“Identifying the hospitalised patient in crisis”—A consensus conference on the afferent limb of Rapid Response Systems

DeVita, Michael A.; Smith, Gary B.; Adam, Sheila; Adams-Pizarro, Inga; Buist, Michael; Bellomo, Rinaldo; Bonello, Robert S.; Cerchiari, Erga; Farlow, Barbara; Goldsmith, Donna; Haskell, Helen; Hillman, Kenneth; Howell, Michael D.; Hravnak, Marilyn; Hunt, Elizabeth A.; Hvarfner, Andreas; Kellett, John; Lighthall, Geoffrey K.; Lippert, Anne; Lippert, Freddy; Mahroof, Razeen; Myers, Jennifer S.; Rosen, Mark J.; Reynolds, Stuart F.; Rotondi, Armando J.; Rubulotta, Francesca; Winters, Bradford D.

doi:10.1016/j.resuscitation.2009.12.008

Cited by 302 publications

(237 citation statements)

References 60 publications

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“…Considering limited resources for an RRS, if the MACHP is set to 0.04, as in Figure 4, sensitivity of the DEWS and the MEWS is 42.7% and 4.0%, respectively. This is why the existing TTSs were not successfully applied to RRSs in some hospitals and is consistent with previous studies 17, 42…”

Section: Discussionsupporting

confidence: 89%

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

221

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: Discussionsupporting

confidence: 89%

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

221

205

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“…HR is an important component of many pediatric care guidelines and clinical tools including early warning scores and medical emergency team calling criteria, [1][2][3][4][5][6][7][8] criteria for the diagnosis of systemic inflammatory response syndrome and sepsis, 9 emergency department (ED) triage scores, 10,11 and Pediatric Advanced Life Support guidelines. 12 Although it is well established that HR increases with increasing body temperature, there is little guidance available to clinicians regarding how to account for this relationship, if at all, when using tools and guidelines that include HR cut points.…”

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confidence: 99%

Heart Rates in Hospitalized Children by Age and Body Temperature

Daymont

Bonafide²,

Brady

2015

Pediatrics

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BACKGROUND AND OBJECTIVES: Heart rate (HR) is frequently used by clinicians in the hospital to assess a patient's severity of illness and make treatment decisions. We sought to develop percentiles that characterize the relationship of expected HR by age and body temperature in hospitalized children and to compare these percentiles with published references in both primary care and emergency department (ED) settings.

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“…22 In an attempt to improve alert relevancy and accuracy, newly designed integrated monitoring systems for ICUs use alert algorithms and trend analysis performed on data received from multiple vital signs monitors. [23][24][25][26][27] With the evident need to continuously monitor patients on hospital floors, 13,28 there is a clear gap to-date in such experience and a need to study the implications for patients and staff as we try to optimize the monitoring tools for this environment.…”

Section: Discussionmentioning

confidence: 99%

“…[10][11][12] Taking these facts together with the observed shortcomings of most current rapid response systems has led experts to call for a shift of focus from the efferent limb of the RRS (the response team) to the afferent limb (the means of detecting patients at risk and obtaining help). 13 A consensus conference held in 2008 emphasized the importance of accurate monitoring of vital signs for all hospitalized patients, and at the same time recommended, if practical and affordable, that all patients be monitored continuously by monitoring technology, which improves patient comfort, is easy to use, and reduces the number of false alerts. 13 Technology applications that allow for continuous vital sign monitoring designed for non-ICU settings may help hospitals achieve meaningful results by providing earlier detection of clinical deterioration, either when implemented as part of an RRS or as a stand-alone system.…”

mentioning

confidence: 99%

“…13 A consensus conference held in 2008 emphasized the importance of accurate monitoring of vital signs for all hospitalized patients, and at the same time recommended, if practical and affordable, that all patients be monitored continuously by monitoring technology, which improves patient comfort, is easy to use, and reduces the number of false alerts. 13 Technology applications that allow for continuous vital sign monitoring designed for non-ICU settings may help hospitals achieve meaningful results by providing earlier detection of clinical deterioration, either when implemented as part of an RRS or as a stand-alone system. To fit this description, monitors would have to be easy to use by the staff, impose little limitation on the patient, and be capable of trend analyses so that they minimize the incidence of false alerts and alert fatigue.…”

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confidence: 99%

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Early recognition of acutely deteriorating patients in non‐intensive care units: Assessment of an innovative monitoring technology

Zimlichman

Szyper‐Kravitz

Shinar

et al. 2012

Journal of Hospital Medicine

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BACKGROUND:Continuous vital sign monitoring has the potential to detect early clinical deterioration. While commonly employed in the intensive care unit (ICU), accurate and noninvasive monitoring technology suitable for floor patients has yet to be used reliably.OBJECTIVE:To establish the accuracy of the Earlysense continuous monitoring system in predicting clinical deterioration.DESIGN:Noninterventional prospective study with retrospective data analysis.SETTING:Two medical wards in 2 academic medical centers.PATIENTS:Patients admitted to a medical ward with a diagnosis of an acute respiratory condition.INTERVENTION:Enrolled patients were monitored for heart rate (HR) and respiration rate (RR) by the Earlysense monitor with the alerts turned off.MEASUREMENTS:Retrospective analysis of vital sign data was performed on a derivation cohort to identify optimal cutoffs for threshold and 24‐hour trend alerts. This was internally validated through correlation with clinical events recognized through chart review.RESULTS:Of 113 patients included in the study, 9 suffered major clinical deterioration. Alerts were found to be infrequent (2.7 and 0.2 alerts per patient‐day for threshold and trend alert, respectively). For the threshold alerts, sensitivity and specificity in predicting deterioration was found to be 82% and 67%, respectively, for HR and 64% and 81%, respectively, for RR. For trend alerts, sensitivity and specificity were 78% and 90% for HR, and 100% and 64% for RR, respectively.CONCLUSIONS:The Earlysense monitor was able to continuously measure RR and HR, providing low alert frequency. The current study provides data supporting the ability of this system to accurately predict patient deterioration. Journal of Hospital Medicine 2012; © 2012 Society of Hospital Medicine

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“Identifying the hospitalised patient in crisis”—A consensus conference on the afferent limb of Rapid Response Systems

Cited by 302 publications

References 60 publications

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

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

Heart Rates in Hospitalized Children by Age and Body Temperature

Early recognition of acutely deteriorating patients in non‐intensive care units: Assessment of an innovative monitoring technology

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