Discovery of plasma proteins associated with ventricular fibrillation during first ST-elevation myocardial infarction via proteomics

AIMS

The underlying biological mechanisms of ventricular fibrillation (VF) during acute myocardial infarction are largely unknown. To our knowledge, this is the first proteomic study for this trait, with the aim to identify and characterize proteins that are associated with VF during first ST-elevation myocardial infarction (STEMI).

METHODS AND RESULTS

We included 230 participants from a Danish ongoing case-control study on patients with first STEMI with VF (case, n = 110) and without VF (control, n = 120) before guided catheter insertion for primary percutaneous coronary intervention. The plasma proteome was investigated using mass spectrometry-based proteomics on plasma samples collected within 24 h of symptom onset, and one patient was excluded in quality control. In 229 STEMI patients {72% men, median age 62 years [interquartile range (IQR): 54-70]}, a median of 257 proteins (IQR: 244-281) were quantified per patient. A total of 26 proteins were associated with VF; these proteins were involved in several biological processes including blood coagulation, haemostasis, and immunity. After correcting for multiple testing, two up-regulated proteins remained significantly associated with VF, actin beta-like 2 [ACTBL2, fold change (FC) 2.25, P < 0.001, q = 0.023], and coagulation factor XIII-A (F13A1, FC 1.48, P < 0.001, q = 0.023). None of the proteins were correlated with anterior infarct location.

CONCLUSION

Ventricular fibrillation due to first STEMI was significantly associated with two up-regulated proteins (ACTBL2 and F13A1), suggesting that they may represent novel underlying molecular VF mechanisms. Further research is needed to determine whether these proteins are predictive biomarkers or acute phase response proteins to VF during acute ischaemia.

ECG waveform dataset for predicting defibrillation outcome in out-of-hospital cardiac arrested patients

The provided database of 260 ECG signals was collected from patients with out-of-hospital cardiac arrest while treated by the emergency medical services. Each ECG signal contains a 9 second waveform showing ventricular fibrillation, followed by 1 min of post-shock waveform. Patients’ ECGs are made available in multiple formats. All ECGs recorded during the prehospital treatment are provided in PFD files, after being anonymized, printed in paper, and scanned. For each ECG, the dataset also includes the whole digitized waveform (9 s pre- and 1 min post-shock each) and numerous features in temporal and frequency domain extracted from the 9 s episode immediately prior to the first defibrillation shock. Based on the shock outcome, each ECG file has been annotated by three expert cardiologists, - using majority decision -, as successful (56 cases), unsuccessful (195 cases), or indeterminable (9 cases). The code for preprocessing, for feature extraction, and for limiting the investigation to different temporal intervals before the shock is also provided. These data could be reused to design algorithms to predict shock outcome based on ventricular fibrillation analysis, with the goal to optimize the defibrillation strategy (immediate defibrillation versus cardiopulmonary resuscitation and/or drug administration) for enhancing resuscitation.

Predicting defibrillation success in out-of-hospital cardiac arrested patients: Moving beyond feature design

OBJECTIVE

Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned.

METHODS

A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components: convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class.

RESULTS

The obtained results show that the proposed model is highly accurate in predicting defibrillation outcome (Acc = 93.6 %). Since recommendations on classifiers suggest at least 50 % specificity and 95 % sensitivity as safe and useful predictors for defibrillation decision, the reported sensitivity of 98.8 % and specificity of 88.2 %, with the analysis speed of 3 ms/input signal, indicate that the proposed model possesses a good prospective to be implemented in automated external defibrillators.

CONCLUSIONS

The learned features demonstrate superiority over hand-crafted ones when performed on the same dataset. This approach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.

Significance of automated external defibrillator in identifying lethal ventricular arrhythmias

Automated electrical defibrillator (AED) is critical in saving children who develop unexpected cardiac arrest (CA), but its diagnostic capacity is not fully acknowledged. Retrospective cohort study of patients with aborted sudden cardiac death (SCD) was performed. Twenty-five patients (14 males) aged 1.3 to 17.5 years who presented with CA survived with prompt cardiopulmonary resuscitation. Eighteen patients had no prior cardiac diagnosis. Cardiac arrest occurred in 10 patients with more than moderate exercise, in 7 with light exercise, and in 8 at rest (including one during sleep). Twenty-two patients were resuscitated with AED, all of which were recognized as a shockable cardiac rhythm. Thorough investigations revealed 6 ion channelopathies (4 catecholaminergic polymorphic ventricular tachycardia, one long QT syndrome, and one Brugada syndrome), 5 congenital heart disease (including 2 with coronary artery obstruction), 6 cardiomyopathies, 2 myocarditis, and 2 miscellaneous. Four patients had no identifiable heart disease. In 5 patients, the downloaded AED-recorded rhythm strip delineated the underlying arrhythmias and their responses to electrical shocks. Four patients who presented with generalized seizure at rest were initially managed for seizure disorder until AED recording identified lethal ventricular arrhythmias.Conclusions: AED reliably identifies the underlying lethal ventricular arrhythmias in addition to aborting SCD. What is Known: • Although infrequent in children, sudden cardiac death (SCD) is often an unexpected and tragic event. The etiology is diverse and sometimes remains unknown despite extensive investigations. • Automated external defibrillator (AED) is both therapeutic in aborting SCD and diagnostic in identifying the underlying lethal ventricular arrhythmias. However, the diagnostic aspect of AED is under-acknowledged by most medical providers. What is New: • Four of 25 patients (16%) were initially managed for possible seizure disorders until AED recording identified lethal ventricular arrhythmia. • The AED recording of the lethal arrhythmia during cardiopulmonary resuscitation (CPR) should always be obtained as it plays a crucial role in the decision-making process before ICD implantation. All medical providers should become familiar with downloading cardiac rhythm strips from AED when requested.

Combination of a leadless pacemaker and subcutaneous defibrillator with nine effective shock treatments during follow-up of 18 months

We present a case of combination of a leadless pacemaker (Micra) and a subcutaneous implantable cardioverter-defibrillator (S-ICD). The patient had a total of nine adequate shock treatments of ventricular fibrillation during 18 months of follow-up after the implantation. The shock treatments did not lead to any alteration in the Micra. All three sensing vectors of the S-ICD worked well. After 18 months, the functioning of both Micra and S-ICD continues to be uneventful. This case demonstrates that S-ICD combined with Micra may be a safe and feasible approach to provide pacing and ICD treatment without intracardiac leads.

A method to differentiate between ventricular fibrillation and asystole during chest compressions using artifact-corrupted ECG alone

In recent years, numerous adaptive filtering techniques have been developed to suppress the chest compression (CC) artifact for reliable analysis of the electrocardiogram (ECG) rhythm without CC interruption. Unfortunately, the result of rhythm diagnosis during CCs is still unsatisfactory in many studies. The misclassification between corrupted asystole (ASY) and corrupted ventricular fibrillation (VF) is generally regarded as one of the major reasons for the poor performance of reported methods. In order to improve the diagnosis of VF/ASY corrupted by CCs, a novel method combining a least mean-square (LMS) filter and an amplitude spectrum area (AMSA) analysis was developed based only on the analysis of the surface of the corrupted ECG episode. This method was tested on 253 VF and 160 ASY ECG samples from subjects who experienced cardiac arrest using a porcine model and was compared with six other algorithms. The validation results indicated that this method, which yielded a satisfactory result with a sensitivity of 93.3%, a specificity of 96.3% and an accuracy of 94.8%, is superior to the other reported techniques. After improvement using the human ECG records in real cardiopulmonary resuscitation (CPR) scenarios, the algorithm is promising for corrupted VF/ASY detection with no hardware alterations in clinical practice.

A new method without reference channels used for ventricular fibrillation detection during cardiopulmonary resuscitation

Ventricular fibrillation (VF) is observed as the initial rhythm in the majority of patients suffering from sudden cardiac arrest. It is vitally important to accurately recognize the initial VF rhythm and then implement electrical defibrillation. However, artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) make the VF detection algorithms utilized by current automated external defibrillators (AEDs) unreliable. CPR must be traditionally interrupted for a reliable diagnosis. However, interruptions in chest compression have a deleterious effect on the success of defibrillation. The elimination of the CPR artifacts would enable compressions to continue during AED VF detection and thereby increase the likelihood of resuscitation success. We have estimated a model of this artifact by adaptively incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) and least mean squares (LMS) and then removing the artifact from the corrupted ECGs. The simulation experiment indicated that the CPR artifact could be accurately modeled without any reference channels. We constructed a BP neural network to evaluate the results. A total of 372 VF and 645 normal sinus rhythm (SR) ECG samples were included in the analysis, and 24 CPR artifact signals were used to construct corrupted ECGs. The results indicated that at different SNR levels ranging from 0 to -12 dB, the sensitivity and specificity were always above 95 and 80 %, respectively.