Materiały źródłowe

• #1 Ventricular Fibrillation (V-Fib): Symptoms & Treatment

  https://my.clevelandclinic.org/health/diseases/21878-ventricular-fibrillation

  Your outlook with ventricular fibrillation depends on how quickly someone helps you. With quick treatment, up to 50% of people can survive. Without immediate treatment, a ventricular fibrillation rhythm is fatal. For each minute you wait for defibrillation, your chance of survival drops by 7% to 10%. […] It’s rare for people to make a complete recovery from v-fib. Contact your provider for help managing lingering issues after ventricular fibrillation. […] Ventricular fibrillation is one of the shockable arrhythmias, meaning an AED can help return your hearts rhythm to a normal one. When someone uses an AED in the first three minutes after a person collapses because of v-fib, the survival rate of ventricular fibrillation can be as high as 95%.

• #2 Ventricular fibrillation Information | Mount Sinai – New York

  https://www.mountsinai.org/health-library/diseases-conditions/ventricular-fibrillation

  Ventricular fibrillation (VF) is a severely abnormal heart rhythm (arrhythmia) that is life threatening. […] VF will lead to death within a few minutes unless it is treated quickly and properly. Even then, long-term survival for people who live through a VF attack outside of the hospital is low. […] People who have survived VF may be in a coma or have long-term brain or other organ damage.

• #3 A logistic regression model to predict the occurrence of ventricular fibrillation in out-of-hospital cardiac arrest | Heart

  https://heart.bmj.com/content/95/Suppl_1/6

  The presence of ventricular fibrillation following OHCA is generally assumed to follow a linear decline with time such that survival declines by 10% per minute delay from collapse to defibrillation. […] Logistic regression analysis suggested that reduced CRI (p=0.002), patient age under 55 years (p=0.016), location of arrest not at home (p=0.026) and initiation of CPR by bystanders (p=0.041) were significantly associated with the rhythm of VF on EMS arrival. […] The simplified model suggests that there is an upper limit for the incidence of VF in OHCA of 64.5% at the moment of collapse. Conversely, without an increase in the level of bystander CPR the percentage probability of being in VF following a witnessed OHCA in Northern Ireland is 35.6% at the ambulance service 8-minute response target.

• #4 A method to predict ventricular fibrillation shock outcome during chest compressions – PubMed

  https://pubmed.ncbi.nlm.nih.gov/33278632/

  Out-of-hospital ventricular fibrillation (VF) cardiac arrest is a leading cause of death. […] A novel algorithm predicted defibrillation success and functional survival during ongoing CPR following VF arrest, providing a potential proof-of-concept towards real-time guidance of resuscitation therapy. […] AUC (95% Confidence Interval) for predicting defibrillation success was 0.74 (0.71-0.77) during CPR and 0.77 (0.74-0.79) without CPR. AUC for predicting functional survival was 0.75 (0.72-0.78) during CPR and 0.76 (0.74-0.79) without CPR.

• #5 Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques

  https://www.mdpi.com/2076-3417/14/23/11167

  This review offers a detailed examination of modern ECG signal processing techniques employed in the prediction and detection of ventricular fibrillation (VF). […] Early and accurate detection of VF is essential to increase the patient’s chances of survival. Studies show that for every minute of delay in defibrillation, the chances of survival decrease by about 10%. […] Recent advances in ECG signal processing, fueled by machine learning and advanced mathematical transformations, have led to significant improvements in the detection of ventricular fibrillation. These techniques offer high accuracy and efficiency, with the potential to transform clinical practice and save lives through rapid and accurate interventions. […] Despite these advances, there are still many challenges in the field of ventricular fibrillation detection. The variability of ECG signals between different patients, the presence of noise and artifacts in the recorded signals, and the need to develop robust models that work efficiently in real time are just some of the challenges that require innovative solutions.

• #6 Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques

  https://www.mdpi.com/2076-3417/14/23/11167

  The identification and development of ECG signal processing methods that are both accurate and computationally efficient is essential for practical implementation in wearable monitoring devices and telemedicine systems. […] This paper aims to provide a comprehensive review of modern ECG signal processing methods used for ventricular fibrillation detection. […] The study [8] showed that inter-patient variability can affect the performance of VF detection algorithms. […] The practical implementation of VF detection algorithms in continuous monitoring and telemedicine systems is another challenge. […] The paper [18] focused on the automatic detection of arrhythmias using different intervals of tachycardia ECG segments with a convolutional neural network. The novelty was the application of this machine learning method for the specific identification of arrhythmias. […] In conclusion, although deep neural networks show clear advantages in the detection and prediction of ventricular fibrillation, there are still significant challenges that need to be addressed.

• #7 Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence

  https://www.jstage.jst.go.jp/article/circj/advpub/0/advpub_CJ-22-0496/_html/-char/en

  Brugada syndrome is a potential cause of sudden cardiac death (SCD) and is characterized by a distinct ECG, but not all patients with A Brugada ECG develop SCD. In this study we sought to examine if an artificial intelligence (AI) model can predict a previous or future ventricular fibrillation (VF) episode from a Brugada ECG. […] This proof-of-concept study showed that an AI-enabled algorithm can predict the presence of VF with a substantial performance. It implies that the AI model may detect a subtle ECG change that is undetectable by humans. […] The present machine learning model used only ECG data and had an AUROC of 0.80, which may be comparable or better than conventional prediction systems. […] The objective of this grouping task was to predict the future development of fatal arrhythmias, which means that false negatives are directly associated with patients survival. It is important to not misinterpret positive cases as negative.

• #8 Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence

  https://www.jstage.jst.go.jp/article/circj/advpub/0/advpub_CJ-22-0496/_html/-char/en

  The performance of this model is not yet able to be applied in clinical practice. However, the prognostic evaluation ability was equal to or better than that of existing scoring systems based on clinical background. […] An AI model that predicts the previous or future onset of fatal arrhythmias from the 12-lead ECGs of patients with Brugada ECGs showed good predictive ability. It is possible that it can detect abnormal changes that clinicians cannot detect.

• #9 Prediction of fatal or near-fatal cardiac arrhythmia events in patients with depressed left ventricular function after an acute myocardial infarction – PubMed

  https://pubmed.ncbi.nlm.nih.gov/19155249/

  A total of 5869 consecutive patients were screened in 10 European centres, and 312 patients (age 65 +/- 11 years) with a mean LVEF of 31 +/- 6% were included in the study. […] Fatal or near-fatal arrhythmias can be predicted by many risk stratification methods, especially by heart rate variability, in patients with reduced LVEF after AMI.

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