Resuscitation
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Cardiac arrest (CA) is a common reason for admission to the cardiac intensive care unit (CICU), though the relative burden of morbidity, mortality, and resource use between admissions with in-hospital (IH) and out-of-hospital (OH) CA is unknown. We compared characteristics, care patterns, and outcomes of admissions to contemporary CICUs after IHCA or OHCA. ⋯ Despite a greater burden of comorbidities, CICU admissions after IHCA have lower lactate, greater invasive therapy utilization, and lower crude mortality than admissions after OHCA.
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Growth of machine learning (ML) in healthcare has increased potential for observational data to guide clinical practice systematically. This can create self-fulfilling prophecies (SFPs), which arise when prediction of an outcome increases the chance that the outcome occurs. ⋯ There is a need for broad recognition of SFPs as ML is increasingly applied in resuscitation science and across medicine. Acknowledging this challenge is crucial to inform research and practice that can transform ML from a tool that risks obfuscating and compounding SFPs into one that sheds light on and mitigates SFPs.
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Risk-standardized survival rates (RSSR) for in-hospital cardiac arrest (IHCA) have been widely used for hospital benchmarking and research. The novel coronavirus 2019 (COVID-19) pandemic has led to a substantial decline in IHCA survival as COVID-19 infection is associated with markedly lower survival. Therefore, there is a need to update the model for computing RSSRs for IHCA given the COVID-19 pandemic. ⋯ We have derived and validated an updated model to risk-standardize hospital rates of survival for IHCA. The updated model yielded RSSRs that were similar to the initial model for IHCAs in the pre-pandemic period and can be used for supporting ongoing efforts to benchmark hospitals and facilitate research that uses data from either before or after the emergence of COVID-19.
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A machine-learning model trained to recognize emergency calls regarding Out-of-Hospital Cardiac Arrest (OHCA) was tested in clinical practice at Copenhagen Emergency Medical Services (EMS) from September 2018 to December 2019. We aimed to investigate emergency call characteristics where the machine-learning model failed to recognize OHCA or misinterpreted a call as being OHCA. ⋯ Continuous optimization of the language model is needed to improve the prediction of OHCA and thereby improve sensitivity and specificity of the machine-learning model on recognising OHCA in emergency telephone calls.
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Observational Study
Doppler Ultrasound Peak Systolic Velocity versus End Tidal Carbon Dioxide during Pulse Checks in Cardiac Arrest1.
An accurate, non-invasive measure of return of spontaneous circulation (ROSC) is needed to improve management of cardiac arrest patients. ⋯ During a pulse check, Doppler ultrasound PSV outperformed ETCO2 for correlation with SBP and accuracy in detecting ROSC with SBP ≥ 60 mmHg.