The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest

Balan, Prakash; Hsi, Brian; Thangam, Manoj; Zhao, Yelin; Monlezun, Dominique; Arain, Salman A.; Charitakis, Konstantinos; Dhoble, Abhijeet; Johnson, Nils P.; Anderson, H. Vernon; Persse, David; Warner, Mark; Ostermayer, Daniel; Prater, Sean N.; Wang, Henry E.; Doshi, Pratik

doi:10.1016/j.resuscitation.2019.09.009

Cited by 28 publications

(19 citation statements)

References 38 publications

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“…Most studies of OHCA survival so far, including ours, have focused on finding out what factors influence survival. However, few studies have recently been conducted to quantify the relative impacts of these factors to develop a predictive model of OHCA survival [ 41 , 42 ]. This predictive model can be used for stratifying each patient according to risk.…”

Section: Discussionmentioning

confidence: 99%

Effects of Prehospital Factors on Survival of Out-Of-Hospital Cardiac Arrest Patients: Age-Dependent Patterns

Rhee

Kim

Lee

2020

IJERPH

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Many prehospital factors that are known to influence survival rates after out-of-hospital cardiac arrest (OHCA) have been rarely studied as to how their influence varies depending on the age. In this study, we tried to find out what prehospital factors affect the survival rate after OHCA by age groups and how large the effect size of those factors is in each age group. We used the South Korean OHCA registry, which includes information on various prehospital factors relating OHCA and final survival status. The association between prehospital factors and survival was explored through logistic regression analyses for each age group. The effects of prehospital factors vary depending on the patient’s age. Being witnessed was relatively more influential in younger patients and the presence of first responders became more important as patients became older. While bystander cardiopulmonary resuscitation (CPR) did not appear to significantly affect survival in younger people, use of an automated external defibrillator (AED) showed the largest effect size on the survival in all age groups. Since the pathophysiology and etiologies of OHCA vary according to age, more detailed information on life support by age is needed for the development and application of more specialized protocols for each age.

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Section: Discussionmentioning

confidence: 99%

Effects of Prehospital Factors on Survival of Out-Of-Hospital Cardiac Arrest Patients: Age-Dependent Patterns

Rhee

Kim

Lee

2020

IJERPH

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“…For the latter response additionally, the delay in defibrillation was approximated by 6.5 min for the 'bCPR + AED' response (objective EVapp: response within 5-8 min). The results of other studies were included in the survival estimations in case the comparator of the study (survival after EMS response) was comparable to the baseline case for Belgium (10% survival for EMS arriving within 10 min) or in case odds ratios based on extensive meta-analysis or literature review had been calculated [8,10,42]. Different studies reported very high average survival rates for bystander defibrillation (of shockable rhythms) of 35-70% [9,[11][12][13][14].…”

Section: Outcomes and Measurement Of Effectivenessmentioning

confidence: 99%

Cost-effectiveness of a novel smartphone application to mobilize first responders after witnessed OHCA in Belgium

Vercammen¹,

Moens

2020

Cost Eff Resour Alloc

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Background EVapp (Emergency Volunteer Application) is a Belgian smartphone application that mobilizes volunteers to perform cardiopulmonary resuscitation (CPR) and defibrillation with publicly available automatic external defibrillators (AED) after an emergency call for suspected out of hospital cardiac arrest (OHCA). The aim is to bridge the time before the arrival of the emergency services. Methods An accessible model was developed, using literature data, to simulate survival and cost-effectiveness of nation-wide EVapp implementation. Initial validation was performed using field data from a first pilot study of EVapp implementation in a city in Flanders, covering 2.5 years of implementation. Results Simulation of nation-wide EVapp implementation resulted in an additional yearly 910 QALY gained over the current baseline case scenario (worst case 632; best case 3204). The cost per QALY associated with EVapp implementation was comparable to the baseline scenario, i.e., 17 vs 18 k€ QALY−1. Conclusions EVapp implementation was associated with a positive balance on amount of QALY gained and cost of QALY. This was a consequence of both the lower healthcare costs for patients with good neurological outcome and the more efficient use of yet available resources, which did not outweigh the costs of operation.

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“…Balan et al 74 created a similar simple scoring system to shockable rhythm‐witness‐age‐pH, the cardiac arrest survival score (CASS), to predict in‐hospital mortality, not neurologic outcome, after out‐of‐hospital cardiac arrest. The score combined age (>75, 3 points), unwitnessed arrest (4 points), arrest at home (2.5 points), no bystander CPR (2.5 points), and non‐shockable initial rhythm (8 points) to produce a linear score associated with in‐hospital mortality with an area under the curve of 0.717 and 0.708 for the derivation and validation cohorts, respectively.…”

Section: Scoring Systemsmentioning

confidence: 99%

Neurologic prognostication after resuscitation from cardiac arrest

2020

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Out‐of‐hospital cardiac arrest remains a leading cause of mortality in the United States, and the majority of patients who die after achieving return of spontaneous circulation die from withdrawal of care due to a perceived poor neurologic prognosis. Unfortunately, withdrawal of care often occurs during the first day of admission and research suggests this early withdrawal of care may be premature and result in unnecessary deaths for patients who would have made a full neurologic recovery. In this review, we explore the evidence for neurologic prognostication in the emergency department for patients who achieve return of spontaneous circulation after an out‐of‐hospital cardiac arrest.

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The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest

Cited by 28 publications

References 38 publications

Effects of Prehospital Factors on Survival of Out-Of-Hospital Cardiac Arrest Patients: Age-Dependent Patterns

Effects of Prehospital Factors on Survival of Out-Of-Hospital Cardiac Arrest Patients: Age-Dependent Patterns

Cost-effectiveness of a novel smartphone application to mobilize first responders after witnessed OHCA in Belgium

Neurologic prognostication after resuscitation from cardiac arrest

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