Acute abdominal pain in triage: A retrospective observational study of the Manchester triage system's validity

Enhancing triage accuracy: The influence of nursing education on risk prediction

AIM

This study aimed to compare the performance in risk prediction of various outcomes between specially trained triage nurses and the Manchester Triage System (MTS).

DESIGN

Prospective observational study.

METHODS

The study was conducted from June 1st to December 31st, 2023, at the Emergency Department of Merano Hospital. Triage nurses underwent continuous training through dedicated courses and daily audits. We compared the risk stratification performed by expert nurses with that of MTS on various outcomes such as mortality, hospitalisation, and urgency defined by the physicians. Comparisons were made using the Areas Under the Receiver Operating Characteristic curve (AUROC).

RESULTS

The agreement in code classification between the MTS and the expert nurse was very low. The AUROC curve analysis showed that the expert nurse outperformed the MTS in all outcomes. The triage nurse's experience led to statistically significant better stratification in admission rates, ICU admissions, and all outcomes based on the physician's assessment.

CONCLUSIONS

The continuous training of nurses enables them to achieve better risk prediction compared to standardized triage systems like MTS, emphasizing the utility and necessity of implementing continuous training pathways for these highly specialised personnel.

Machine learning based prediction models for analyzing risk factors in patients with acute abdominal pain: a retrospective study

Background

Acute abdominal pain (AAP) is a common symptom presented in the emergency department (ED), and it is crucial to have objective and accurate triage. This study aims to develop a machine learning-based prediction model for AAP triage. The goal is to identify triage indicators for critically ill patients and ensure the prompt availability of diagnostic and treatment resources.

Methods

In this study, we conducted a retrospective analysis of the medical records of patients admitted to the ED of Wuhan Puren Hospital with acute abdominal pain in 2019. To identify high-risk factors, univariate and multivariate logistic regression analyses were used with thirty-one predictor variables. Evaluation of eight machine learning triage prediction models was conducted using both test and validation cohorts to optimize the AAP triage prediction model.

Results

Eleven clinical indicators with statistical significance (p < 0.05) were identified, and they were found to be associated with the severity of acute abdominal pain. Among the eight machine learning models constructed from the training and test cohorts, the model based on the artificial neural network (ANN) demonstrated the best performance, achieving an accuracy of 0.9792 and an area under the curve (AUC) of 0.9972. Further optimization results indicate that the AUC value of the ANN model could reach 0.9832 by incorporating only seven variables: history of diabetes, history of stroke, pulse, blood pressure, pale appearance, bowel sounds, and location of the pain.

Conclusion

The ANN model is the most effective in predicting the triage of AAP. Furthermore, when only seven variables are considered, including history of diabetes, etc., the model still shows good predictive performance. This is helpful for the rapid clinical triage of AAP patients and the allocation of medical resources.

Triaging and referring in adjacent general and emergency departments (the TRIAGE trial): A cluster randomised controlled trial

OBJECTIVES

To determine whether a new triage system safely diverts a proportion of emergency department (ED) patients to a general practitioner cooperative (GPC).

METHODS

Unblinded randomised controlled trial with weekends serving as clusters (three intervention clusters for each control). The intervention was triage by a nurse using a new extension to the Manchester Triage System assigning low-risk patients to the GPC. During intervention weekends, patients were encouraged to follow this assignment; it was not communicated during control weekends (all patients remained at the ED). The primary outcome was the proportion of patients assigned to and handled by the GPC during intervention weekends. The trial was randomised for the secondary outcome: the proportion of patients assigned to the GPC. Additional outcomes were association of these outcomes with possible confounders (study tool parameters, nurse, and patient characteristics), proportion of patients referred back to the ED by the GPC, hospitalisations, and performance of the study tool to detect primary care patients (the opinion of the treating physician was the gold standard).

RESULTS

In the intervention group, 838/6294 patients (13.3%, 95% CI 12.5 to 14.2) were assigned to the GPC, in the control group this was 431/1744 (24.7%, 95% CI 22.7 to 26.8). In total, 599/6294 patients (9.5%, 95% CI 8.8 to 10.3) experienced the primary outcome which was influenced by the reason for encounter, age, and the nurse. 24/599 patients (4.0%, 95% CI 2.7 to 5.9) were referred back to the ED, three were hospitalised. Positive and negative predictive values of the studied tool during intervention weekends were 0.96 (95%CI 0.94 to 0.97) and 0.60 (95% CI 0.58 to 0.62). Out of the patients assigned to the GPC, 2.4% (95% CI 1.7 to 3.4) were hospitalised.

CONCLUSIONS

ED nurses using a new tool safely diverted 9.5% of the included patients to primary care.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03793972.

The Manchester Triage System's performance in clinical risk prioritisation of patients presenting with headache in emergency department: A retrospective observational study

OBJECTIVE

Non-traumatic headache is a frequent reason for visits to the emergency department (ED). We evaluated the performance of the Manchester Triage System (MTS) in prioritising patients presenting to the ED with non-traumatic headache.

METHODS

In this single-centre observational retrospective study, we compared the association of MTS priority classification codes with a final diagnosis of a severe neurological condition requiring timely management (ischaemic or haemorrhagic stroke, subarachnoid haemorrhage, cerebral sinus venous thrombosis, central nervous system infection or brain tumour). The study was conducted and reported according to the STROBE statement. The overall prioritisation accuracy of MTS was estimated by the area under the receiver operating characteristic (ROC) curve. The correctness of triage prediction was estimated based on the "very urgent" MTS grouping. An undertriage was defined as a patient with an urgent and severe neurological who received a low priority/urgency MTS code (green/yellow).

RESULTS

Over 30 months, 3002 triage evaluations of non-traumatic headache occurred (1.7% of ED visits). Of these, 2.3% (68/3002) were eventually diagnosed with an urgent and severe neurological condition. The MTS had an acceptable prioritisation accuracy, with an area under the ROC curve of 0.734 (95% CI 0.668-0.799). The sensitivity of the MTS for urgent codes (yellow, orange and red) was 79.4% (95% CI 74.5-84.3), with a specificity of 54.1% (95% CI 52.9-55.3). The triage prediction was incorrect in only 6.3% (190/3002) of patients with headache.

CONCLUSION

The MTS is a safe and accurate tool for prioritising patients with non-traumatic headache in the ED. However, MTS may need further specific tools for evaluating the more complicated symptoms and for correctly identifying patients with urgent and severe underlying pathologies.

RELEVANCE TO CLINICAL PRACTICE

The triage nurse using MTS may need additional tools to improve the assessment of patients with headache, although MTS provides a good safety profile.