Cardiopulmonary Resuscitation in Adults and Children With Mechanical Circulatory Support: A Scientific Statement From the American Heart Association.

Cardiac arrest in patients on mechanical support is a new phenomenon brought about by the increased use of this therapy in patients with end-stage heart failure. This American Heart Association scientific statement highlights the recognition and treatment of cardiovascular collapse or cardiopulmonary arrest in an adult or pediatric patient who has a ventricular assist device or total artificial heart. Specific, expert consensus recommendations are provided for the role of external chest compressions in such patients.

Cardiac Arrest in Pregnancy: A Scientific Statement From the American Heart Association.

This is the first scientific statement from the American Heart Association on maternal resuscitation. This document will provide readers with up-to-date and comprehensive information, guidelines, and recommendations for all aspects of maternal resuscitation. Maternal resuscitation is an acute event that involves many subspecialties and allied health providers; this document will be relevant to all healthcare providers who are involved in resuscitation and specifically maternal resuscitation.

Part 2: Evidence Evaluation and Management of Conflicts of Interest: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations.

The process for evaluating the resuscitation science has evolved considerably over the past 2 decades. The current process, which incorporates the use of the GRADE methodology, culminated in the 2015 CoSTR publication, which in turn will inform the international resuscitation councils' guideline development processes. Over the next few years, the process will continue to evolve as ILCOR moves toward a more continuous evaluation of the resuscitation science.

Preliminary Experience With Inertial Movement Technology to Characterize Endotracheal Intubation Kinematics.

BACKGROUND: Endotracheal intubation (ETI) is an important emergency intervention. Only limited data describe ETI skill acquisition and often use bulky technology, not easily transitioned to the clinical setting. In this study, we used small, portable inertial detection technology to characterize intubation kinematic differences between experienced and novice intubators. METHODS: We performed a prospective study including novice (<10 prior clinical ETI) and experienced (>100 clinical ETI) emergency providers. We tracked upper extremity motion with roll, pitch, and yaw using inertial measurement units (IMU) placed on the bilateral hands and wrists of the intubator. Subject performed 6 simulated emergency intubations on a mannequin. Using machine learning algorithms, we determined the motions that best discriminated experienced and novice providers. RESULTS: We included data on 12 novice and 5 experienced providers. Four machine learning algorithms (artificial neural network, support vector machine, decision tree, and K-nearest neighbor search) were applied. Artificial neural network had the greatest accuracy (95% confidence interval) for discriminating between novice and experienced providers (91.17%, 90.8%-91.5%) and was the most parsimonious of the tested algorithms. Using artificial neural network, information from 5 movement features (right hand, roll amplitude; right hand, pitch amplitude; right hand, yaw standard deviation; left hand, yaw standard deviation; left hand, pitch frequency of peak amplitude) was able discriminated experienced from novice providers. CONCLUSIONS: Novice and experienced providers have different ETI movement patterns and can be distinguished by 5 specific movements. Inertial detection technology can be used to characterize the kinematics of emergency airway management.

Implementation of a Low-Dose, High-Frequency Cardiac Resuscitation Quality Improvement Program in a Community Hospital.

BACKGROUND: In 2015 the American Heart Association launched the Resuscitation Quality ImprovementⓇ (RQIⓇ) Program to address the urgent need to improve in-hospital cardiac arrest survival through a novel competency-based model for health care provider (HCP) cardiopulmonary resuscitation (CPR) training. This innovation differs from the traditional Basic Life Support (BLS) training model by providing self-directed, low-dose, high-frequency CPR skill activities with the objectives of skills mastery and retention. A program implementation study was conducted at the first hospital in the state of Illinois to adopt RQI in 2016. METHODS: The study was designed to evaluate implementation of the RQI program, CPR performance during RQI simulation sessions, and participant impressions at a community hospital. Quantitative data were evaluated based on psychomotor compression and ventilation performance. Quantitative and qualitative data were evaluated based on a perceptual CPR confidence and program satisfaction survey. RESULTS: Statistical analysis demonstrates significant improvement in HCPs' quarterly psychomotor CPR skill performance over a one-year period in first compression score, and first and highest ventilation score per quarterly session. The number of attempts to pass the ventilation skill session decreased between the first and fourth quarter. Survey results of HCPs' program perceptions 30 months post-RQI implementation indicate satisfaction with the RQI program and an increase in CPR skill confidence. CONCLUSION: Findings demonstrate that the RQI program for ongoing verification of BLS skill and knowledge provides improvements in HCPs' CPR psychomotor competence and confidence/satisfaction using an efficient and sustainable method at a community hospital.