Promising candidates for extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest

Shin, Yo Sep; Kim, Youn-Jung; Ryoo, Seung Mok; Sohn, Chang Hwan; Seo, Dong Woo; Kim, Won Young

doi:10.1038/s41598-020-79283-1

Cited by 15 publications

(19 citation statements)

References 33 publications

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“…Increase in population, urbanization, demand for expanded agriculture, modernized living standards have increased the demand for water 1 . Water is a renewable resource, and it is transferred between the ocean, atmosphere, and the land (through rainfall) 2 . Rainfall is a life-sustaining water resource, and its variability influences the water availability across any region.…”

Section: Introductionmentioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.

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Section: Introductionmentioning

confidence: 99%

Automated predictive analytics tool for rainfall forecasting

Raval

Sivashanmugam

Pham

et al. 2021

Sci Rep

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“…However, determining optimal threshold in practice is challenging. Although we dichotomised no-flow time based on the previous studies and real-world practice for initiating ECPR,4–6 the indication for ECPR remains unestablished. Moreover, the optimal threshold depends on the situation where the marker is applied.…”

Section: Discussionmentioning

confidence: 99%

“…Because ECPR requires considerable resources, current recommendations suggest using ECPR for selected patients who have the best chances of successful outcomes. Although the indication criteria have not been established, a criterion for no-flow time of <5 min or <10 min has been proposed 4–6. However, estimating the no-flow time is uncertain in unwitnessed OHCAs which account for 33.3%–55.1% of OHCA cases 7–9.…”

Section: Introductionmentioning

confidence: 99%

“…However, estimating the no-flow time is uncertain in unwitnessed OHCAs which account for 33.3%–55.1% of OHCA cases 7–9. Patients with unwitnessed OHCA are generally presumed to have poor outcomes and are often excluded from the candidacy for ECPR 5 10. To this end, previous studies have investigated markers of short no-flow time after OHCA, which helps to identify candidacy for resource-intensive treatment.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of initial shockable rhythm as an indicator of short no-flow time in cardiac arrest: a national registry study

et al. 2022

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BackgroundThe duration from collapse to initiation of cardiopulmonary resuscitation (no-flow time) is one of the most important determinants of outcomes after out-of-hospital cardiac arrest (OHCA). Initial shockable cardiac rhythm (ventricular fibrillation or ventricular tachycardia) is reported to be a marker of short no-flow time; however, there is conflicting evidence regarding the impact of initial shockable cardiac rhythm on treatment decisions. We investigated the association between initial shockable cardiac rhythm and the no-flow time and evaluated whether initial shockable cardiac rhythm can be a marker of short no-flow time in patients with OHCA.MethodsPatients aged 18 years and older experiencing OHCA between 2010 and 2016 were selected from a nationwide population-based Japanese database. The association between the no-flow time duration and initial shockable cardiac rhythm was evaluated. Diagnostic accuracy was evaluated using the sensitivity, specificity and positive predictive value.ResultsA total of 177 634 patients were eligible for the analysis. The median age was 77 years (58.3%, men). Initial shockable cardiac rhythm was recorded in 11.8% of the patients. No-flow time duration was significantly associated with lower probability of initial shockable cardiac rhythm, with an adjusted OR of 0.97 (95% CI 0.96 to 0.97) per additional minute. The sensitivity, specificity and positive predictive value of initial shockable cardiac rhythm to identify a no-flow time of <5 min were 0.12 (95% CI 0.12 to 0.12), 0.88 (95% CI 0.88 to 0.89) and 0.35 (95% CI 0.34 to 0.35), respectively. The positive predictive values were 0.90, 0.95 and 0.99 with no-flow times of 15, 18 and 28 min, respectively.ConclusionsAlthough there was a significant association between initial shockable cardiac rhythm and no-flow time duration, initial shockable cardiac rhythm was not reliable when solely used as a surrogate of a short no-flow time duration after OHCA.

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“…Observational studies reported that E‐CPR may improve survival and neurological outcomes in patients with OHCA when compared to C‐CPR 9–13 . However, other studies showed small or no effect on survival and systematic reviews conducted on the topic yielded contrasting results 14,15 .…”

Section: Introductionmentioning

confidence: 99%

Extracorporeal cardiopulmonary resuscitation for out‐of‐hospital cardiac arrest: A systematic review and meta‐analysis of randomized and propensity score‐matched studies

et al. 2022

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Background:In selected patients with refractory out-of-hospital cardiac arrest, extracorporeal cardiopulmonary resuscitation represents a promising approach when conventional cardiopulmonary resuscitation fails to achieve return of spontaneous circulation. This systematic review and meta-analysis aimed to compare extracorporeal cardiopulmonary resuscitation to conventional cardiopulmonary resuscitation. Methods: We searched PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials up to November 28, 2021, for randomized trials and observational studies reporting propensity score-matched data and comparing adults with out-of-hospital cardiac arrest treated with extracorporeal cardiopulmonary resuscitation with those treated with conventional cardiopulmonary resuscitation. The primary outcome was survival with favorable neurological outcome at the longest follow-up available. Secondary outcomes were survival at the longest follow-up available and survival at hospital discharge/30 days. Results: We included six studies, two randomized and four propensity scorematched studies. Patients treated with extracorporeal cardiopulmonary resuscitation had higher rates of survival with favorable neurological outcome (81/584 [14%] vs. 46/593 [7.8%]; OR = 2.11; 95% CI, 1.41-3.15; p < 0.001, number needed to treat 16) and of survival (131/584 [22%] vs. 102/593 [17%]; OR = 1.40; 95% CI, 1.05-1.87; p = 0.02) at the longest follow-up available compared with conventional cardiopulmonary resuscitation. Survival at hospital discharge/30 days was similar between the two groups (142/584 [24%] vs. 122/593 [21%]; OR = 1.26; 95% CI, 0.95-1.66; p = 0.10). Conclusions: Evidence from randomized trials and propensity score-matched studies suggests increased survival and favorable neurological outcome in patients with refractory out-of-hospital cardiac arrest treated with extracorporeal

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Promising candidates for extracorporeal cardiopulmonary resuscitation for out-of-hospital cardiac arrest

Cited by 15 publications

References 33 publications

Automated predictive analytics tool for rainfall forecasting

Automated predictive analytics tool for rainfall forecasting

Evaluation of initial shockable rhythm as an indicator of short no-flow time in cardiac arrest: a national registry study

Extracorporeal cardiopulmonary resuscitation for out‐of‐hospital cardiac arrest: A systematic review and meta‐analysis of randomized and propensity score‐matched studies

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