A comparison of Antecedents to Cardiac Arrests, Deaths and EMergency Intensive care Admissions in Australia and New Zealand, and the United Kingdom—the ACADEMIA study

Kause, Juliane; Smith, Gary B.; Prytherch, David; Parr, Michael; Flabouris, Arthas; Hillman, Ken

doi:10.1016/j.resuscitation.2004.05.016

Cited by 577 publications

(368 citation statements)

References 19 publications

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“…These vital signs are associated with adverse clinical outcomes and are measured periodically and frequently 6, 16, 26. Furthermore, they are objective values that are barely affected by medical staff measuring them 27.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

223

205

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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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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

223

205

View full text Add to dashboard Cite

show abstract

“…1 Postsurgical physiologic deterioration or alterations of normal hormonal and metabolic physiology may contribute to respiratory failure and lead to eventual unplanned reintubation. 2,3 In modern health care, quality is not only expected but is also reportable. Unplanned reintubation is an identifiable event in a patient's postoperative course and serves as an outcome marker for quality improvement.…”

Section: Introductionmentioning

confidence: 99%

Association of Unplanned Reintubation with Higher Mortality in Old, Frail Patients: A National Surgical Quality-Improvement Program Analysis

Karamanos

Schmoekel

Blyden

et al. 2016

TPJ

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Background: Unplanned postoperative reintubation increases the risk of mortality, but associated factors are unclear.Objective: To elucidate factors associated with increased mortality risk in patients with unplanned postoperative reintubation.Design: Retrospective study. Patients older than 40 years who underwent unplanned reintubation from 2005 to 2010 were identified using the American College of Surgeons National Surgical Quality Improvement Program database. Multiple regression models were used to examine the impact on mortality of factors that included the modified frailty index (mFI) we developed, American Society of Anesthesiologists (ASA) score, age decile, and days to reintubation.Main Outcome Measure: Mortality.Results: A total of 17,051 postoperative reintubations in adults were analyzed. Overall mortality was 29.4% (n = 5009). On postoperative day 1, 4434 patients were reintubated and 878 (19.8%) died. On postoperative day 7 and beyond, 6329 patients were reintubated and 2215 (35.0%) died. Increasing mFI resulted in increasing incidence of mortality (mFl of 0 = 20.5% mortality vs mFl of 0.37-0.45 = 41.7% mortality). As ASA score increased from 1 to 5, reintubation was associated with a mortality of 12.1% to 41.6%, respectively. Similarly, increasing age decile was associated with increasing incidence of mortality (40-49 years, 17.9% vs 80-89 years, 42.1%). After adjustment for confounding factors, mFI, ASA score, age decile, and increasing number of days to reintubation were independently and significantly associated with increased mortality in the study population.Conclusion: Among patients who underwent unplanned reintubation, older and more frail patients had an increased risk of mortality.

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“…Research in adults demonstrated that cardiopulmonary arrest or emergency admissions to Intensive Care were often preceded by a period of physiological instability which, once recognised, provided an opportunity for the healthcare team to intervene to improve outcome (Hodgetts et al, 2002;Kause et al, 2004). A similar window of opportunity may exist for hospitalised children (Haines, 2005;Tasker, 2005;Tume, 2004Tume, , 2006.…”

Section: Introductionmentioning

confidence: 99%