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Background: Risk factors for bleeding complications during extracorporeal life support (ECLS) indicated for cardiac support remain poorly investigated. The aim is to develop and internally validate a prediction model to calculate the risk for bleeding complications in adult patients receiving veno‐arterial (V‐A) ECLS. Methods: Data of the Extracorporeal Life Support Organization registry of adult patients undergoing V‐A ECLS between 2010 and 2020 were analyzed. The primary outcome was bleeding complications recorded during V‐A ECLS. Multivariable logistic regression with backward stepwise elimination was used to develop the prediction model. Performance of the model was tested by discriminative ability and calibration with receiver operator characteristic, area under the curve, and visual inspection of the calibration plot. Internal validation was performed to detect overfitting of the model. Results: In total 28 767 adult patients were included, of which 29.0% developed bleeding complications. Sex, body mass index, surgical cannulation, pre‐ECLS respiratory and hemodynamic variables, pre‐ECLS support and interventions, and different type of diagnosis were included in the prediction model. This prediction model showed a predictive capability with an AUC of 0.66. Conclusion: The model is based on the largest cohort of V‐A ECLS patients and is the best available predictive model for bleeding events given the predictors that are available in V‐A ECLS compared to current literature. The model can help in identifying patients at high risk for bleeding complications and will help in developing further research and decision‐making in terms of anticoagulation management. External validation is warranted to extrapolate this model in the clinical setting. [ABSTRACT FROM AUTHOR]

Objective: Myocardial damage occurs in up to 25% of coronavirus disease 2019 (COVID‐19) cases. While veno‐venous extracorporeal life support (V‐V ECLS) is used as respiratory support, mechanical circulatory support (MCS) may be required for severe cardiac dysfunction. This systematic review summarizes the available literature regarding MCS use rates, disease drivers for MCS initiation, and MCS outcomes in COVID‐19 patients. Methods: PubMed/EMBASE were searched until October 14, 2021. Articles including adults receiving ECLS for COVID‐19 were included. The primary outcome was the rate of MCS use. Secondary outcomes included mortality at follow‐up, ECLS conversion rate, intubation‐to‐cannulation time, time on ECLS, cardiac diseases, use of inotropes, and vasopressors. Results: Twenty‐eight observational studies (comprising both ECLS‐only populations and ECLS patients as part of larger populations) included 4218 COVID‐19 patients (females: 28.8%; median age: 54.3 years, 95%CI: 50.7–57.8) of whom 2774 (65.8%) required ECLS with the majority (92.7%) on V‐V ECLS, 4.7% on veno‐arterial ECLS and/or Impella, and 2.6% on other ECLS. Acute heart failure, cardiogenic shock, and cardiac arrest were reported in 7.8%, 9.7%, and 6.6% of patients, respectively. Vasopressors were used in 37.2%. Overall, 3.1% of patients required an ECLS change from V‐V ECLS to MCS for heart failure, myocarditis, or myocardial infarction. The median ECLS duration was 15.9 days (95%CI: 13.9–16.3), with an overall survival of 54.6% and 28.1% in V‐V ECLS and MCS patients. One study reported 61.1% survival with oxy‐right ventricular assist device. Conclusion: MCS use for cardiocirculatory compromise has been reported in 7.3% of COVID‐19 patients requiring ECLS, which is a lower percentage compared to the incidence of any severe cardiocirculatory complication. Based on the poor survival rates, further investigations are warranted to establish the most appropriated indications and timing for MCS in COVID‐19. [ABSTRACT FROM AUTHOR]

Background: During extracorporeal life support (ECLS), bleeding is one of the most frequent complications, associated with high morbidity and increased mortality, despite continuous improvements in devices and patient care. Risk factors for bleeding complications in veno‐venous (V‐V) ECLS applied for respiratory support have been poorly investigated. We aim to develop and internally validate a prediction model to calculate the risk for bleeding complications in adult patients receiving V‐V ECLS support. Methods: Data from adult patients reported to the extracorporeal life support organization (ELSO) registry between the years 2010 and 2020 were analyzed. The primary outcome was bleeding complications recorded during V‐V ECLS. Multivariable logistic regression with backward stepwise elimination was used to develop the predictive model. The performance of the model was tested by discriminative ability and calibration with receiver operating characteristic curves and visual inspection of the calibration plot. Results: In total, 18 658 adult patients were included, of which 3 933 (21.1%) developed bleeding complications. The prediction model showed a prediction of bleeding complications with an AUC of 0.63. Pre‐ECLS arrest, surgical cannulation, lactate, pO2, HCO3, ventilation rate, mean airway pressure, pre‐ECLS cardiopulmonary bypass or renal replacement therapy, pre‐ECLS surgical interventions, and different types of diagnosis were included in the prediction model. Conclusions: The model is based on the largest cohort of V‐V ECLS patients and reveals the most favorable predictive value addressing bleeding events given the predictors that are feasible and when compared to the current literature. This model will help identify patients at risk of bleeding complications, and decision making in terms of anticoagulation and hemostatic management. [ABSTRACT FROM AUTHOR]

Introduction: Although the technology used for extracorporeal life support (ECLS) has improved greatly in recent years, the application of these devices to the patient is quite complex and requires extensive training of team members both individually and together. Human factors is an area that addresses the activities, contexts, environments, and tools which interact with human behavior in determining overall system performance. Hypothesis: Analyses of the cognitive behavior of ECLS teams and individual members of these teams with respect to the occurrence of human errors may identify additional opportunities to enhance safety in delivery of ECLS. Results: The aim of this article is to support health‐care practitioners who perform ECLS, or who are starting an ECLS program, by establishing standards for the safe and efficient use of ECLS with a focus on human factor issues. Other key concepts include the importance of ECLS team leadership and management, as well as controlling the environment and the system to optimize patient care. Conclusion: Expertise from other industries is extrapolated to improve patient safety through the application of simulation training to reduce error propagation and improve outcomes. [ABSTRACT FROM AUTHOR]