physicians receiving calls for chest pain in the French Emergency Medical System.

METHODS

From 16 November to 13 December 2009, calls to the Medical Dispatch Centre for non-traumatic chest pain were included prospectively in a multicentre observational study. Clinical characteristics and triage decisions were collected.

RESULTS

A total of 1647 patients were included in the study. An interview was conducted with the patient in only 30.5% of cases, and with relatives, bystanders or physicians in the other cases. A Mobile Intensive Care Unit was dispatched to 854 patients (51.9%) presenting with typical angina chest pains and a high risk of cardiovascular disease. Paramedics were sent to 516 patients (31.3%) and a general practitioner was sent to 169 patients (10.3%). Patients were given medical advice only by telephone in 108 cases (6.6%).

CONCLUSIONS

Emergency physicians in the Medical Dispatch Centre sent an effecter to the majority of patients who called the Emergency Medical System for chest pain. The response level was based on the characteristics of the chest pain and the patient's risk profile.

Characteristics and outcome among patients who dial for the EMS due to chest pain

OBJECTIVES

This study aims to describe patients who called for the emergency medical service (EMS) due to chest discomfort, in relation to gender and age.

METHODS

All patients who called the emergency dispatch centre of western Sweden due to chest discomfort, between May 2009 and February 2010, were included. Initial evaluation, aetiology and outcome are described as recorded in the databases at the dispatch centre, the EMS systems and hospitals. Patients were divided into the following age groups: ≤50, 51-64 and ≥65 years.

RESULTS

In all, 14,454 cases were enrolled. Equal proportions of men (64%) and women (63%) were given dispatch priority 1. The EMS clinicians gave priority 1 more frequently to men (16% versus 12%) and older individuals (10%, 15% and 14%, respective of age group). Men had a significantly higher frequency of central chest pain (83% versus 81%); circulatory compromise (34% versus 31%); ECG signs of ischaemia (17% versus 11%); a preliminary diagnosis of acute coronary syndrome (40% versus 34%); a final diagnosis of acute myocardial infarction (14% versus 9%) and any potentially life-threatening condition (18% versus 12%). Individuals aged ≥65 years were given a lower priority than individuals aged 51-64 years, despite poorer characteristics and outcome. In all, 78% of cases with a potentially life-threatening condition and 67% of cases that died within 30 days of enrolment received dispatch priority 1. Mortality at one year was 1%, 4% and 18% in each individual age group.

CONCLUSION

Men and the elderly were given a disproportionately low priority by the EMS.

Fuel use in a large, dynamically deployed emergency medical services system

INTRODUCTION

Emergency medical services (EMS) systems are a central component of the healthcare system, particularly for older patients. As currently configured, EMS transport is fundamentally petroleum dependent. Petroleum scarcity is an emerging public health concern, particularly for patient transport. Little is known regarding EMS fuel use, potential impacts of scarcity on operations, or strategies to minimize these impacts.

OBJECTIVE

The objective of this study was to characterize the fuel use of a large, urban, hospital-based, dynamically-deployed EMS system, and to identify broad optimization categories to minimize EMS's petroleum dependence.

METHODS

Fuel use was reviewed retrospectively using fuel purchasing and maintenance data from January 2007 through September 2008. Data on unit-hours, call volume, and patient transports also were collected. Data were processed using descriptive statistics.

RESULTS

During the study period, a fleet of 35 diesel ambulances operated for 277,849 unit-hours and traveled 1,902,710 miles. Detailed mileage data were available for 66,527 unit-hours, 23.9% of the sample. Overall, vehicles averaged 6.6.89 (6.71, 7.08) miles per gallon (mpg), 11.5 (10.4, 12.6) miles were travelled per call, and 16.2 (14.8, 17.6) miles per transport; 2.7 (2.4, 2.9) gallons of fuel were used per transport.

CONCLUSIONS

In this EMS system, operations are fundamentally dependent on petroleum. Mileage estimates can serve as a baseline to evaluate interventions for reducing petroleum dependence and in contingency planning. As cost pressures increase and these interventions become more common, systematic evaluations will be important.

Early identification and delay to treatment in myocardial infarction and stroke: differences and similarities

Background

The two major complications of atherosclerosis are acute myocardial infarction (AMI) and acute ischemic stroke. Both are life-threatening conditions characterised by the abrupt cessation of blood flow to respective organs, resulting in an infarction. Depending on the extent of the infarction, loss of organ function varies considerably.

In both conditions, it is possible to limit the extent of infarction with early intervention. In both conditions, minutes count.

This article aims to describe differences and similarities with regard to the way patients, bystanders and health care providers act in the acute phase of the two diseases with the emphasis on the pre-hospital phase.

Method

A literature search was performed on the PubMed, Embase (Ovid SP) and Cochrane Library databases.

Results

In both conditions, symptoms vary considerably. Patients appear to suspect AMI more frequently than stroke and, in the former, there is a gender gap (men suspect AMI more frequently than women).

With regard to detection of AMI and stroke at dispatch centre and in Emergency Medical Service (EMS) there is room for improvement in both conditions. The use of EMS appears to be higher in stroke but the overall delay to hospital admission is shorter in AMI. In both conditions, the fast track concept has been shown to influence the delay to treatment considerably.

In terms of diagnostic evaluation by the EMS, more supported instruments are available in AMI than in stroke. Knowledge of the importance of early treatment has been reported to influence delays in both AMI and stroke.

Conclusion

Both in AMI and stroke minutes count and therefore the fast track concept has been introduced. Time to treatment still appears to be longer in stroke than in AMI. In the future improvement in the early detection as well as further shortening to start of treatment will be in focus in both conditions. A collaboration between cardiologists and neurologists and also between pre-hospital and in-hospital care might be fruitful.

Evolution and challenges in the design of computational systems for triage assistance

Compared with expert systems for specific disease diagnosis, knowledge-based systems to assist decision making in triage usually try to cover a much wider domain but can use a smaller set of variables due to time restrictions, many of them subjective so that accurate models are difficult to build. In this paper, we first study criteria that most affect the performance of systems for triage assistance. Such criteria include whether principled approaches from machine learning can be used to increase accuracy and robustness and to represent uncertainty, whether data and model integration can be performed or whether temporal evolution can be modeled to implement retriage or represent medication responses. Following the most important criteria, we explore current systems and identify some missing features that, if added, may yield to more accurate triage systems.