Resuscitation
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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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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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Review Meta Analysis
Ventilation devices for neonatal resuscitation at birth: a systematic review and meta-analysis.
Initial management of inadequate adaptation to extrauterine life relies on non-invasive respiratory support. Two types of devices are available: fixed pressure devices (FPD; T-pieces or ventilators) and hand driven pressure devices (HDPD; self- or flow-inflating bags). This systematic review and meta-analysis aims to compare clinical outcomes after neonatal resuscitation according to device type. ⋯ Resuscitation at birth with FPD improves respiratory transition and decreases BPD with a very low to moderate certainty of evidence. There is suggestion of decreases in mortality and cPVL. Further studies are still needed to confirm those results.
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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.