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Whether rapid transportation can benefit patients with trauma remains controversial. We determined the association between prehospital time and outcome to explore the concept of the \"golden hour\" for injured patients. We conducted a retrospective cohort study of trauma patients transported from the scene to hospitals by emergency medical service (EMS) from January 1, 2016, to November 30, 2018, using data from the Pan-Asia Trauma Outcomes Study (PATOS) database. Prehospital time intervals were categorized into response time (RT), scene to hospital time (SH), and total prehospital time (TPT). The outcomes were 30-day mortality and functional status at hospital discharge. Multivariable logistic regression was used to investigate the association of prehospital time and outcomes to adjust for factors including age, sex, mechanism and type of injury, Injury Severity Score (ISS), Revised Trauma Score (RTS), and prehospital interventions. Overall, 24,365 patients from 4 countries (645 patients from Japan, 16,476 patients from Korea, 5,358 patients from Malaysia, and 1,886 patients from Taiwan) were included in the analysis. Among included patients, the median age was 45 years (lower quartile [Q1]-upper quartile [Q3]: 25-62), and 15,498 (63.6%) patients were male. Median (Q1-Q3) RT, SH, and TPT were 20 (Q1-Q3: 12-39), 21 (Q1-Q3: 16-29), and 47 (Q1-Q3: 32-60) minutes, respectively. In all, 280 patients (1.1%) died within 30 days after injury. Prehospital time intervals were not associated with 30-day mortality. The adjusted odds ratios (aORs) per 10 minutes of RT, SH, and TPT were 0.99 (95% CI 0.92-1.06, p = 0.740), 1.08 (95% CI 1.00-1.17, p = 0.065), and 1.03 (95% CI 0.98-1.09, p = 0.236), respectively. However, long prehospital time was detrimental to functional survival. The aORs of RT, SH, and TPT per 10-minute delay were 1.06 (95% CI 1.04-1.08, p < 0.001), 1.05 (95% CI 1.01-1.08, p = 0.007), and 1.06 (95% CI 1.04-1.08, p < 0.001), respectively. The key limitation of our study is the missing data inherent to the retrospective design. Another major limitation is the aggregate nature of the data from different countries and unaccounted confounders such as in-hospital management. Longer prehospital time was not associated with an increased risk of 30-day mortality, but it may be associated with increased risk of poor functional outcomes in injured patients. This finding supports the concept of the \"golden hour\" for trauma patients during prehospital care in the countries studied.

In-hospital cardiac arrest (IHCA) is a severe and sudden medical emergency that is characterized by the abrupt cessation of circulatory function, leading to death or irreversible organ damage if not addressed immediately. Emergency department (ED)-based IHCA (EDCA) accounts for 10% to 20% of all IHCA cases. Early detection of EDCA is crucial, yet identifying subtle signs of cardiac deterioration is challenging. Traditional EDCA prediction methods primarily rely on structured vital signs or electrocardiogram (ECG) signals, which require additional preprocessing or specialized devices. This study introduces a novel approach using image-based 12-lead ECG data obtained at ED triage, leveraging the inherent richness of visual ECG patterns to enhance prediction and integration into clinical workflows. This study aims to address the challenge of early detection of EDCA by developing an innovative deep learning model, the ECG-Image-Aware Network (EIANet), which uses 12-lead ECG images for early prediction of EDCA. By focusing on readily available triage ECG images, this research seeks to create a practical and accessible solution that seamlessly integrates into real-world ED workflows. For adult patients with EDCA (cases), 12-lead ECG images at ED triage were obtained from 2 independent data sets: National Taiwan University Hospital (NTUH) and Far Eastern Memorial Hospital (FEMH). Control ECGs were randomly selected from adult ED patients without cardiac arrest during the same study period. In EIANet, ECG images were first converted to binary form, followed by noise reduction, connected component analysis, and morphological opening. A spatial attention module was incorporated into the ResNet50 architecture to enhance feature extraction, and a custom binary recall loss (BRLoss) was used to balance precision and recall, addressing slight data set imbalance. The model was developed and internally validated on the NTUH-ECG data set and was externally validated on an independent FEMH-ECG data set. The model performance was evaluated using the F -score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). There were 571 case ECGs and 826 control ECGs in the NTUH data set and 378 case ECGs and 713 control ECGs in the FEMH data set. The novel EIANet model achieved an F -score of 0.805, AUROC of 0.896, and AUPRC of 0.842 on the NTUH-ECG data set with a 40% positive sample ratio. It achieved an F -score of 0.650, AUROC of 0.803, and AUPRC of 0.678 on the FEMH-ECG data set with a 34.6% positive sample ratio. The feature map showed that the region of interest in the ECG was the ST segment. EIANet demonstrates promising potential for accurately predicting EDCA using triage ECG images, offering an effective solution for early detection of high-risk cases in emergency settings. This approach may enhance the ability of health care professionals to make timely decisions, with the potential to improve patient outcomes by enabling earlier interventions for EDCA.

The effect of paramedic crew size in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) remains inconclusive. We hypothesised that teams with a larger crew size have better resuscitation performance including chest compression fraction (CCF), advanced life support (ALS), and teamwork performance than those with a smaller crew size. We conducted a randomized controlled study in a simulation setting. A total of 140 paramedics from New Taipei City were obtained by stratified sampling and were randomly allocated to 35 teams with crew sizes of 2, 3, 4, 5, and 6 (i.e. 7 teams in every paramedic crew size). A scenario involving an OHCA patient who experienced ventricular fibrillation and was attached to a cardiopulmonary resuscitation (CPR) machine was simulated. The primary outcome was the overall CCF; the secondary outcomes were the CCF in manual CPR periods, time from the first dose of epinephrine until the accomplishment of intubation, and teamwork performance. Tasks affecting the hands-off time during CPR were also analysed. In all 35 teams with crew sizes of 2, 3, 4, 5, and 6, the overall CCFs were 65.1%, 64.4%, 70.7%, 72.8%, and 71.5%, respectively (P = 0.148). Teams with a crew size of 5 (58.4%, 61.8%, 68.9%, 72.4%, and 68.7%, P<0.05) had higher CCF in manual CPR periods and better team dynamics. Time to the first dose of epinephrine was significantly shorter in teams with 4 paramedics, while time to completion of intubation was shortest in teams with 6 paramedics. Troubleshooting of M-CPR machine decreased the hands-off time during resuscitation (39 s), with teams comprising 2 paramedics having the longest hands-off time (63s). Larger paramedic crew size (≧4 paramedics) did not significantly increase the overall CCF in OHCA resuscitation but showed higher CCF in manual CPR period before the setup of the CPR machine. A crew size of ≧4 paramedics can also shorten the time of ALS interventions, while teams with 5 paramedics will have the best teamwork performance. Paramedic teams with a smaller crew size should focus more on the quality of manual CPR, teamwork, and training how to troubleshoot a M-CPR machine.

The association between out-of-hospital cardiac arrest patient survival and advanced life support response time remained controversial. We aimed to test the hypothesis that for adult, non-traumatic, out-of-hospital cardiac arrest patients, a shorter advanced life support response time is associated with a better chance of survival. We analyzed Utstein-based registry data on adult, non-traumatic, out-of-hospital cardiac arrest patients in Taipei from 2011 to 2015. Patients without complete data, witnessed by emergency medical technicians, or with response times of ≥ 15 minutes, were excluded. We used logistic regression with an exposure of advanced life support response time. Primary and secondary outcomes were survival to hospital discharge and favorable neurological outcomes (cerebral performance category ≤ 2), respectively. Subgroup analyses were based on presenting rhythms of out-of-hospital cardiac arrest, bystander cardiopulmonary resuscitation, and witness status. A total of 4,278 cases were included in the final analysis. The median advanced life support response time was 9 minutes. For every minute delayed in advanced life support response time, the chance of survival to hospital discharge would reduce by 7% and chance of favorable neurological outcome by 9%. Subgroup analysis showed that a longer advanced life support response time was negatively associated with the chance of survival to hospital discharge among out-of-hospital cardiac arrest patients with shockable rhythm and pulse electrical activity groups. In non-traumatic, adult, out-of-hospital cardiac arrest patients in Taipei, a longer advanced life support response time was associated with declining odds of survival to hospital discharge and favorable neurologic outcomes, especially in patients presenting with shockable rhythm and pulse electrical activity.

Termination-of-resuscitation rules (TORRs) in out-of-hospital cardiac arrest (OHCA) patients have been applied in western countries; in Asia, two TORRs were developed and have not been externally validated widely. We aimed to externally validate the TORRs using the registry of Pan-Asian Resuscitation Outcomes Study (PAROS). PAROS enrolled 66,780 OHCA patients in seven Asian countries from 1 January 2009 to 31 December 2012. The American Heart Association-Basic Life Support and AHA-ALS (AHA-BLS), AHA-Advanced Life Support (AHA-ALS), Goto, and Shibahashi TORRs were selected. The diagnostic test characteristics and area under the receiver operating characteristic curve (AUC) were calculated. We further determined the most suitable TORR in Asia and analysed the variable differences between subgroups. We included 55,064 patients in the final analysis. The sensitivity, specificity, negative predictive value, positive predictive value, and AUC, respectively, for AHA-BLS, AHA-ALS, Goto, Shibashi TORRs were 79.0%, 80.0%, 19.6%, 98.5%, and 0.80; 48.6%, 88.3%, 9.8%, 98.5%, and 0.60; 53.8%, 91.4%, 11.2%, 99.0%, and 0.73; and 35.0%, 94.2%, 8.4%, 99.0%, and 0.65. In countries using the Goto TORR with PPV<99%, OHCA patients were younger, had more males, a higher rate of shockable rhythm, witnessed collapse, pre-hospital defibrillation, and survival to discharge, compared with countries using the Goto TORR with PPV ≥99%. There was no single TORR fit for all Asian countries. The Goto TORR can be considered the most suitable; however, a high predictive performance with PPV ≥99% was not achieved in three countries using it (Korea, Malaysia, and Taiwan).

Major trauma is a leading cause of unexpected death globally, with increasing age-adjusted death rates for unintentional injuries. Field triage schemes (FTSs) assist emergency medical technicians in identifying appropriate medical care facilities for patients. While full FTSs may improve sensitivity, step-by-step field triage is time-consuming. A simplified FTS (sFTS) that uses only physiological and anatomical criteria may offer a more rapid decision-making process. However, evidence for this approach is limited, and its performance in identifying all age groups requiring trauma center resources in Asia remains unclear. We conducted a multinational retrospective cohort study involving adult trauma patients admitted to emergency departments in the included countries from 2016 to 2020. Prehospital and hospital data were reviewed from the Pan-Asia Trauma Outcomes Study database. Patients aged ≥18 years transported by emergency medical services were included. Patients lacking data regarding age, sex, physiological criteria, or injury severity scores were excluded. We examined the performance of sFTS in all age groups and fine-tuned physiological criteria to improve sFTS performance in identifying high-risk trauma patients in different age groups. The sensitivity and specificity of the physiological and anatomical criteria for identifying major trauma (injury severity score ≥ 16) were 80.6% and 58.8%, respectively. The modified sFTS showed increased sensitivity and decreased specificity, with more pronounced changes in the young age group. Adding the shock index further increased sensitivity in both age groups. sFTS using only physiological and anatomical criteria is suboptimal for Asian adult patients with trauma of all age groups. Adjusting the physiological criteria and adding a shock index as a triage tool can improve the sensitivity of severely injured patients, particularly in young age groups. A swift field triage process can maintain acceptable sensitivity and specificity in severely injured patients. •Simplified field triage scheme is reliable in pre-hospital settings for trauma patient in Asia.•Modified field triage scheme increased sensitivity for major trauma prediction, especially in younger age group, implying tailored field triage scheme is warranted for different demographics and trauma mechanism.•The combination of modified field triage scheme with shock index further increased the sensitivity for major trauma prediction.

Background The use of mechanical cardiopulmonary resuscitation device has been very prevalent in out-of-hospital cardiac arrest rescue. This study aimed to investigate whether the timing of mechanical cardiopulmonary resuscitation device set-up correlated with the the outcome of cardiac arrest patients. Methods We retrospectively reviewed adult nontrauma cardiac arrest cases in New Taipei City, Taiwan, from January to December 2022. Demographic data, intervention-related factors, and the time variables of mechanical cardiopulmonary resuscitation were collected. The outcomes included the return of spontaneous circulation and 24-hour survival. We compared patients who achieved spontaneous circulation and those who did not with univariate and multivariable regression analyses. Results In total, 1680 patients who received mechanical cardiopulmonary resuscitation were included in the analysis. Reducing the time interval from manual chest compression initiation to device setup was independently associated with the return of spontaneous circulation and 24-hour survival, especially in the subgroup of patients of initial shockable rhythm. Receiver operating characteristic analysis revealed that the outcome of patients with an initial shockable rhythm could be predicted by the mechanical cardiopulmonary resuscitation setup time, with areas under the curve of 60.8% and 63.9% for ROSC and 24-hour survival, respectively. The cutoff point was 395.5 s for patients with an initial shockable rhythm. Conclusion A positive correlation was found between early mechanical cardiopulmonary resuscitation intervention and the outcomes of out-of-hospital cardiac arrest patients. The time between manual chest compression and device setup could predict the return of spontaneous circulation and 24-hour survival in the subgroup of patients with initially shockable rhythm with the optimal cutoff point at 395.5 s.