Accuracy of intracoronary ECG parameters for myocardial ischemia detection

The value and accuracy of intracoronary electrocardiogram in the diagnosis of myocardial ischemia in coronary heart disease

BACKGROUND

Although intracoronary electrocardiography (IC-ECG) offers direct electrophysiological insights into myocardial ischemia caused by insufficient coronary blood supply, compared to common diagnostic methods like electrocardiography (ECG), it lacks widespread adoption and robust clinical research.

OBJECTIVE

To analyze the value and accuracy of intracoronary electrocardiogram in myocardial ischemia diagnosis in coronary heart disease patients.

METHODS

Three hundred patients treated at our hospital were included in the study. Patients were categorized into non-ischemic group A (Fraction Flow Reserve [FFR] > 0.8) and ischemic group B (FFR < 0.75) based on FFR examination results. Both groups underwent IC-ECG examination. The ischemic group received percutaneous coronary intervention (PCI) treatment followed by another FFR examination, dividing them into non-ischemic subgroup B1 (FFR > 0.8) and ischemic subgroup B2 (FFR < 0.75). Both subgroups underwent IC-ECG examination. Receiver operating curves were constructed using FFR to assess the clinical utility of different IC-ECG parameters.

RESULTS

Group A patients showed a significant decrease in ST-segment shift at J-point, ST-segment integral, T-peak, T-wave integral, and T-peak to end-time, while the Corrected Q-T interval (QTc-time) was significantly higher in the B group (p< 0.05). The parameters, including ST-segment shift at J-point, ST-segment integral, T-wave integral, T-peak, T-peak to end-time, and QTc-time, were found to have clinical significance in predicting the occurrence of myocardial ischemia (p< 0.05).

CONCLUSION

Intracoronary electrocardiogram QT interval dispersion and Q-T peak (QTp) interval dispersion have a high diagnostic accuracy for myocardial ischemia in coronary heart disease.

Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance

Background

ST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.

Methods

Machine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.

Results

Over 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784-0.889] in the derivation cohort and 0.730 (95% CI: 0.293-1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.

Conclusion

The iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences.

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes

Background

There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.

Methods

We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].

Findings

In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.

Conclusion

Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

High salt intake damages myocardial viability and induces cardiac remodeling via chronic inflammation in the elderly

Background

The heart is an important target organ for the harmful effects of high dietary salt intake. However, the effects and associations of high salt intake on myocardial viability, cardiac function changes, and myocardial remodeling are unclear.

Methods

A total of 3,810 participants aged 60 years and older were eligible and enrolled from April 2008 to November 2010 and from August 2019 to November 2019 in the Shandong area of China. Salt intake was estimated using 24-h urine collection consecutively for 7 days. Myocardial strain rates, cardiac function and structure, and serum high-sensitivity C-reactive protein (hsCRP) levels were assessed. Participants were classified into low (n = 643), mild (n = 989), moderate (n = 1,245), and high (n = 933) groups, corresponding to < 6, 6–9, 9–12, and >12 g/day of salt intake, respectively, depending on the salt intake estimation.

Results

The global early diastolic strain rate (SRe) and late diastolic strain rate (SRa) in the high group were 1.58 ± 0.26, 1.38 ± 0.24. respectively, and significantly lower compared with the low, mild, and moderate groups (all P < 0.05). The global systolic strain rate (SRs) in the high group was −1.24 ± 0.24, and it was higher than those in the low, mild, and moderate groups (all P < 0.05). Salt intake was independently and positively correlated with global SRs, Tei index, and the parameters of left ventricular structure separately; negatively correlated with global SRe and SRa, left ventricular short axis shortening rate, left ventricular ejection fraction after adjusting for confounders (all P_adjusted < 0.001). Hayes process analyses demonstrated that the mediating effects of hsCRP on global SRe, SRa, and SRs; Tei index; and left ventricular remodeling index were −0.013 (95% CI: −0.015 to −0.010), −0.010 (−0.012 to −0.008), 0.008 (0.006–0.010), 0.005 (0.003–0.006), and 0.010 (0.009–0.012), respectively (all P_adjusted < 0.001).

Conclusion

Our data indicate that excess salt intake is independently associated with the impairment in myocardial viability and cardiac function, as well as myocardial remodeling. Chronic inflammation might play a mediating role in the association between high salt intake and cardiac function damage and myocardial remodeling.

P2X7R-NEK7-NLRP3 Inflammasome Activation: A Novel Therapeutic Pathway of Qishen Granule in the Treatment of Acute Myocardial Ischemia

Background

Acute myocardial ischemia (AMI) is a common heart disease with increasing morbidity and mortality year by year. Persistent and sterile inflammatory infiltration of myocardial tissue is an important factor triggering of acute myocardial ischemia secondary to acute myocardial infarction, and NLRP3 inflammasome activation is an important part of sterile inflammatory response after acute myocardial ischemia. Previous studies have shown that Qishen granule (QSG) can significantly inhibit the inflammatory injury of myocardial tissue caused by ischemia, but its effect and specific mechanism of inhibiting the activation of NLRP3 inflammasome have not been reported. This study was to investigate the specific mechanism of QSG inhibiting inflammation after AMI, and to validate the possible targets.

Methods

The myocardial ischemia model in mice was established by ligation of the left anterior descending coronary artery. Echocardiography was used to evaluate the cardiac function of the mice. Plasma CK-MB and cTnl were detected by ELISA to evaluate the degree of myocardial injury. The extent of myocardial tissue inflammation in mice was assessed by HE staining and immunohistochemistry of IL-18, IL-1β. The expressions of NLRP3, ASC, Caspase-1, and CD86 were detected by immunofluorescence; detection of key pathway proteins P2X7R, NEK7, NLRP3, ASC, Caspase-1, and effector proteins IL-18, IL-1β by Western blot. In vitro experiments, ATP+LPS was used to construct a RAW264.7 macrophage NLRP3 inflammasome activation model. Immunofluorescence and Western blot analysis were performed to detect the expression of NLRP3 pathway activator and effector proteins. Plasmid-transfected P2X7R overexpression and immunoprecipitation assays were used to evaluate the QSG-regulated NLRP3 inflammasome activation pathway.

Results

QSG rescued cardiac function and further reduced inflammatory effects in mice by inhibiting NLRP3 inflammasome activation. In vitro, QSG inhibited LPS combined with ATP-induced NLRP3 inflammasome activation in RAW264.7 macrophages by downregulating the expression of NLRP3 inflammasome key pathway proteins. In addition, inhibition or overexpression of P2X7R in RAW264.7 macrophages and immunoprecipitated protein interactions further confirmed that QSG reduces macrophages inflammasome activation via the P2X7R-NEK7-NLRP3 pathway.

Conclusion

P2X7R-NEK7-NLRP3 inflammasome activation is a novel therapeutic mechanism of QSG in the treatment of acute myocardial ischemia.

Algorithm for real-time analysis of intracoronary electrocardiogram

Introduction

Since its first implementation in 1985, intracoronary (ic) electrocardiogram (ECG) has shown ample evidence for its diagnostic value given the higher sensitivity for myocardial ischemia detection in comparison to surface ECG. However, a lack of online systems to quantitatively analyze icECG in real-time prevents its routine use. The present study aimed to develop and validate an autonomous icECG analyzing algorithm.

Materials and methods

This is a retrospective observational study in 100 patients with chronic coronary syndrome. From each patient, a non-ischemic as well as ischemic icECG at the end of a 1-min proximal coronary balloon occlusion was available. An ECG expert as well as the newly developed algorithm for autonomous icECG analysis measured the icECG ST-segment shift in mV for each icECG tracing.

Results

Intraclass correlation coefficient (ICC) demonstrated low variability between the two methods (ICC = 0.968). Using the time point of icECG recording as allocation reference for absent or present myocardial ischemia, ROC-analysis for ischemia detection by the manually determined icECG ST-segment shift showed an area under the curve (AUC) of 0.968 ± 0.021 (p &lt; 0.0001). AUC for the algorithm analysis was 0.967 ± 0.023 (p &lt; 0.0001; p = 0.925 for the difference between the ROC curve AUCs). Time to complete analysis was below 1,000 ms for the autonomous icECG analysis and above 5 min for manual analysis.

Conclusion

A newly developed autonomous icECG analysing algorithm detects myocardial ischemia with equal accuracy as manual ST-segment shift assessment. The algorithm provides the technical fundament for an analysing system to quantitatively obtain icECG in real-time.

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.

Prognostic and diagnostic accuracy of intracoronary electrocardiogram recorded during percutaneous coronary intervention: a meta-analysis

OBJECTIVE

Intracoronary ECG (IC-ECG) recording has been shown to be sensitive and reliable for detecting myocardial viability and local myocardial ischaemia in some studies. But IC-ECG is neither widely used during percutaneous coronary intervention (PCI) nor recommended in guidelines. This up-to-date meta-analysis of published studies was conducted to evaluate the prognostic and diagnostic accuracy of IC-ECG recorded during PCI.

METHODS

Relevant studies were identified by searches of MEDLINE until 19 June 2021. Observational and diagnostic studies which reported the prognostic or diagnostic accuracy of IC-ECG were included. Data were extracted independently by two authors. Summary estimates of clinical outcomes were obtained using a random effects model. Summary diagnostic accuracy was obtained by using a Bayesian bivariate random effects model.

RESULTS

Of the 12 included studies, 7 studies reported the clinical outcomes (821 patients) and 6 studies reported the diagnostic accuracy (485 patients) of IC-ECG. The pooled ORs with 95% CIs of ST-segment elevation recorded by IC-ECG were 4.65 (1.69 to 12.77), 5.08 (1.10 to 23.44), 4.53 (0.79 to 25.90) and 1.83 (0.93 to 3.62) for major adverse cardiac events, myocardial infarction, cardiac death and revascularisation, respectively. The weighted mean difference were 6.49 (95% CIs 3.84 to 9.14) for ejection fraction when ST-segment resolution was recorded, and 0.86 (95% CIs -8.55 to 10.26) when ST-segment elevation was recorded. The pooled sensitivity and specificity of ST-segment elevation were 0.78 (95% credibility intervals 0.64 to 0.89) and 0.87 (95% credibility intervals 0.75 to 0.94), respectively.

CONCLUSIONS

These findings provide quantitative data supporting that IC-ECG had promising diagnostic ability for local myocardial injury, and could predict clinical outcomes.

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks

INTRODUCTION

The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG).

METHOD

This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia.

RESULTS

Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection.

CONCLUSIONS

When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.