Clinical Antecedents to In-Hospital Cardiopulmonary Arrest

Schein, Roland M. H.; Hazday, Nelson; Pena, M. A.; Ruben, Bradley H.; Sprung, Charles L.

doi:10.1378/chest.98.6.1388

Cited by 787 publications

(500 citation statements)

References 24 publications

Supporting

Mentioning

466

Contrasting

Unclassified

Order By: Relevance

“…These were based in part on early literature on the clinical markers that most often precede clinical deterioration. 14,15 In addition, 2 much broader categories for RRT activation were added ("Inability to reach the patient's primary team of treating physicians for any of the above" and "Any potentially serious medical errors or adverse events") in order to minimize the need for a very specific physiologic definition to be met in order to activate the team. Physicians, nurses, and other staff with significant daily contact with inpatients and outpatients were in-serviced about the purpose of the RRT and how to activate the system via the hospital paging operator.…”

Section: Methodsmentioning

confidence: 99%

Establishing a rapid response team (RRT) in an academic hospital: One year's experience

King

Horvath

Shulkin

2006

Journal of Hospital Medicine

View full text Add to dashboard Cite

BACKGROUNDRapid response teams and medical emergency teams have been utilized to rapidly manage seriously ill patients at risk of cardiopulmonary arrest and other high‐risk conditions but have not been extensively described in the American medical literature.OBJECTIVESTo describe a full year's experience of implementing a rapid response team (RRT) in an academic medical center.DESIGNRetrospective analysis of our hospital's RRT database and description of the implementation process from July 2004 to July 2005.SETTINGUrban, academic medical center.RESULTSThe RRT system was activated for 307 potentially unstable patients. The most common reasons for an RRT activation were cardiac, respiratory, and neurological conditions. At least 37% of RRT calls were for off‐unit inpatients and to outpatient/common areas frequented by outpatients and visitors, whereas at least 42% occurred in inpatient units. Most RRT calls, 82.9%, occurred during daytime hours. In the opinion of RRT leaders 98% of the evaluated calls were appropriate and 85% of the RRT responses resulted in the prevention of further clinical deterioration.CONCLUSIONSAn RRT was introduced into an academic medical center, and the results suggested it is capable of preventing clinical deterioration in unstable patients and may have the potential to decrease the frequency of cardiac arrests. The RRT also may fill a gap in patient safety by enabling rapid triage and expedited treatment of off‐unit inpatients, outpatients, and visitors. The keys to the early success of our implementation of an RRT were multidisciplinary input and improvements made in real time. Journal of Hospital Medicine 2006;1:296–305. © 2006 Society of Hospital Medicine.

show abstract

Section: Methodsmentioning

confidence: 99%

Establishing a rapid response team (RRT) in an academic hospital: One year's experience

King

Horvath

Shulkin

2006

Journal of Hospital Medicine

View full text Add to dashboard Cite

show abstract

“…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%

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

Kwon

Lee²,

Lee³

et al. 2018

JAHA

221

205

View full text Add to dashboard Cite

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.

show abstract

“…It has been demonstrated that there are clear signs of patient deterioration hours before events such as cardiopulmonary arrest (Schein et al . 1990). Regular assessment of multi‐parameter vital signs has been shown to be important in identifying patients at risk for serious adverse events, allowing time for nursing interventions to prevent FTR (Storm‐Versloot et al .…”

Section: Introductionmentioning

confidence: 99%

Nursing assessment of continuous vital sign surveillance to improve patient safety on the medical/surgical unit

Watkins¹,

Whisman

Booker³

2015

Journal of Clinical Nursing

View full text Add to dashboard Cite

Aims and objectivesEvaluate continuous vital sign surveillance as a tool to improve patient safety in the medical/surgical unit.BackgroundFailure‐to‐rescue is an important measure of hospital quality. Patient deterioration is often preceded by changes in vital signs. However, continuous multi‐parameter vital sign monitoring may decrease patient safety with an abundance of unnecessary alarms.DesignProspective observational study at two geographically disperse hospitals in a single hospital system.MethodsA multi‐parameter vital sign monitoring system was installed in a medical/surgical unit in Utah and one in Alabama providing continuous display of SpO2, heart rate, blood pressure and respiration rate on a central station. Alarm thresholds and time to alert annunciations were set based on prior analysis of the distribution of each vital sign. At the end of 4 weeks, nurses completed a survey on their experience. An average alert per patient, per day was determined retrospectively from the saved vital signs data and knowledge of the alarm settings.ResultsNinety‐two per cent of the nurses agreed that the number of alarms and alerts were appropriate; 54% strongly agreed. On average, both units experienced 10·8 alarms per patient, per day. One hundred per cent agreed the monitor provided valuable patient data that increased patient safety; 79% strongly agreed.ConclusionsContinuous, multi‐parameter patient monitoring could be performed on medical/surgical units with a small and appropriate level of alarms. Continuous vital sign assessment may have initiated nursing interventions that prevented failure‐to‐rescue events. Nurses surveyed unanimously agreed that continuous vital sign surveillance will help enhance patient safety.Relevance to clinical practiceNursing response to abnormal vital signs is one of the most important levers in patient safety, by providing timely recognition of early clinical deterioration. This occurs through diligent nursing surveillance, involving assessment, interpretation of data, recognition of a problem and meaningful response.

show abstract

Clinical Antecedents to In-Hospital Cardiopulmonary Arrest

Cited by 787 publications

References 24 publications

Establishing a rapid response team (RRT) in an academic hospital: One year's experience

Establishing a rapid response team (RRT) in an academic hospital: One year's experience

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

Nursing assessment of continuous vital sign surveillance to improve patient safety on the medical/surgical unit

Contact Info

Product

Resources

About