Predictors of in-hospital cardiac arrest within 24 h after emergency department triage: A case-control study in urban Thailand

Establishment of a simplified score for predicting risk during intrahospital transport of critical patients: A prospective cohort study

AIMS AND OBJECTIVES

To establish a simple score that enables nurses to quickly, conveniently and accurately identify patients whose condition may change during intrahospital transport.

BACKGROUND

Critically ill patients may experience various complications during intrahospital transport; therefore, it is important to predict their risk before they leave the emergency department. The existing scoring systems were not developed for this population.

DESIGN

A prospective cohort study.

METHODS

This study used convenience sampling and continuous enrolment from 1 January, 2019, to 30 June, 2021, and 584 critically ill patients were included. The collected data included vital signs and any condition change during transfer. The STROBE checklist was used.

RESULTS

The median age of the modelling group was 74 (62, 83) years; 93 (19.7%) patients were included in the changed group, and 379 (80.3%) were included in the stable group. The five independent model variables (respiration, pulse, oxygen saturation, systolic pressure and consciousness) were statistically significant (p < .05). The above model was simplified based on beta coefficient values, and each variable was assigned 1 point, for a total score of 0-5 points. The AUC of the simplified score in the modelling group was 0.724 (95% CI: 0.682-0.764); the AUC of the simplified score in the validation group (112 patients) was 0.657 (95% CI: 0.566-0.741).

CONCLUSIONS

This study preliminarily established a simplified scoring system for the prediction of risk during intrahospital transport from the emergency department to the intensive care unit. It provides emergency nursing staff with a simple assessment tool to quickly, conveniently and accurately identify a patient's transport risk.

RELEVANCE TO CLINICAL PRACTICE

This study suggested the importance of strengthening the evaluation of the status of critical patients before intrahospital transport, and a simple score was formed to guide emergency department nurses in evaluating patients.

Development of a machine-learning algorithm to predict in-hospital cardiac arrest for emergency department patients using a nationwide database

In this retrospective observational study, we aimed to develop a machine-learning model using data obtained at the prehospital stage to predict in-hospital cardiac arrest in the emergency department (ED) of patients transferred via emergency medical services. The dataset was constructed by attaching the prehospital information from the National Fire Agency and hospital factors to data from the National Emergency Department Information System. Machine-learning models were developed using patient variables, with and without hospital factors. We validated model performance and used the SHapley Additive exPlanation model interpretation. In-hospital cardiac arrest occurred in 5431 of the 1,350,693 patients (0.4%). The extreme gradient boosting model showed the best performance with area under receiver operating curve of 0.9267 when incorporating the hospital factor. Oxygen supply, age, oxygen saturation, systolic blood pressure, the number of ED beds, ED occupancy, and pulse rate were the most influential variables, in that order. ED occupancy and in-hospital cardiac arrest occurrence were positively correlated, and the impact of ED occupancy appeared greater in small hospitals. The machine-learning predictive model using the integrated information acquired in the prehospital stage effectively predicted in-hospital cardiac arrest in the ED and can contribute to the efficient operation of emergency medical systems.

Outcomes of Early versus Late Endotracheal Intubation in Patients with Initial Non-Shockable Rhythm Cardiopulmonary Arrest in the Emergency Department

Background

Sudden cardiac arrest is a critical condition in the emergency department (ED). Currently, there is no considerable evidence supporting the best time to complete advanced airway management (AAM) with endotracheal intubation in cardiac arrest patients presented with initial non-shockable cardiac rhythm.

Objectives

To compare survival to hospital discharge and discharge with favorable neurological outcome between the ED cardiac arrest patients who have received AAM with endotracheal intubation within 2 minutes (early AAM group) and those over 2 minutes (late AAM group) after the start of chest compression in ED.

Methods

We conducted a retrospective cohort study involving the ED cardiac arrest patients who presented with initial non-shockable rhythm in ED. Multivariable logistic regression analysis was used to evaluate the independent effect of early AAM on outcomes. The outcomes included the survival to hospital discharge and discharge with favorable neurological outcome.

Results

There were 416 eligible participants: 209 in the early AAM group and 207 participants in the late AAM group. The early AAM group showed higher survival to hospital discharge compared with the late AAM group, but no statistically significant difference (adjusted odds ratio (aOR): 1.28, 95% confidence interval (CI): 0.59 -2.76, p = 0.524). Discharge with favorable neurological outcome is also higher in the early AAM group (aOR: 1.68, 95% CI, 0.52 -5.45, p = 0.387).

Conclusion

This study did not demonstrate a significant improvement of survival to hospital discharge and discharge with favorable neurological outcome in the ED cardiac arrest patients with initial non-shockable cardiac arrest who underwent early AAM within two minutes. More research is needed on the timing of AAM and on airway management strategies to improve survival.

Cross-cultural adaptation and validation of the Hamilton Early Warning Score for Brazil

ABSTRACT Objective Cross-culturally adapt and validate, for Portuguese, the Hamilton Early Warning Score to detect clinical deterioration in emergency services. Method Methodological study comprising the stages of translation, synthesis, back translation, expert committee (n=13), pre-test, submission, and analysis of the measurement properties in a sample of 188 patients. The Canadian Acute Scale Triage was compared with the Hamilton Early Warning Score. The Weighted Kappa Coefficient, Intraclass and Pearson Correlation Coefficient, Binary Logistic Regression and the Area Under the Receiver Operating Characteristic Curve were used for data analysis. Results The Hamilton Early Warning Score showed excellent reliability, α=0.924 (p<0.001). The construct validity identified a strong and negative correlation r=-0.75 and the predictive one presented an odds ratio of 1.63, 95% CI (1.358-1.918) (p<0.001). Conclusion The Hamilton Early Warning Score in Portuguese is valid and reliable to recognize patients in a condition of clinical deterioration in emergency services.

Adaptação transcultural e validação da Hamilton Early Warning Score para o Brasil

RESUMO Objetivo Adaptar transculturalmente e validar, para a língua portuguesa, a Hamilton Early Warning Score para detectar a deterioração clínica em serviços de emergência. Método Estudo metodológico compreendendo as etapas de tradução, síntese, retrotradução, comitê de especialistas (n=13), pré-teste, envio e análise das propriedades de medidas em uma amostra composta por 188 pacientes. Comparou-se a Canadian Acute Scale Triage com a Hamilton Early Warning Score. Foram utilizados o Coeficiente Kappa Ponderado, Coeficiente de Correlação Intraclasse e de Pearson, Regressão Logística Binária e a Área Sob a Curva Receiver Operating Characteristic para a análise dos dados. Resultados A Hamilton Early Warning Score apresentou confiabilidade excelente, ou seja, α=0,924 (p<0,001). A validade de construto identificou correlação forte e negativa r=-0,75 e a preditiva apresentou um odds ratio de 1,63, IC 95% (1,358-1,918) (p<0,001). Conclusão A Hamilton Early Warning Score em português é válida e confiável para reconhecer pacientes em condição de deterioração clínica em serviços de emergência.

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing

Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.