Cha WC, et al. J Korean Med Sci. 2016 Aug;31(8):1331-1336. https://doi.org/10.3346/jkms.2016.31.8.1331

This study aimed to examine whether injury severity differs with respect to age among elderly pedestrians involved in traffic accidents and identify factors affecting injury severity. Using emergency department-based injury in-depth surveillance data, we analyzed the data of patients aged ≥60 years who were victims of pedestrian traffic accidents during 2011 to 2016. The pedestrians' ages were divided into 5-year age strata beginning at 60 years. In a multivariate analysis, injury severity was classified as severe to critical or mild to moderate. The analysis included 10,449 patients. All age groups had a female predominance, and accidents most frequently occurred during the early morning. Multivariate analyses revealed that compared to the 60 to 64 years group, the odds ratios for incurring a severe injury were 1.18 (95% confidence interval [CI], 1.02 to 1.37) for the 65 to 69 years group, 1.42 (95% CI, 1.23 to 1.64) for the 70 to 74 years group, 1.70 (95% CI, 1.45 to 1.98) for the 75 to 79 years group, and 1.83 (95% CI, 1.56 to 2.15) for the ≥80 years group. In this study of emergency department-based data, we found that injury severity increased with age among elderly victims of traffic accidents. Furthermore, injury severity varied with respect to sex, time and location of the accident, and type of vehicle involved. Therefore, measures intended to reduce and prevent traffic accidents involving elderly pedestrians should consider these findings.

Objective: We aimed to determine the factors associated with rearrest after prehospital return of spontaneous circulation (ROSC) and examine the factors associated with survival despite rearrest. Methods: We conducted a prospective multi-regional observational study of out-of-hospital cardiac arrest (OHCA) patients between August 2015 and July 2016. Patients received prehospital advanced cardiovascular life support performed by emergency medical technicians (EMTs). EMTs were directly supervised by medical directors (physicians) via real-time smartphone video calls [Smart Advanced Life Support (SALS)]. The study participants were categorized into rearrest (+) and rearrest (−) groups depending on whether rearrest occurred after prehospital ROSC. After rearrest, patients were further classified as survivors or non-survivors at discharge. Results: SALS was performed in 1,711 OHCA patients. Prehospital ROSC occurred in 345 patients (20.2%); of these patients, 189 (54.8%) experienced rearrest [rearrest (+) group] and 156 did not experience rearrest [rearrest (−) group]. Multivariate analysis showed that a longer interval from collapse to first prehospital ROSC was independently associated with rearrest [odds ratio (OR) 1.081; 95% confidence interval (CI) 1.050–1.114]. The presence of an initial shockable rhythm was independently associated with survival after rearrest (OR 6.920; 95% CI 2.749–17.422). As a predictor of rearrest, the interval from collapse to first prehospital ROSC (cut-off: 24min) had a sensitivity of 77% and a specificity of 54% (AUC =0.715 [95% CI 0.661–0.769]). Conclusions: A longer interval from collapse to first prehospital ROSC was associated with rearrest, and an initial shockable rhythm was associated with survival despite the occurrence of rearrest. Emergency medical service providers and physicians should be prepared to deal with rearrest when pulses are obtained late in the resuscitation.

Background/Objectives: The clinical impact of automated large vessel occlusion (LVO) detection tools using non-contrast CT (NCCT) is still unknown. We evaluated whether the implementation of Heuron ELVO, an artificial intelligence (AI)-driven software for triage and notification of LVO stroke using NCCT, can reduce treatment times and improve clinical outcomes in a real-world setting. Methods: We compared patients with LVO stroke before (pre-AI cohort, 84 patients) and after (post-AI cohort, 48 patients) the implementation of Heuron ELVO at a comprehensive stroke center. Primary outcomes included time-to-treatment initiation, including door-to-IV tPA and door-to-endovascular thrombectomy (EVT) times. Secondary outcomes measured changes in the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores. Statistical analyses involved multiple linear regression to adjust for confounders. Results: The implementation of Heuron ELVO significantly reduced the door-to-EVT time (30.2 min, 95% CI, −56. to −4.3), CT-to-neurologist examination time (16.4 min, 95% CI, −27.6 to −5.3), and CT-to-EVT time (29.4 min, 95% CI, −53.6 to −5.0). There was no statistical difference in the door-to-IV tPA time (8.9 min). The post-AI cohort exhibited a greater improvement in the NIHSS score compared to the pre-AI cohort, with a reduction of 4.3 points. While the post-AI cohort demonstrated a higher proportion of good outcomes (mRS 0–1, 26% vs. 40%) at the 3-month follow-up, there was no statistical significance. Conclusions: The implementation of Heuron ELVO demonstrated substantial improvements in the timeliness of stroke interventions and patient outcomes. These findings underscore the potential of AI-driven NCCT analysis in enhancing acute stroke workflows and expediting treatments in real-world settings.