Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

Randomized controlled trials in resuscitation

Randomized controlled trials (RCTs) are a gold standard in research and crucial to our understanding of resuscitation science. Many trials in resuscitation have had neutral findings, questioning which treatments are effective in cardiac resuscitation. While it is possible than many interventions do not improve patient outcomes, it is also possible that the large proportion of neutral findings are partially due to design limitations. RCTs can be challenging to implement, and require extensive resources, time, and funding. In addition, conducting RCTs in the out-of-hospital setting provides unique challenges that must be considered for a successful trial. This article will outline many important aspects of conducting trials in resuscitation in the out-of-hospital setting including patient and outcome selection, trial design, and statistical analysis

Targeted temperature management following out-of-hospital cardiac arrest: a systematic review and network meta-analysis of temperature targets

Purpose: Targeted temperature management (TTM) may improve survival and functional outcome in comatose survivors of out-of-hospital cardiac arrest (OHCA), though the optimal target temperature remains unknown. We conducted a systematic review and network meta-analysis to investigate the efficacy and safety of deep hypothermia (31–32 °C), moderate hypothermia (33–34 °C), mild hypothermia (35–36 °C), and normothermia (37–37.8 °C) during TTM. Methods: We searched six databases from inception to June 2021 for randomized controlled trials (RCTs) evaluating TTM in comatose OHCA survivors. Two reviewers performed screening, full text review, and extraction independently. The primary outcome of interest was survival with good functional outcome. We used GRADE to rate our certainty in estimates. Results: We included 10 RCTs (4218 patients). Compared with normothermia, deep hypothermia (odds ratio [OR] 1.30, 95% confidence interval [CI] 0.73–2.30), moderate hypothermia (OR 1.34, 95% CI 0.92–1.94) and mild hypothermia (OR 1.44, 95% CI 0.74–2.80) may have no effect on survival with good functional outcome (all low certainty). Deep hypothermia may not improve survival with good functional outcome, as compared to moderate hypothermia (OR 0.97, 95% CI 0.61–1.54, low certainty). Moderate hypothermia (OR 1.23, 95% CI 0.86–1.77) and deep hypothermia (OR 1.27, 95% CI 0.70–2.32) may have no effect on survival, as compared to normothermia. Finally, incidence of arrhythmia was higher with moderate hypothermia (OR 1.45, 95% CI 1.08–1.94) and deep hypothermia (OR 3.58, 95% CI 1.77–7.26), compared to normothermia (both high certainty). Conclusions: Mild, moderate, or deep hypothermia may not improve survival or functional outcome after OHCA, as compared to normothermia. Moderate and deep hypothermia were associated with higher incidence of arrhythmia. Routine use of moderate or deep hypothermia in comatose survivors of OHCA may potentially be associated with more harm than benefit