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Effect of Time to Treatment With Antiarrhythmic Drugs on Return of Spontaneous Circulation in Shock-Refractory Out-of-Hospital Cardiac Arrest.

ABSTRACT: Background The effects of amiodarone and lidocaine on the return of spontaneous circulation (ROSC) in relation to time to treatment in patients with out-of-hospital cardiac arrest is not known. We conducted a post hoc analysis of the ROC ALPS (Resuscitation Outcomes Consortium Amiodarone, Lidocaine, Placebo) randomized controlled trial examining the association of time to treatment (drug or placebo) with ROSC at hospital arrival. Methods and Results In the trial, adults with nontraumatic out-of-hospital cardiac arrest with initial refractory ventricular fibrillation or pulseless ventricular tachycardia after at least 1 defibrillation were randomly assigned to receive amiodarone, lidocaine, or placebo. We used logistic regression to examine the association of time to treatment (911 call to study drug administration) with ROSC. An interaction term between treatment and time to treatment was included to determine the potential effect of time on treatment effects. Overall, 1112 (36.7%) patients had ROSC at hospital arrival (350 in the amiodarone arm, 396 in the lidocaine arm, and 366 in the placebo arm). The proportion of patients who had ROSC decreased as time to drug administration increased, in patients treated with amiodarone (odds ratio, 0.92; 95% CI, 0.90-0.94 per minute increase), lidocaine (odds ratio, 0.95; 95% CI, 0.93-0.96), and placebo (odds ratio, 0.95; 95% CI, 0.93-0.96). With shorter times to drug administration, the proportion with ROSC was higher in amiodarone versus placebo recipients. Conclusions The probability of ROSC decreased as time to drug administration increased. The effect of amiodarone but not lidocaine to restore ROSC declined with longer times to drug administration, potentially attributable to its adverse hemodynamic effects.

SUBMITTER: Rahimi M

PROVIDER: S-EPMC9075276 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:This study aimed to identify neurological and pathophysiological factors that predicted return of spontaneous circulation (ROSC) among patients with out-of-hospital cardiac arrest (OHCA). This prospective 1-year observational study evaluated patients with cardiogenic OHCA who were admitted to a tertiary medical center, Nippon Medical School Hospital. Physiological and neurological examinations were performed at admission for quantitative infrared pupillometry (measured with NPi-200, NeurOptics, CA, USA), arterial blood gas, and blood chemistry. Simultaneous blood samples were also collected to determine levels of neuron-specific enolase (NSE), S-100b, phosphorylated neurofilament heavy subunit, and interleukin-6. In-hospital standard advanced cardiac life support was performed for 30?minutes.The ROSC (n?=?26) and non-ROSC (n?=?26) groups were compared, which a revealed significantly higher pupillary light reflex ratio, which was defined as the percent change between maximum pupil diameter before light stimuli and minimum pupil diameter after light stimuli, in the ROSC group (median: 1.3% [interquartile range (IQR): 0.0-2.0%] vs. non-ROSC: (median: 0%), (Cut-off: 0.63%). Furthermore, NSE provided the great sensitivity and specificity for predicting ROSC, with an area under the receiver operating characteristic curve of 0.86, which was created by plotting sensitivity and 1-specificity. Multivariable logistic regression analyses revealed that the independent predictors of ROSC were maximum pupillary diameter (odds ratio: 0.25, 95% confidence interval: 0.07-0.94, P?=?0.04) and NSE at admission (odds ratio: 0.96, 95% confidence interval: 0.93-0.99, P?=?0.04). Pupillary diameter was also significantly correlated with NSE concentrations (r?=?0.31, P?=?0.027). Conclusively, the strongest predictors of ROSC among patients with OHCA were accurate pupillary diameter and a neuronal biomarker, NSE. Quantitative pupillometry may help guide the decision to terminate resuscitation in emergency departments using a neuropathological rationale. Further large-scale studies are needed.

Project description:IntroductionThe benefit of medications used in out-of-hospital, shock-refractory cardiac arrest remains controversial. This study aims to compare the treatment outcomes of medications for out-of-hospital, shock-refractory ventricular fibrillation (VF) or pulseless ventricular tachycardia (pVT).MethodsThe inclusion criteria were randomized controlled trials of participants older than eight years old who had atraumatic, out-of-hospital, shock-refractory VF/pVT in which at least one studied group received a medication. We conducted a database search on October 28, 2019, that included PubMed, Scopus, Web of Science, CINAHL Complete, and Cochrane CENTRAL. Citations of relevant meta-analyses were also searched. We performed frequentist network meta-analysis (NMA) to combine the comparisons. The outcomes were analyzed by using odds ratios (OR) and compared to placebo. The primary outcome was survival to hospital discharge. The secondary outcomes included the return of spontaneous circulation (ROSC), survival to hospital admission, and the neurological outcome at discharge. We ranked all outcomes using surface under the cumulative ranking score.ResultsWe included 18 studies with 6,582 participants. The NMA of 20 comparisons included 12 medications and placebo. Only norepinephrine showed a significant increase of ROSC (OR = 8.91, 95% confidence interval [CI], 1.88-42.29). Amiodarone significantly improved survival to hospital admission (OR = 1.53, 95% CI, 1.01-2.32). The ROSC and survival-to-hospital admission data were significantly heterogeneous with the I2 of 55.1% and 59.1%, respectively. This NMA satisfied the assumption of transitivity.ConclusionNo medication was associated with improved survival to hospital discharge from out-of-hospital, shock-refractory cardiac arrest. For the secondary outcomes, norepinephrine was associated with improved ROSC and amiodarone was associated with an increased likelihood of survival to hospital admission in the NMA.

Project description:BackgroundWe performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods.MethodsWe performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome.ResultsA total of 1,207 patients were included in the study. Among them, 631, 139, and 153 were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI], 0.9352-0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612-0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860-1.0000); sensitivity, 0.9594 (95% CI, 0.9245-0.9943); specificity, 0.9714 (95% CI, 0.9162-1.0000); PPV, 0.9916 (95% CI, 0.9752-1.0000); NPV, 0.8718 (95% CI, 0.7669-0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825-0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845-0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087-0.9867); sensitivity, 0.9595 (95% CI, 0.9145-1.0000); specificity, 0.6500 (95% CI, 0.5022-0.7978); PPV, 0.8353 (95% CI, 0.7564-0.9142); NPV, 0.8966 (95% CI, 0.7857-1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets.ConclusionWe established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

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Effect of Time to Treatment With Antiarrhythmic Drugs on Return of Spontaneous Circulation in Shock-Refractory Out-of-Hospital Cardiac Arrest.

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