European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015

Nolan, Jerry P.; Soar, Jasmeet; Cariou, Alain; Cronberg, Tobias; Moulaert, Véronique; Deakin, Charles D.; Böttiger, Bernd W.; Friberg, Hans; Sunde, Kjetil; Sandroni, Claudio

doi:10.1016/j.resuscitation.2015.07.018

Cited by 970 publications

(968 citation statements)

References 433 publications

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“…[19] Delayed defibrillation has been associated with a decreased survival rate, with a 10% decrease in survival for each minute. [20] One of the limitations of this study was the inability to use initial rhythm in prognostication due to insufficient data. However, there are defibrillators in all the wards and in ED in our hospital, and portable emergency kits are available at certain points.…”

Section: Discussionmentioning

confidence: 96%

Mortality in Code Blue; can Apache II and PRISM scores be markers for prognostication?

Bakan¹,

Karaören

Tomruk³

et al. 2017

Ulus Travma Acil Cerrahi Derg

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BACKGROUND: Code blue (CB) is an emergency call system developed to respond to cardiac and respiratory arrest in hospitals. However, in literature, no scoring system has been reported that can predict mortality in CB procedures. In this study, we aimed to investigate the effectiveness of estimated APACHE II and PRISM scores in the prediction of mortality in patients assessed using CB to retrospectively analyze CB calls.

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Section: Discussionmentioning

confidence: 96%

Mortality in Code Blue; can Apache II and PRISM scores be markers for prognostication?

Bakan¹,

Karaören

Tomruk³

et al. 2017

Ulus Travma Acil Cerrahi Derg

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“…33 Hospital care could account for some of the variation in survival to discharge observed across services in this study. Specialist cardiac arrest centres may improve survival 34 and further research is needed.…”

Section: Discussionmentioning

confidence: 99%

Epidemiology and outcomes from out-of-hospital cardiac arrests in England

et al. 2017

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“…More than a half of cardiac arrests result from respiratory failure or hypovolemic shock, and 80% of patients with cardiac arrest show signs of deterioration in the 8 hours before cardiac arrest 4, 5, 6, 7, 8, 9. However, 209 000 in‐hospital cardiac arrests occur in the United States each year, and the survival discharge rate for patients with cardiac arrest is <20% worldwide 10, 11. Rapid response systems (RRSs) have been introduced in many hospitals to detect cardiac arrest using the track‐and‐trigger system (TTS) 12, 13…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

Kwon

Lee²,

Lee³

et al. 2018

JAHA

201

190

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BackgroundIn‐hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track‐and‐trigger systems are used to predict cardiac arrest early, they have limitations, with low sensitivity and high false‐alarm rates. We propose a deep learning–based early warning system that shows higher performance than the existing track‐and‐trigger systems.Methods and ResultsThis retrospective cohort study reviewed patients who were admitted to 2 hospitals from June 2010 to July 2017. A total of 52 131 patients were included. Specifically, a recurrent neural network was trained using data from June 2010 to January 2017. The result was tested using the data from February to July 2017. The primary outcome was cardiac arrest, and the secondary outcome was death without attempted resuscitation. As comparative measures, we used the area under the receiver operating characteristic curve (AUROC), the area under the precision–recall curve (AUPRC), and the net reclassification index. Furthermore, we evaluated sensitivity while varying the number of alarms. The deep learning–based early warning system (AUROC: 0.850; AUPRC: 0.044) significantly outperformed a modified early warning score (AUROC: 0.603; AUPRC: 0.003), a random forest algorithm (AUROC: 0.780; AUPRC: 0.014), and logistic regression (AUROC: 0.613; AUPRC: 0.007). Furthermore, the deep learning–based early warning system reduced the number of alarms by 82.2%, 13.5%, and 42.1% compared with the modified early warning system, random forest, and logistic regression, respectively, at the same sensitivity.ConclusionsAn algorithm based on deep learning had high sensitivity and a low false‐alarm rate for detection of patients with cardiac arrest in the multicenter study.

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European Resuscitation Council and European Society of Intensive Care Medicine Guidelines for Post-resuscitation Care 2015

Cited by 970 publications

References 433 publications

Mortality in Code Blue; can Apache II and PRISM scores be markers for prognostication?

Mortality in Code Blue; can Apache II and PRISM scores be markers for prognostication?

Epidemiology and outcomes from out-of-hospital cardiac arrests in England

An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest

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