Predictive accuracy of triage nurses evaluation in risk stratification of syncope in the emergency department

Recent Updates and Technological Developments in Evaluating Cardiac Syncope in the Emergency Department

Syncope is a commonly encountered problem in the emergency department (ED), accounting for approximately 3% of presenting complaints. Clinical assessment of syncope can be challenging due to the diverse range of conditions that can precipitate the symptom. Annual mortality for patients presenting with syncope ranges from 0-12%, and if the syncope is secondary to a cardiac cause, then this figure rises to 18-33%. In ED, it is paramount to accurately identify those presenting with syncope, especially patients with an underlying cardiac aetiology, initiate appropriate management, and refer them for further investigations. In 2018, the European Society of Cardiology (ESC) updated its guidelines with regard to diagnosing and managing patients with syncope. We highlight recent developments and considerations in various components of the workup, such as history, physical examination, investigations, risk stratification, and novel biomarkers, since the establishment of the 2018 ESC guidelines. We further discuss the emerging role of artificial intelligence in diagnosing cardiac syncope and postulate how wearable technology may transform evaluating cardiac syncope in ED.

Triage assessment of transitory loss of consciousness in the emergency department-A retrospective observational study

AIM

To establish how the Manchester Triage System can correctly prioritize patients admitted to the emergency department for transitory loss of consciousness in relation to their risk of presenting severe acute disease.

DESIGN

A observational retrospective study.

METHODS

A total of 2291 patients who required a triage evaluation for a transitory loss of consciousness at the emergency department of Merano Hospital between 1 January 2017 and 30 June 2019 were considered. Transitory loss of consciousness was classified according to European Society of Cardiology guidelines. The baseline characteristics of the patients were collected and divided according to the priority level assigned at triage into two different study groups: high priority (red/orange) and low priority (blue/green/yellow). The composite outcome of the study was defined as the diagnosis of a severe acute disease.

RESULTS

Of the patients enrolled, 17% (390/2291) had a high-priority code and 83% (1901/2291) received a low-priority code. Overall, a severe acute disease was present in 16.9% of patients (387/2291). The Manchester Triage System had a sensitivity of 42.4%, a specificity of 88.1% and an accuracy of 80.4% for predicting severe acute disease. The discriminatory ability had an area under the receiver operating characteristic curve of 0.651 (CI 95%: 0.618-0.685).

CONCLUSIONS

Despite the good specificity, the low sensitivity does not currently allow the Manchester Triage System to completely exclude patients with a severe acute disease who presented in the emergency department for a transitory loss of consciousness. Therefore, it is important to develop precise nursing tools or assessments that can improve triage performance.

IMPACT

The assessment of a complex symptom can create difficulties in the stratification of patients in triage, assigning low-priority codes to patients with a severe disease. Additional tools are needed to allow the correct triage assessment of patients presenting with transitory loss of consciousness.

Nurse triage accuracy in the evaluation of syncope according to European Society of Cardiology guidelines

AIMS

The role of triage for patients admitted to the emergency department (ED) for a syncopal transitory loss of consciousness (TLOC) has not been debated, and no comparisons with the recent European Society of Cardiology (ESC) guidelines are currently available. To verify the ability of triage to correctly prioritize patients with syncopal TLOC.

METHODS AND RESULTS

All patients who received a triage assessment at the ED of the Merano Hospital (Italy) between 1 January 2017 and 30 June 2019 for a syncope were considered. All syncope were reclassified according to the aetiology reported in the ESC guidelines. The baseline characteristics of the patients were recorded and divided according to the severity code provided during triage into two study groups: high priority (red/orange) and low priority (yellow/green/blue). The outcome of the study was the presence of a diagnosed cardiac cause within 30 days after the admission. A total of 2066 patients were enrolled (14.3% high priority vs. 85.7% low priority). Cardiac syncope was present in 7.5% of patients. Nurse triage showed a sensitivity for cardiac syncope of 44.8%, a specificity of 88.1%, and an accuracy of 84.9%. The observed discriminatory ability presented an area under the receiver operating characteristic curve of 0.685 (95% confidence interval 0.638-0.732). The possible identification of the aetiology of the syncopal TLOC by the nurse showed good agreement with the medical diagnosis (Cohen's kappa 0.857, P < 0.001).

CONCLUSIONS

In cases of syncopal TLOC, nurse triage had a fair specificity but suboptimal sensitivity for cardiac causes. Specific nursing assessments following triage (e.g. precise scores or electrocardiogram) could improve the triage performance.

Machine Learning and Syncope Management in the ED: The Future Is Coming

In recent years, machine learning (ML) has been promisingly applied in many fields of clinical medicine, both for diagnosis and prognosis prediction. Aims of this narrative review were to summarize the basic concepts of ML applied to clinical medicine and explore its main applications in the emergency department (ED) setting, with a particular focus on syncope management. Through an extensive literature search in PubMed and Embase, we found increasing evidence suggesting that the use of ML algorithms can improve ED triage, diagnosis, and risk stratification of many diseases. However, the lacks of external validation and reliable diagnostic standards currently limit their implementation in clinical practice. Syncope represents a challenging problem for the emergency physician both because its diagnosis is not supported by specific tests and the available prognostic tools proved to be inefficient. ML algorithms have the potential to overcome these limitations and, in the future, they could support the clinician in managing syncope patients more efficiently. However, at present only few studies have addressed this issue, albeit with encouraging results.

Description and development of a nurse-led cardiac assessment team

A problem was identified where patient care was affected because of delays in receiving specialist cardiology input. This report describes the experience of developing a specialist cardiac assessment where senior cardiac nurses were trained to provide a 24-hour presence in the emergency department (ED). We describe the service and our evaluation of the service. These dedicated specialised nurses can optimise patient management including admission or safely discharge patients with relevant follow-up when necessary. The team also runs three clinics per week with consultant support. The team of 10 nurses provides a cardiology opinion to approximately 400 patients a month in the ED and 100 patients a month in the acute medical unit (AMU). Eighty-seven per cent of patients are seen in the ED within 30 minutes of referral. Approximately 40% of patients reviewed are accepted directly into cardiology beds thus avoiding admission to the AMU. It has been estimated that 6 bed-days are saved each day, which translated to an estimated £400,000 each year. The team also provides outpatient rapid access services which generates £121,792 income for the directorate. We demonstrate that a cardiac nurse assessment team can provide a cost-effective 24-hour presence in the ED.

Accuracy in ED Triage for Symptoms of Acute Myocardial Infarction

More than 6 million people present to emergency departments across the United States annually with symptoms of acute myocardial infarction (AMI). Of the 1 million patients with AMI, 350,000 die during the acute phase. Accurate ED triage can reduce mortality and morbidity, yet accuracy rates are low. In this study we explored the relationship between patient and nurse characteristics and accuracy of triage in patients with symptoms of AMI.

METHODS

This retrospective, descriptive study used patient data from electronic medical records. The sample of 286 patients was primarily white, with a mean age of 61.44 years (standard deviation [SD], ±13.02), and no history of heart disease. The sample of triage nurses was primarily white and female, with a mean age of 45.46 years (SD, ±11.72) and 18 years of nursing experience. Nineteen percent of the nurses reported having earned a bachelor's degree.

RESULTS

Emergency nurse triage accuracy was 54%. Patient race and presence of chest pain were significant predictors of accuracy. Emergency nurse age was a significant predictor of accuracy in triage, but years of experience in nursing was not a significant predictor.

DISCUSSION

Of the 9 variables investigated, only patient race, symptom presentation, and emergency nurse age were significant predictors of triage accuracy. Inconsistency in triage decisions may be due to other conditions not yet explored, such as critical thinking skills and executive functions. This study adds to the body of evidence regarding ED triage of patients with symptoms of AMI. However, further exploration into decisions at triage is warranted to improve accuracy, expedite care, and improve outcomes.