BackgroundLong durational chest compression (CC) deteriorates cardiopulmonary resuscitation (CPR) quality. The appropriate number of CC personnel for minimizing rescuer’s fatigue is mostly unknown.ObjectiveWe determined the optimal number of personnel needed for 30-min CPR in a rescue-team.MethodsWe conducted a randomized, manikin trial on healthcare providers. We divided them into Groups A to D according to the assigned different rest period to each group between the 2 min CCs. Groups A, B, C, and D performed CCs at 2, 4, 6, and 8 min rest period. All participants performed CCs for 30 min with a different rest period; participants allocated to Groups A, B, C, and D performed, eight, five, four, and three cycles, respectively. We compared a quality change of CCs among these groups to investigate how the assigned rest period affects the maintenance of CC quality during the 30-min CPR.ResultsThis study involved 143 participants (male 58 [41%]; mean age, 24 years,) for the evaluation. As participants had less rest periods, the quality of their CCs such as sufficient depth ratio declined over 30-min CPR. A significant decrease in the sufficient CC depth ratio was observed in the second to the last cycle as compared to the first cycle. (median changes; A: −4%, B: −3%, C: 0%, and D: 0% p < 0.01).ConclusionsA 6 min rest period after 2 min CC is vital in order to sustain the quality of CC during a 30-min CPR cycle. At least four personnel may be needed to reduce rescuer's fatigue for a 30-min CPR cycle when the team consists of men and women.
Background
There is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED).
Objective
We applied modern machine learning approaches to predict difficult airways and first-pass success.
Methods
In a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success.
Results
Of 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models—except for k-point nearest neighbor and multilayer perceptron—had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models—except k-point nearest neighbor and random forest models—had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001).
Conclusions
Machine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED.
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