Could ten questions asked by the dispatch center predict the outcome for patients with chest discomfort?

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.

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.

Interactional implications of either/or-questions during telephone triage of callers with chest discomfort in out-of-hours primary care: A conversation analysis

OBJECTIVE

To explore the interactional implications of either/or-questions on the interaction between people who call out-of-hours services in primary care (OHS-PC) and triage nurses who use a decision support tool called the 'Netherlands Triage Standard' (NTS) during telephone triage.

METHODS

A qualitative study of 68 triage conversations at six Dutch OHS-PC. Patients called the OHS-PC with symptoms, e.g. chest discomfort, suggestive of acute coronary syndrome. Using conversation analysis, we identified two categories of multiple-choice either/or-questions that indicated interactional difficulties, shown in hesitation markers within callers' responses.

RESULTS

Our analysis shows that interactional difficulties mainly arise when (i) questions are poorly designed by the triage nurse; or (ii) when the caller's complaints are ambiguously presented reflecting patient's difficulties to verbalize them (e.g. "not feeling well").

CONCLUSION

The way NTS displays key diagnostic options encourages triage nurses to use multiple-choice either/or-questions. More awareness among triage nurses is needed on undesirable implications of either/or-questions on the interaction.

PRACTICE IMPLICATIONS

We recommend changing the NTS display of diagnostic options and to use questions with fewer options in order to decrease the chance of formulating ambiguous questions soliciting unclear responses. Furthermore, asking content questions when complaints are ambiguously formulated may specify the presentation of complaints.

Limited reliability of experts' assessment of telephone triage in primary care patients with chest discomfort

OBJECTIVE

Root cause analyses of serious adverse events (SAE) in out-of-hours primary care (OHS-PC) often point to errors in telephone triage. Such analyses are, however, hampered by hindsight bias. We assessed whether experts, blinded to the outcome, recognize (un)safety of triage of patients with chest discomfort, and we quantified inter-rater reliability.

STUDY DESIGN AND SETTING

This is a case-control study with triage recordings from 2013-2017 at OHS-PC. Cases were missed acute coronary syndromes (ACSs, considered as SAE). These cases were age- and gender-matched 1:8 with the controls, sampled from the remainder of people calling for chest discomfort. Fifteen experts listened to the recordings and rated the safety of triage. We calculated sensitivity and specificity of recognizing an ACS and the intraclass correlation.

RESULTS

In total, 135 calls (15 SAE, 120 matched controls) were relistened. The experts identified ACSs with a sensitivity of 0.86 (95% CI: 0.71-0.95) and a specificity of 0.51 (95% CI: 0.43-0.58). Cases were rated significantly more often as unsafe than the controls (73.3% vs. 22.5%, P < 0.001). The inter-rater reliability for safety was poor: ICC 0.16 (95% CI: 0.00-0.32).

CONCLUSIONS

Blinded experts rated calls of missed ACSs more often as unsafe than matched control calls, but with a low level of agreement among the experts.

Predicting acute coronary syndrome in males and females with chest pain who call an emergency medical communication centre

Background

Chest pain is a frequent reason for calls in emergency medical communication centre (EMCC). Detecting a coronary origin by phone is a challenge. This is especially so as the presentations differ according to gender. We aimed to establish and validate a sex-based model to predict a coronary origin of chest pain in patients calling an EMCC.

Methods

This prospective cohort study enrolled patients at 18 years of age or older who called the EMCC because of non-traumatic chest pain. The main outcome was the diagnosis of acute coronary syndrome (ACS) determined by expert evaluation of patient files.

Results

During 18 months, 3727 patients were enrolled: 2097 (56%) men and 1630 (44%) women. ACS was diagnosed in 508 (24%) men and 139 (9%) women. For men, independent factors associated with an ACS diagnosis were age, tobacco use, severe and permanent pain; retrosternal, breathing non-related and radiating pain; and additional symptoms. The area under the receiver operating characteristic curve (AUC) was 0.76 (95% confidence interval [CI] 0.73–0.79) for predicting ACS. The accuracy of the male model to predict ACS was validated in a validation dataset (Hosmer-Lemeshow test: p = 0.554); the AUC was 0.77 (95%CI 0.73–0.80). For women, independent factors associated with an ACS diagnosis were age ≥ 60 years, personal history of coronary artery disease, and breathing non-related and radiating pain. The AUC was 0.79 (95%CI 0.75–0.83). The accuracy of the female model to predict ACS was not validated in the validation dataset (Hosmer-Lemeshow test: p = 0.035); the AUC was 0.67 (95%CI 0.60–0.74).

Conclusions

Predictors of an ACS diagnosis in patients calling an EMCC for chest pain differ between men and women. We developed an accurate predictive model for men, but for women, the accuracy was poor.

Trial registration

This study is registered with ClinicalTrials.gov (NCT02042209).

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.

Association between the reported intensity of an acute symptom at first prehospital assessment and the subsequent outcome: a study on patients with acute chest pain and presumed acute coronary syndrome

BACKGROUND

To decrease the morbidity burden of cardiovascular disease and to avoid the development of potentially preventable complications, early assessment and treatment of acute coronary syndrome (ACS) are important. The aim of this study has therefore been to explore the possible association between patients' estimated intensity of chest pain when first seen by the ambulance crew in suspected ACS, and the subsequent outcome before and after arrival in hospital.

METHODS

Data was collected both prospectively and retrospectively. The inclusion criteria were chest pain raising suspicion of ACS and a reported intensity of pain ≥4 on the visual analogue scale.

RESULTS

All in all, 1603 patients were included in the study. Increased intensity of chest pain was related to: 1) more heart-related complications before hospital admission; 2) a higher proportion of heart failure, anxiety and chest pain after hospital admission; 3) a higher proportion of acute myocardial infarction and 4) a prolonged hospitalisation. However, there was no significant association with mortality neither in 30 days nor in three years. Adjustment for possible confounders including age, a history of smoking and heart failure showed similar results.

CONCLUSION

The estimated intensity of chest pain reported by the patients on admission by the ambulance team was associated with the risk of complications prior to hospital admission, heart failure, anxiety and chest pain after hospital admission, the final diagnosis and the number of days in hospital.

TRIAL REGISTRATION

ClinicalTrials.gov 151:2008/4564 Identifier: NCT00792181. Registred 17 November 2008 'retrospectively registered'.

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.