Search

Objective: A strengthened chain of survival benefits patient outcomes after out-of-hospital cardiac arrest (OHCA).2 Over the past decade, the Taipei Fire Department (TFD) has continuously implemented system-wide initiatives on this issue.We hypothesised that for adult, non-trauma OHCA patients, the bundle of these system-wide initiatives are associated with better outcomes.Methods: We conducted a registry-based, retrospective study to examine the association between consecutive system-level initiatives and OHCA survival on a two-yearly basis using trend analysis and multivariable logistic regression. The primary outcome was survival to hospital discharge (STHD) and favourable neurological status.Results: We analysed 18,076 cases from 2008 to 2017. The numbers of two-yearly cases of OHCA with resuscitation attempts from 2008 to 2017 were 3,576, 3,456, 3,822, 3,811, and 3,411. There was a significant trend of improved STHD (Two-fold) and favourable neurological outcome (Six-fold) over the past decade. Similar trends were observed in the shockable and non-shockable groups. Considering the first 2 years as baseline, the odds of STHD and favourable neurological status in the end of the initiatives increased significantly after adjusting for universally recognised predictors for OHCA survival.Conclusion: For non-trauma adult OHCA in Taipei, continuous, multifaceted system-wide initiatives on the community chain of survival were associated with improved odds of STHD and favourable neurologic outcomes. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to develop an AI model for detecting a caller's emotional state during out-of-hospital cardiac arrest calls by processing audio recordings of dispatch communications.Methods: Audio recordings of 337 out-of-hospital cardiac arrest calls from March-April 2011 were retrieved. The callers' emotional state was classified based on the emotional content and cooperative scores. Mel-frequency cepstral coefficients extracted essential information from the voice signals. A support vector machine was utilised for the automatic judgement, and repeated random sub-sampling cross validation (RRS-CV) was applied to evaluate robustness. The results from the artificial intelligence classifier were compared with the consensus of expert reviewers.Results: The audio recordings were classified into five emotional content and cooperative score levels. The proposed model had an average positive predictive value of 72.97%, a negative predictive value of 93.47%, sensitivity of 38.76%, and specificity of 98.29%. If only the first 10 seconds of the recordings were considered, it had an average positive predictive value of 84.62%, a negative predictive value of 93.57%, sensitivity of 52.38%, and specificity of 98.64%. The artificial intelligence model's performance maintained preferable results for emotionally stable cases.Conclusion: Artificial intelligence models can possibly facilitate the judgement of callers' emotional states during dispatch conversations. This model has the potential to be utilised in practice, by pre-screening emotionally stable callers, thus allowing dispatchers to focus on cases that are judged to be emotionally unstable. Further research and validation are required to improve the model's performance and make it suitable for the general population. [ABSTRACT FROM AUTHOR]

Aim: Our study aimed to compare cardiopulmonary resuscitation (CPR) performance among laypeople with different retraining intervals.Methods: Ninety-six non-medical university students were randomly allocated into 3 groups after receiving initial CPR and automated external defibrillator (AED) training. Sixty participants completed the study. The participants in the 3-, 6-, and 12-month groups received the same retraining every 3-, 6-, and 12 months. An 80-min retraining course comprised a video lecture and hands-on practice, with feedback from the instructors and the Resusci Anne® QCPR. The primary outcome was a skill pass rate one year post-initial training. The secondary outcomes included a skill pass rate prior to each retraining course, knowledge test scores, and individual skill performance evaluated by assessors and by SkillReporter® software one year post-initial training.Results: The characteristics among the groups were similar. The 3-month group had the highest pass rate (3-month group: 6-month group: 12-month group, 100.0%: 78.9%: 19.0%, p < 0.001) in the primary outcome. In secondary outcomes, the 3-month group had a higher pass rate than the 6-month group at 6 months post-initial training. The 3-month group achieved the highest knowledge test scores, and performed best in many ventilation items. They showed similar performance to the 6-month group and better performance than the 12-month group in chest compression items. The 3 groups performed similarly in AED manipulation.Conclusions: Although young laypeople with a 3-month retraining interval had the highest pass rate when performing conventional CPR, a 6-month retraining interval may be considered for training compression-only CPR and AED when balancing outcomes and resources. [ABSTRACT FROM AUTHOR]