The Association Between Patient Outcomes and the Initial Emergency Severity Index Triage Score in Patients With Suspected Acute Coronary Syndrome

Nursing student's experiences of Role-Play Scenario-Based Disaster Triage (START) training: A qualitative research

BACKGROUND

Disasters can overwhelm healthcare systems, making effective triage a crucial skill for nurses. Role-Play Scenario-Based Disaster Triage Training offers a promising approach to prepare nursing students for these situations. Understanding students' experiences with this training method can inform its development and effectiveness in fostering disaster preparedness. However, little is known about this training experience of nursing students. In particular, by examining the challenges faced by students, the emotional distress they experience and their achievements, it will provide guidance for the development of education programs in a more comprehensive and student-oriented manner.

AIM

The aim of this study is to elucidate the experiences of nursing students regarding Role-Play Scenario-Based Disaster Triage Training.

DESIGN AND METHODS

A qualitative approach was employed to comprehend participating students' experiences. Face-to-face and online semi-structured interviews were conducted with 17 students using an interview guide, and the interactions were recorded for subsequent analysis. Thematic analysis was employed to develop themes.

RESULTS

The study results emphasize the challenges experienced by students during triage training, their gains and the support they need to cope with the challenges they experience. Data were organized around four main themes: "Challenges of the triage process" with subthemes such as "Thinking of the actual earthquake moment" and "Ethical dilemmas"; "Emotional turmoil" with subthemes including "Anxiety" and "Fear and Helplessness"; "Achievements" with subthemes such as "Increase in knowledge and skills" and "The importance of the nurse"; and "Suggestions" with subthemes including "Continuous education" and "Mandatory course".

CONCLUSIONS

The findings of the study provide new insights into the experiences of nursing students during the disaster triage process. The results may contribute to the improvement of future training programs by identifying the challenges and needs faced by nursing students after disaster triage education.

HEART vs EDACS Scores on Predicting Major Events Among Patients With Suspected Acute Coronary Syndrome at the Cardiac Emergency Department

Coronary risk scores, such as History, Electrocardiogram, Age, Risk Factors, and Troponin (HEART) and Emergency Department Assessment of Chest Pain Score (EDACS) scores, help nurses identify suspected acute coronary syndrome (ACS) patients who have a risk for major adverse cardiac events (MACE) within 30 days.

AIM

To compare the accuracy of HEART and EDACS scores in predicting major events among patients suspected of ACS in the cardiac emergency department (ED).

DESIGN AND METHODS

A prospective correlational observational study design was performed on cardiac ED patients who presented with suspected ACS.

TOOLS

Three tools were utilized to collect data pertinent to the study: Tool I comprises patients' assessment (personal characteristics, risk factors for ACS, and chest pain assessment sheet); Tool II is the risk assessment tool that includes HEART and EDACS scores; and Tool III is MACE incidence among studied patients within 30 days.

RESULTS

HEART score was significantly (P < .01) higher among patients for whom MACE was present than absent. However, EDACS score showed no significant difference (P > .05) among patients whose MACE was present or absent. HEART risk score >6 correctly predicted MACE cases with sensitivity and specificity of 77.46% and 48.28%, respectively. However, EDACS score >18 correctly predicted MACE cases with sensitivity and specificity of 42.25% and 75.86%, respectively.

CONCLUSION

This study concludes that HEART score has better sensitivity than EDACS in predicting MACE among suspected ACS patients at the cardiac ED. The HEART score provides the nurses with a quicker and more reliable predictor of MACE shortly after the arrival of the suspected ACS patients at the cardiac ED than the EDACS score. The study recommended the implementation of a HEART score in the cardiac ED for predicting MACE in suspected ACS patients. Follow up closely for high-risk patients to MACE. An educational program should be made for nurses about the implementation of the heart score in the cardiac ED.

Effect of triage training on nurses' practice and triage outcomes of patients with acute coronary syndrome

BACKGROUND

Accurate assessment and prompt management of patients with acute coronary syndrome (ACS) is a complex process for emergency department (ED) nurses and has variable clinical outcomes. The aim of the present study was to determine the effectiveness of an educational intervention on nurses' practice during the triage of patients with ACS and the triage outcomes in this group of patients.

METHODS

In this quasi-experimental study, a pretest-posttest group of 24 nurses were included by convenience sampling method and 960 patients with ACS were selected by sequential sampling during the pre-intervention (n = 480) and post-intervention (n = 480) phases. A case-based learning (CBL) intervention was performed for nurses for one month considering the role of the triage nurse according to the American College of Cardiology (ACC) and the American Heart Association (AHA) recommendations as well as the factors affecting the proper identification and management of patients with ACS. During patient triage in the pre- and post-intervention phases, the "Triage Nurse Practice Checklist" and the "Medical Electronic Records" were used to assess nurses' practice and the triage outcomes in patients, respectively.

RESULTS

The overall mean score of the triage nurses' practice and its subscales, including Primary monitoring and assessment, cardiovascular risk factors assessment, evaluation of coronary heart disease (CHD) symptoms, chest pain management, and adherence to the ACC/AHA practice guidelines were significantly improved in the post-intervention phase compared with the pre-intervention phase (p < 0.001). There was no significant difference between the triage outcomes, including in-hospital mortality within 24 hours, death in ED, hospitalization in other wards, and discharge from ED in the pre and post-intervention phases (P = 0.723).

CONCLUSION

The development of a cardiac triage-specific educational program could improve the performance of nurses in the evaluation and management of patients with ACS, but had no effect on the triage outcomes in this group of patients. We recommend a quality improvement project or a critical outcomes-based triage system to assess ACS patients' care needs in the ED.

Development and Validation of an Artificial Intelligence Electrocardiogram Recommendation System in the Emergency Department

The machine learning-assisted electrocardiogram (ECG) is increasingly recognized for its unprecedented capabilities in diagnosing and predicting cardiovascular diseases. Identifying the need for ECG examination early in emergency department (ED) triage is key to timely artificial intelligence-assisted analysis. We used machine learning to develop and validate a clinical decision support tool to predict ED triage patients’ need for ECG. Data from 301,658 ED visits from August 2017 to November 2020 in a tertiary hospital were divided into a development cohort, validation cohort, and two test cohorts that included admissions before and during the COVID-19 pandemic. Models were developed using logistic regression, decision tree, random forest, and XGBoost methods. Their areas under the receiver operating characteristic curves (AUCs), positive predictive values (PPVs), and negative predictive values (NPVs) were compared and validated. In the validation cohort, the AUCs were 0.887 for the XGBoost model, 0.885 for the logistic regression model, 0.878 for the random forest model, and 0.845 for the decision tree model. The XGBoost model was selected for subsequent application. In test cohort 1, the AUC was 0.891, with sensitivity of 0.812, specificity of 0.814, PPV of 0.708 and NPV of 0.886. In test cohort 2, the AUC was 0.885, with sensitivity of 0.816, specificity of 0.812, PPV of 0.659, and NPV of 0.908. In the cumulative incidence analysis, patients not receiving an ECG yet positively predicted by the model had significantly higher probability of receiving the examination within 48 h compared with those negatively predicted by the model. A machine learning model based on triage datasets was developed to predict ECG acquisition with high accuracy. The ECG recommendation can effectively predict whether patients presenting at ED triage will require an ECG, prompting subsequent analysis and decision-making in the ED.

Exploring the complex interactions of baseline patient factors to improve nursing triage of acute coronary syndrome

Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.