Criteria for ECG detection of acute myocardial ischemia: Sensitivity versus specificity

Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram

OBJECTIVES

Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs.

METHODS

In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs.

RESULTS

Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs.

CONCLUSIONS

The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).

Immune-related biomarkers in myocardial infarction; diagnostic/prognostic value and therapeutic potential

The incidence of myocardial infarction (MI) is increasing worldwide on an annual basis. The incorporation of circulating biomarkers, along with electrocardiography, echocardiography, coronary angiograms, and other diagnostic techniques, is essential in the evaluation, prediction, and therapeutic efficacy assessment of patients afflicted with MI. Biomarker evaluation has been employed in the diagnosis of MI for over five decades. Further biomarker research can be carried out as newer biomarkers have been discovered in pathways such as inflammatory response, neurohormonal stimulation, or myocardial stress that initiate significantly earlier than myocyte necrosis and the diagnostic establishment of cardiac troponins. The assessment of biomarkers for MI is on the brink of a significant transformation due to advancements in comprehending the intricate pathophysiology of the condition. This has led to a pursuit of innovative biomarkers that could potentially overcome the limitations of current biomarkers. For individuals with a high-risk profile, this may facilitate tailoring of appropriate treatment. This review places emphasis on a diverse array of biomarkers that have the potential to offer diagnostic and prognostic information, as well as the latest clinical and preclinical evidence that is driving theoretical advancements in cardiovascular immunotherapy.

Ultra-High-Resolution Electrocardiography Enables Earlier Detection of Transmural and Subendocardial Myocardial Ischemia Compared to Conventional Electrocardiography

The sensitivity of exercise ECG is marginally sufficient for the detection of mild reduction of coronary blood flow in patients with early coronary atherosclerosis. Here, we describe the application of a new technique of ECG registration/analysis-ultra-high-resolution ECG (UHR ECG)-for early detection of myocardial ischemia (MIS). The utility of UHR ECG vs. conventional ECG (C ECG) was tested in anesthetized rats and pigs. Transmural MIS was induced in rats by the ligation of the left coronary artery (CA). In pigs, subendocardial ischemia of a variable extent was produced by stepwise inflation of a balloon within the right CA, causing a 25-100% reduction of its lumen. In rats, a reduction in power spectral density (PSD) in the high-frequency (HF) channel of UHR ECG was registered at 60 s after ischemia (power 0.81 ± 0.14 vs. 1.25 ± 0.12 mW at baseline, p < 0.01). This was not accompanied by any ST segment elevation on C ECG. In pigs, PSD in the HF channel of UHR ECG was significantly decreased at a 25% reduction of CA lumen, while the ST segment on C ECG remained unchanged. In conclusion, UHR ECG enabled earlier detection of transmural MIS compared to C ECG. PSD in the HF channel of UHR ECG demonstrated greater sensitivity in the settings of subendocardial ischemia.

Diagnostic value of end-tidal carbon dioxide in the differential diagnosis of unstable angina and non-cardiac chest pain

OBJECTIVE

This study aims to investigate the diagnostic value of End-tidal carbon dioxide (ETCO₂) measured non-invasively at the bedside in order to distinguish between unstable angina pectoris (UAP) and non-cardiac chest pain among patients who present to the emergency department with chest pain without a history of cardiac pathology.

MATERIAL AND METHODS

This clinical study is a prospective case-control study among patients presenting to the emergency department of a tertiary hospital with chest pain. After evaluating the inclusion and exclusion criteria, the patients were divided into two groups: 62 patients with UAP and 62 patients with non-cardiac chest pain. Receiver Operating Characteristic (ROC) analysis was used to determine the cut-off in diagnostic value measurements. For UAP prediction, the odds ratio of ETCO₂ (including 95% confidence intervals) was calculated using univariate with binary logistic regression analysis.

RESULTS

ETCO₂ had an excellent diagnostic power in detecting UAP, with 35 cut-offs determined (AUC: 0.84, 95% Cl: 0.76-0.90, p < 0.001). When ETCO₂, which affects both non-cardiac chest pain and UAP, is evaluated, an ETCO2 of <35 is statistically significant and 9.74 times more common among UAP patients than patients with non-cardiac chest pain.

CONCLUSION

ETCO₂, a non-invasive parameter that can be measured immediately at the bedside, may be proposed as a potential biomarker for differentiating patients with UAP from those with non-cardiac chest pain.

Development and validation of a dynamic deep learning algorithm using electrocardiogram to predict dyskalaemias in patients with multiple visits

Aims

Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits.

Methods and results

We retrospectively collected 168 450 ECGs with corresponding serum potassium (K⁺) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K⁺ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720-0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888-0.915/0.908) in patients with multiple visits.

Conclusion

Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.

Performance and limitations of automated ECG interpretation statements in patients with suspected acute coronary syndrome

BACKGROUND

The 12‑lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of "Acute MI" or "Acute Ischemia" crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS).

METHODS

An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<<. Two independent reviewers retrospectively adjudicated the study outcomes and disagreements were resolved by a third reviewer.

RESULTS

Our study included 2400 patients (age 59 ± 16, 47% females, 41% Black), with 190 (8%) patients with documented automated diagnostic statements of acute MI or acute ischemia. The sensitivity/specificity of the automated algorithm for detecting confirmed STEMI (n = 143, 6%); presence of actionable coronary culprit lesions (n = 258, 11%), myocardial necrosis (n = 291, 12%), or any ACS (n = 378, 16%) were 62.9%/95.6%; 37.2%/95.6%; 38.5%/96.4%; and 30.7%/96.3%, respectively.

CONCLUSION

Although being very specific, automated interpretation statements of acute MI/acute ischemia on prehospital ECGs are not satisfactorily sensitive to exclude symptomatic coronary disease. Patients without these automated interpretation statements should be considered further for significant underlying coronary disease based on the clinical context.

TRIAL REGISTRATION

ClinicalTrials.gov # NCT04237688.

A multiview feature fusion model for heartbeat classification

Objective.An electrocardiogram (ECG) is one of the most common means to diagnose arrhythmia according to different waveforms clinically. Although there are advanced classification methods such as deep learning, the single view feature cannot meet the demand of classification accuracy for new individuals. To this end, a classification model based on multiview fusion was proposed.Approach.First, handcrafted view features were extracted from heartbeats and then deep view features were obtained from the deep learning model. The features of two different perspectives were fused in the fully connected layer, and the random forest classifier was used instead of the Softmax classifier for classification. Notably, Bayesian optimization was utilized in the hyper-parameter tuning of the classifier. The proposed method employed the MIT-BIH database to classify five classes: normal heartbeat (N), left bundle branch block heartbeat (LB), right bundle branch block heartbeat (RB), atrial premature contraction (APC) and premature ventricular contraction (PVC).Main results.The experimental results achieved a higher average accuracy of 98.93%, average precision of 96.92%, average sensitivity of 96.46%, and average specificity of 99.33% in five types of heartbeat classification for inter-patient.Significance.The proposed framework improves the performance of ECG detection for new individuals. And it provides an feasible algorithmic model for single-lead wearable devices with multiview fusion.

Past, Present, and Future of Blood Biomarkers for the Diagnosis of Acute Myocardial Infarction-Promises and Challenges

Despite important advancements in acute myocardial infarction (AMI) management, it continues to represent a leading cause of mortality worldwide. Fast and reliable AMI diagnosis can significantly reduce mortality in this high-risk population. Diagnosis of AMI has relied on biomarker evaluation for more than 50 years. The upturn of high-sensitivity cardiac troponin testing provided extremely sensitive means to detect cardiac myocyte necrosis, but this increased sensitivity came at the cost of a decrease in diagnostic specificity. In addition, although cardiac troponins increase relatively early after the onset of AMI, they still leave a time gap between the onset of myocardial ischemia and our ability to detect it, thus precluding very early management of AMI. Newer biomarkers detected in processes such as inflammation, neurohormonal activation, or myocardial stress occur much earlier than myocyte necrosis and the diagnostic rise of cardiac troponins, allowing us to expand biomarker research in these areas. Increased understanding of the complex AMI pathophysiology has spurred the search of new biomarkers that could overcome these shortcomings, whereas multi-omic and multi-biomarker approaches promise to be game changers in AMI biomarker assessment. In this review, we discuss the evolution, current application, and emerging blood biomarkers for the diagnosis of AMI; we address their advantages and promises to improve patient care, as well as their challenges, limitations, and technical and diagnostic pitfalls. Questions that remain to be answered and hotspots for future research are also emphasized.

A telediagnosis assistance system for multiple-lead electrocardiography

The diffusion of telemedicine opens-up a new perspective for the development of technologies furthered by Biomedical Engineering. In particular, herein we deal with those related to telediagnosis through multiple-lead electrocardiographic signals. This study focuses on the proof-of-concept of an internet-based telemedicine system as a use case that attests to the feasibility for the development, within the university environment, of techniques for remote processing of biomedical signals for adjustable detection of myocardial ischemia episodes. At each signal lead, QRS complexes are detected and delimited with the J-point marking. The same procedure to detect the complex is used to identify the respective T wave, then the area over the ST segment is applied to detect ischemia-related elevations. The entire system is designed on web-based telemedicine services using multiuser, remote access technologies, and database. The measurements for sensitivity and precision had their respective averages calculated at 11.79 and 24.21% for the leads of lower noise. The evaluations regarding the aspects of user friendliness and the usefulness of the application, resulted in 88.57 and 89.28% of broad or total acceptance, respectively. They are robust enough to enable scalability and can be offered by cloud computing, besides enabling the development of new biomedical signal processing techniques within the concept of distance services, using a modular architecture with collaborative bias.

Peguero-Lo Presti criteria for the diagnosis of left ventricular hypertrophy: A systematic review and meta-analysis

Background

The Peguero-Lo Presti criteria are novel electrocardiographic (ECG) diagnostic criteria for the detection of left ventricular hypertrophy (LVH) and represent the sum of the amplitude of the deepest S wave in any lead with the S wave in lead V₄ (S_D+SV₄). The diagnostic efficacy of the Peguero-Lo Presti criteria in LVH is still debatable. We aimed to test the sensitivity and specificity of the Peguero-Lo Presti criteria and compared them with those of the Cornell voltage index to assess their overall performance in LVH diagnosis.

Methods

Electronic databases (e.g., Medline, Web of Knowledge, Embase, and the Cochrane Library) were searched from their inception until May 18, 2020. Trials written in English that investigated the Peguero-Lo Presti criteria for detecting LVH were included. Data were independently extracted and analyzed by two investigators.

Results

A total of 51 records were screened, and 6 trials comprising 13,564 patients were finally included. A bivariate analysis showed that the sensitivity of the Peguero-Lo Presti criteria (0.52, 95% confidence interval (CI) 0.46–0.58) was higher than that of the Cornell voltage index (0.29, 95% CI 0.23–0.36) and Sokolow-Lyon criteria (0.24, 95% CI 0.21–0.27); the diagnostic accuracy of the Peguero-Lo Presti criteria (0.69, 95% CI 0.65–0.73) was also higher than that of the Cornell voltage index (0.67, 95% CI 0.62–0.71) and Sokolow-Lyon criteria (0.28, 95% CI 0.25–0.32); and the specificity of the Peguero-Lo Presti criteria (0.85, 95% CI 0.79–0.90) was similar to that of the Cornell voltage index (0.92, 95% CI 0.89–0.95) and Sokolow-Lyon criteria (0.94, 95%CI 0.88–0.97). Two trials (including 12,748 patients) were discharged because they included partly healthy subjects and accounted for substantial heterogeneity. Pooled analysis of the remaining 4 trials (including 816 patients) showed that the sensitivity of the Peguero-Lo Presti criteria (0.56, 95% CI 0.51–0.61) was also higher than that of the Cornell voltage index (0.36, 95% CI 0.31–0.42) and Sokolow-Lyon criteria (0.24, 95% CI 0.18–0.31); the diagnostic accuracy of the Peguero-Lo Presti criteria (0.84, 95% CI 0.80–0.87) was also higher than that of the Cornell voltage index (0.54, 95% CI 0.50–0.58) and Sokolow-Lyon criteria (0.38, 95% CI 0.34–0.42); and the specificity of the Peguero-Lo Presti criteria (0.90, 95% CI 0.87–0.92) was similar to that of the Cornell voltage index (0.93, 95% CI 0.88–0.96) and Sokolow-Lyon criteria (0.97, 95% CI 0.90–0.99). Both the likelihood ratio and posttest probability of the Peguero-Lo Presti criteria and Cornell voltage index were moderate.

Conclusion

Based on this systematic review and meta-analysis, the Peguero-Lo Presti criteria-based ECG diagnostic method for LVH has high sensitivity, specificity and diagnostic accuracy and should be applied in clinical practice settings.

Current diagnostic ECG criteria for left ventricular hypertrophy: is it time to change paradigm in the analysis of data?

BACKGROUND

Twelve-lead ECG represents the most common diagnostic tool in clinical cardiology and allows an immediate screening of left ventricular hypertrophy (LVH), but current criteria might have poor clinical usefulness in everyday clinical practice due to lack of sensitivity.

METHODS

The current study aims to review and compare the clinical performance of known ECG criteria of LVH in a real-life setting; 2134 patients had ECG and echocardiographic exams performed during the same hospitalization. All traces were retrospectively analysed, and the amplitudes of the waves were manually measured. Transthoracic echocardiography was considered as the gold standard to assess LVH.

RESULTS

LVH had a prevalence of 58%. Considering the diagnostic performance of ECG criteria for LVH, the Cornell voltage carried the best area under the receiver operating characteristic curve (0.678), while RaVF (R wave in aVF lead) had the poorer result (0.440). The R5/R6 criterion had the best sensitivity (60%), but with the worst specificity (37.4%). The 'Q or S aVR' had the best specificity (99.9%) but lacks sensitivity (0.80%). The Peguero Lo Presti criterion had a sensitivity of 42.3% and a specificity of 75.8%. The Cornell voltage and the Cornell product had similar area under the receiver operating characteristic curve values which were found to be significantly greater compared with other criteria.

CONCLUSION

Current ECG criteria of LVH have low sensitivity despite an acceptable specificity. Among these, Cornell voltage and Cornell product criteria were equally found to have a more accurate diagnostic performance compared with other criteria. To overcome the intrinsic limitations of the current ECG LVH criteria, a new paradigm in the analysis of electrocardiographic data might be necessary.

Modelling of the electrocardiographic signal during an angioplasty procedure in the right coronary artery

Dynamical models are useful tools to generate sets of varied morphological signals by synthesizing human electrocardiograms (ECGs). These signals are used for testing and improving algorithms of ECG delineation, patient monitoring and heart disease detection. This work presents a procedure based on the ECGSYN model to synthesize ECG morphological changes induced by a percutaneous transluminal coronary angioplasty (PTCA) procedure in the right coronary artery. We provide a set of parameters to be used in ECGSYN and generate heartbeats with altered ST-T complexes. These characteristic model parameters were obtained through a non-linear fitting algorithm applied to every available heartbeat. To extend these parameters, normal distributions were generated with their means and standard deviations obtained from the STAFF III database. Parameters were presented for P, QRS and T-waves at leads II, III and aVF. The synthesis procedure shows an average correlation and positive predictive value of 92.2% and 88.2%, respectively. In conclusion, we provide a technique capable of synthesizing electrocardiographic ischemic morphology with physiological plausibility. Then, the generation of data sets for algorithm testing can benefit from this system of ECG signal synthesis.

Biomarkers in acute myocardial infarction: current perspectives

PURPOSE

Acute myocardial infarction (AMI) is the most common cause of death in the world. Comprehensive risk assessment of patients presenting with chest pain and eliminating undesirable results should decrease morbidity and mortality rates, increase the quality of life of patients, and decrease health expenditure in many countries. In this study, the advantages and disadvantages of the enzymatic and nonenzymatic biomarkers used in the diagnosis of patients with AMI are given in historical sequence, and some candidate biomarkers - hFABP, GPBB, S100, PAPP-A, RP, TNF, IL6, IL18, CD40 ligand, MPO, MMP9, cell-adhesion molecules, oxidized LDL, glutathione, homocysteine, fibrinogen, and D-dimer procalcitonin - with a possible role in the diagnosis of AMI are discussed.

METHODS

The present study was carried out using meta-analyses, reviews of clinical trials, evidence-based medicine, and guidelines indexed in PubMed and Web of Science.

RESULTS

These numerous AMI biomarkers guide clinical applications (diagnostic methods, risk stratification, and treatment). Today, however, TnI remains the gold standard for the diagnosis of AMI. Details in the text will be given of many biomarkers for the diagnosis of AMI.

CONCLUSION

We evaluated the advantages and disadvantages of routine enzymatic and nonenzymatic biomarkers and the literature evidence of other candidate biomarkers in the diagnosis of AMI, and discuss challenges and constraints that limit translational use from bench to bedside.

The Importance of the 15-lead Versus 12-lead ECG Recordings in the Diagnosis and Treatment of Right Ventricle and Left Ventricle Posterior and Lateral Wall Acute Myocardial Infarctions

Introduction

The 12-lead ECG at admission of patients suffering from acute myocardial infarction (AMI) is mandatory for accurate diagnosis and prompt therapeutic measures, mainly reperfusion. It has been shown that recording additional ECG leads may improve the diagnostic accuracy and therefore, the prognosis of selected cases.

Aim

The aim of the study was to assess the usefulness of the 15-lead ECG (12 classic plus 3 posterior leads) in the management of chest pain patients, especially when 12-lead ECG is not diagnostic of AMI.

Methods

Total amount of 186 consecutive patients (127 men, 59 women, mean age 69.7±13.8 years) were admitted with an acute coronary syndrome. The initial ECG recorded the 12 classic leads, and subsequently, the 3 additional posterior leads. Demographic and clinical data, including ECG alterations and selected treatment strategy, were also studied. The cumulative impact of the 15-lead ECG on the diagnosis and management of AMI were, overall, evaluated.

Results

The 12-lead ECG was diagnostic of ST-elevation AMI (STEMI) in 158 patients (Group A-84.5%) who were promptly reperfused. On the other hand, the interpretation of the posterior leads was required in 28 patients (Group B-15.1%) to establish the STEMI diagnosis warranting reperfusion therapy. Multivariate analysis illustrated that the 15-lead ECG was the only factor associated with achieving the STEMI diagnosis in non-conclusive 12-lead ECG cases (OR=2.43-p=0.04).

Conclusion

The use of the 15-lead ECG contributes to a faster and more accurate diagnosis of STEMI, particularly in the Emergency Department, facilitating the prompt reperfusion therapy.

Cardioprotective Effects of Puerarin-V on Isoproterenol-Induced Myocardial Infarction Mice Is Associated with Regulation of PPAR-Υ/NF-κB Pathway

Puerarin is a well-known traditional Chinese medicine which has been used for the treatment of cardiovascular diseases. Recently, a new advantageous crystal form of puerarin, puerarin-V, has been developed. However, the cardioprotective effects of puerarin-V on myocardial infarction (MI) heart failure are still unclear. In this research, we aim to evaluate the cardioprotective effects of puerarin-V on the isoproterenol (ISO)-induced MI mice and elucidate the underlying mechanisms. To induce MI in C57BL/6 mice, ISO was administered at 40 mg/kg subcutaneously every 12 h for three times in total. The mice were randomly divided into nine groups: (1) control; (2) ISO; (3) ISO + puerarin injection; (4⁻9) ISO + puerarin-V at different doses and timings. After treatment, cardiac function was evaluated by electrocardiogram (ECG), biochemical and histochemical analysis. In vitro inflammatory responses and apoptosis were evaluated in human coronary artery endothelial cells (HCAECs) challenged by lipopolysaccharide (LPS). LPS-induced PPAR-Υ/NF-κB and subsequently activation of cytokines were assessed by the western blot and real-time polymerase chain reaction (PCR). Administration of puerarin-V significantly inhibits the typical ST segment depression compared with that in MI mice. Further, puerarin-V treatment significantly improves ventricular wall infarction, decreases the incidence of mortality, and inhibits the levels of myocardial injury markers. Moreover, puerarin-V treatment reduces the inflammatory milieu in the heart of MI mice, thereby blocking the upregulation of proinflammatory cytokines (TNF-α, IL-1β and IL-6). The beneficial effects of puerarin-V might be associated with the normalization in gene expression of PPAR-Υ and PPAR-Υ/NF-κB /ΙκB-α/ΙΚΚα/β phosphorylation. In the in vitro experiment, treatment with puerarin-V (0.3, 1 and 3 μM) significantly reduces cell death and suppresses the inflammation cytokines expression. Likewise, puerarin-V exhibits similar mechanisms. The cardioprotective effects of puerarin-V treatment on MI mice in the pre + post-ISO group seem to be more prominent compared to those in the post-ISO group. Puerarin-V exerts cardioprotective effects against ISO-induced MI in mice, which may be related to the activation of PPAR-γ and the inhibition of NF-κB signaling in vivo and in vitro. Taken together, our research provides a new therapeutic option for the treatment of MI in clinic.