The "2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care" increased the focus on methods to ensure that high-quality cardiopulmonary resuscitation (CPR) is performed in all resuscitation attempts. There are 5 critical components of high-quality CPR: minimize interruptions in chest compressions, provide compressions of adequate rate and depth, avoid leaning between compressions, and avoid excessive ventilation. Although it is clear that high-quality CPR is the primary component in influencing survival from cardiac arrest, there is considerable variation in monitoring, implementation, and quality improvement. As such, CPR quality varies widely between systems and locations. Victims often do not receive high-quality CPR because of provider ambiguity in prioritization of resuscitative efforts during an arrest. This ambiguity also impedes the development of optimal systems of care to increase survival from cardiac arrest. This consensus statement addresses the following key areas of CPR quality for the trained rescuer: metrics of CPR performance; monitoring, feedback, and integration of the patient’s response to CPR; team-level logistics to ensure performance of high-quality CPR; and continuous quality improvement on provider, team, and systems levels. Clear definitions of metrics and methods to consistently deliver and improve the quality of CPR will narrow the gap between resuscitation science and the victims, both in and out of the hospital, and lay the foundation for further improvements in the future.

Rationale: The 2016 definitions of sepsis included the quick Sepsisrelated Organ Failure Assessment (qSOFA) score to identify highrisk patients outside the intensive care unit (ICU).Objectives: We sought to compare qSOFA with other commonly used early warning scores. . Using the highest non-ICU score of patients, >2 SIRS had a sensitivity of 91% and specificity of 13% for the composite outcome compared with 54% and 67% for qSOFA >2, 59% and 70% for MEWS >5, and 67% and 66% for NEWS >8, respectively. Most patients met >2 SIRS criteria 17 hours before the combined outcome compared with 5 hours for >2 and 17 hours for >1 qSOFA criteria.Conclusions: Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non-ICU patients. These results suggest that the qSOFA score should not replace general early warning scores when risk-stratifying patients with suspected infection.

OBJECTIVE Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. DESIGN Observational cohort study. SETTING Five hospitals, from November 2008 until January 2013. PATIENTS Hospitalized ward patients INTERVENTIONS None MEASUREMENTS AND MAIN RESULTS Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score (MEWS), a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC 0.80 [95% CI 0.80–0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC 0.77 vs 0.74; p<0.01), and all models were more accurate than the MEWS (AUC 0.70 [95% CI 0.70–0.70]). CONCLUSIONS In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

June 23/30, 2020 e933 Existing American Heart Association cardiopulmonary resuscitation (CPR) guidelines do not address the challenges of providing resuscitation in the setting of the coronavirus disease 2019 (COVID-19) global pandemic, wherein rescuers must continuously balance the immediate needs of the patients with their own safety. To address this gap, the American Heart Association, in collaboration with the American Academy of Pediatrics, American Association for Respiratory Care, American College of Emergency Physicians, The Society of Critical Care Anesthesiologists, and American Society of Anesthesiologists, and with the support of the American Association of Critical Care Nurses and National Association of EMS Physicians, has compiled interim guidance to help rescuers treat individuals with cardiac arrest with suspected or confirmed COVID-19.Over the past 2 decades, there has been a steady improvement in survival after cardiac arrest occurring both inside and outside the hospital. 1 That success has relied on initiating proven resuscitation interventions such as high-quality chest compressions and defibrillation within seconds to minutes. The evolving and expanding outbreak of severe acute respiratory syndrome coronavirus 2 infections has created important challenges to such resuscitation efforts and requires potential modifications of established processes and practices. The challenge is to ensure that patients with or without COVID-19 who experience cardiac arrest get the best possible chance of survival without compromising the safety of rescuers, who will be needed to care for future patients. Complicating the emergency response to both out-of-hospital and in-hospital cardiac arrest is that COVID-19 is highly transmissible, particularly during resuscitation, and carries a high morbidity and mortality.Approximately 12% to 19% of COVID-positive patients require hospital admission, and 3% to 6% become critically ill. [2][3][4] Hypoxemic respiratory failure secondary to acute respiratory distress syndrome, myocardial injury, ventricular arrhythmias, and shock are common among critically ill patients and predispose them to cardiac arrest, [5][6][7][8] as do some of the proposed treatments such as hydroxychloroquine and azithromycin, which can prolong the QT. 9 With infections currently growing exponentially in the United States and internationally, the percentage of patients with cardiac arrests and COVID-19 is likely to increase.Healthcare workers are already the highest-risk profession for contracting the disease. 10 This risk is compounded by worldwide shortages of personal protective equipment (PPE). Resuscitations carry added risk to healthcare workers for many reasons. First, the administration of CPR involves performing numerous aerosol-generating procedures, including chest compressions, positive-pressure ventilation, and establishment of an advanced airway. During those procedures, viral particles can remain suspended in the air with a half-life of ≈1 hour and

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