Nighttime is associated with decreased survival for out of hospital cardiac arrests: A meta-analysis of observational studies

Objective Hospital characteristics, such as hospital type and admission time, have been reported to be associated with survival in adult out-of-hospital cardiac arrest (OHCA) patients. However, findings regarding the effects of hospital types on pediatric OHCA patients have been limited. The aim of this study was to analyze the relationship between the hospital characteristics and the outcomes of pediatric OHCA patients. Methods This study was a retrospective secondary analysis of the Japanese Association for Acute Medicine-out-of-hospital cardiac arrest registry. The period of this study was from 1 June 2014 to 31 December 2015. We enrolled all pediatric patients (those 0–17 years of age) experiencing OHCA in this study. We enrolled all types of OHCA. The primary outcome of this study was 1-month survival after the onset of cardiac arrest. Results We analyzed 310 pediatric patients (those 0–17 years of age) with OHCA. In survivors, the rate of witnessed arrest and daytime admission was significantly higher than nonsurvivors (56% vs. 28%, p < 0.001: 49% vs. 31%; p = 0.03, respectively). The multiple logistic regression model showed that daytime admission was related to 1-month survival (odds ratio, OR: 95% confidence interval, CI, 3.64: 1.23–10.80) (p = 0.02). OHCA of presumed cardiac etiology and witnessed OHCA were associated with higher 1-month survival. (OR: 95% CI, 3.92: 1.23–12.47, and 6.25: 1.98–19.74, respectively). Further analyses based on the time of admission showed that there were no significant differences in the proportions of patients with witnessed arrest and who received bystander cardiopulmonary resuscitation and emergency medical service response time by admission time. Conclusion Pediatric OHCA patients who were admitted during the day had a higher 1-month survival rate after cardiac arrest than patients who were admitted at night.

Section: Time Of Admission and Survival After Cardiac Arrestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Daytime admission is associated with higher 1-month survival for pediatric out-of-hospital cardiac arrest: Analysis of a nationwide multicenter observational study in Japan

et al. 2021

“…Factors can now be included that were traditionally left out of formal triage protocols. For example, a patient's time of arrival are important [22], and a patient's mode of arrival is also anecdotally acknowledged as being important by experienced triage nurses: patients that arrive on ambulances or arrive at night tend to be sicker. In fact, in our MLS results, the most important triage feature was arrival mode, which is similar to other machine learning triage studies [20].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of A Practical Machine-Learning Triage Algorithm for The Detection of Patients in Need of Critical Care in The Emergency Department

Gao

et al. 2022

Preprint

Background One of the great challenges in the emergency department (ED) is to accurately differentiate critically ill from stable patients. Although there are many triage systems in the world, these all rely to varying degrees on the subjective judgment of triage officers, which may contribute to mis-triage errors. To accurately differentiate critically ill from stable patients, we present a machine learning system (MLS) that can provide a second opinion to ED triage officers to help recognize critically ill patients. The system also provides individual explanations, visible to triage officers, as to why a patient scored the way they did. Methods To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training. Once the MLS was derived, we conducted a prospective randomized cohort study of ED patients between March and April 2020. The mis-triage rate (defined as a patient initially triaged to a EETS level 3 or 4 acuity that was then up-triaged or taken to the resuscitation room) was calculated with and without the input of the MLS. Results The MLS system achieved an area under the receiver operating characteristic curve (AUC) value of 0.86 in a retrospective validation dataset of 2,000 cases. In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate in the control group, the mis-triage rate in the MLS-assisted group was 0.9%. Conclusion This MLS method with a real-time practical explanation for triage officers was able to lower the mis-triage rate of ED patients in need of critical care.

“…Factors can now be included that were traditionally left out of formal triage protocols. For example, a patient’s time of arrival are important 24 , and a patient’s mode of arrival is also anecdotally acknowledged as being important by experienced triage nurses: patients that arrive on ambulances or arrive at night tend to be sicker. In fact, in our MLS results, the most important triage feature was arrival mode, which is similar to other machine learning triage studies 8 .…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a practical machine-learning triage algorithm for the detection of patients in need of critical care in the emergency department

Gao

et al. 2021

Sci Rep

Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.