[Adrenaline in patients with out-of-hospital cardiac arrest : PARAMEDIC2 trial]

Ethanolic Extracts of Cupressaceae Species Conifers Provide Rapid Protection against Barium Chloride-Induced Cardiac Arrhythmia

Sudden cardiac death (SCD) is responsible for a high percentage of cardiovascular fatalities, with ventricular arrhythmias being the most common cause. Despite numerous clinically available antiarrhythmic drugs (AADs), AADs retain some undesirable arrhythmic effects, and their inappropriate use can lead to severe adverse reactions. The exploration of new therapeutic options against arrhythmias with fewer unreceptive effects is of utmost importance. The ethanolic extracts of seven Cupressaceae species, namely, Chamaecyparis obtusa, Juniperus chinensis (L.) Ant., Sabina chinensis (L.) Ant. cv. Kaizuca, Platycladus orientalis (L.) Franco, Juniperus sabina L., Fokienia hodginsii, and Juniperus chinensis 'Pyramidalis' were investigated for their pharmacological effects on barium chloride (BaCl2)-induced arrhythmia using normal II lead electrocardiogram (ECG) measurements in a mouse model. According to the ECG profiles, pretreatment with C. obtusa, P. orientalis, and J. sabina extracts provoked dose-dependent protection against BaCl2-induced arrhythmia, while pretreatment with the other four species and amiodarone did not exert cardioprotective effects. The treatment effects were confirmed using a rat model. The therapeutic effects of C. obtusa, P. orientalis, and J. sabina extracts on the M2 and M3 receptors but not the M1 receptor were mediated by the inhibition of the M2 receptor blocker (methoctramine tetrahydrochloride), M3 antagonist (4-DAMP), or M1 receptor blocker (pirenzepine dihydrochloride). This first-line evidence illustrates that certain Cupressaceae species possess active antiarrhythmic components. The first line of key findings revealed that active components of certain Cupressaceae species have cardioprotective effects, suggesting that these innovative phytochemicals have promising potential for preventing the occurrence of cardiac arrhythmia and reducing sudden cardiac death.

A Machine Learning Prediction Model for Non-cardiogenic Out-of-hospital Cardiac Arrest with Initial Non-shockable Rhythm

Objectives

The purpose of this study was to develop and validate a machine learning prediction model for the prognosis of non-cardiogenic out-of-hospital cardiac arrest (OHCA) with an initial non-shockable rhythm.

Design

Data were obtained from a nationwide OHCA registry in Japan. Overall, 222,056 patients with OHCA and an initial non-shockable rhythm were identified from the registry in 2016 and 2017. Patients aged <18 years and OHCA caused by cardiogenic origin, cancer, and external factors were excluded. Finally, 58,854 participants were included.

Methods

Patients were classified into the training dataset (n=29,304, data from 2016) and the test dataset (n=29,550, data from 2017). The training dataset was used to train and develop the machine learning model, and the test dataset was used for internal validation. We selected XGBoost as the machine learning classifier. The primary outcome was the poor prognosis defined as cerebral performance category of 3-5 at 1 month. Eleven prehospital variables were selected as outcome predictors.

Results

In validation, the machine learning model predicted the primary outcome with an accuracy of 90.8% [95% confidence interval (CI): 90.5-91.2], a sensitivity of 91.4% [CI: 90.7-91.4], a specificity of 74.1% [CI: 69.2-78.6], and an area under the receiver operating characteristic value of 0.89 [0.87-0.92]. The important features for model development were the prehospital return of spontaneous circulation, prehospital adrenaline administration, and initial electrical rhythm.

Conclusions

We developed a favorable machine learning model to predict the prognosis of non-cardiogenic OHCA with an initial non-shockable rhythm in the early stage of resuscitation.

Poria cum Radix Pini Rescues Barium Chloride-Induced Arrhythmia by Regulating the cGMP-PKG Signalling Pathway Involving ADORA1 in Zebrafish

The traditional Chinese medicine Poria cum Radix Pini (PRP) is a fungal medicinal material that has been proven to play an important role in the treatment of arrhythmia. However, the mechanism of its effect on arrhythmia is still unclear. In this study, network pharmacology and metabolomics correlation analysis methods were used to determine the key targets, metabolites and potential pathways involved in the effects of PRP on arrhythmia. The results showed that PRP can significantly improve cardiac congestion, shorten the SV-BA interval and reduce the apoptosis of myocardial cells induced by barium chloride in zebrafish. By upregulating the expression of the ADORA1 protein and the levels of adenosine and cGMP metabolites in the cGMP-PKG signalling pathway, PRP can participate in ameliorating arrhythmia. Therefore, we believe that PRP shows great potential for the treatment of arrhythmia.