Advanced electrocardiography predicts computed tomography coronary artery calcium score

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This retrospective study examined whether an advanced ECG (A-ECG) score derived from a standard 12-lead ECG could predict the presence of coronary artery calcium (CAC) as measured by CT. Using 520 patients with CT-confirmed absence (n=265) or presence (n=255) of any CAC, the authors built a multivariable elastic net logistic regression model incorporating age, sex, and four ECG-derived measures, and assessed performance via nested resampling. The A-ECG score predicted CAC with an area under the ROC curve of 0.78, with sensitivity of 73% and specificity of 66%, and the study did not report an explicit external validation limitation in the abstract. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

BACKGROUND Coronary artery calcification (CAC) has been linked to an increased risk of cardiovascular events. Its detection in asymptomatic individuals is valuable for reclassifying cardiac risk and informing management strategies. We hypothesised that an advanced electrocardiography (A-ECG) score derived from the standard 12-lead ECG predicts CAC with good diagnostic accuracy compared to computed tomography (CT). METHODS This retrospective study included patients that had undergone a 12-lead ECG and CT CAC scoring demonstrating either the absence (n=265) or presence (n=255) of any CAC. Multivariable elastic net logistic regression was used to generate an A-ECG score validated by nested resampling. RESULTS An A-ECG score for detecting CAC comprised of age, sex, and four ECG measures encompassing the duration of the Q wave in lead I, vectorcardiographic measures derived from the 12-lead ECG related to the spatial direction of the QRS complex (two measures) and the magnitude of the ST segment (one measure). Nested resampling estimated performance for predicting the presence of any CAC with an area under the receiver operating characteristics curve [95% confidence interval] of 0.78 [0.77-0.79], sensitivity 73 [72-75]%, specificity 66 [65-68]%, positive predictive value 70 [68-71]%, negative predictive value 71 [69-72]%, positive likelihood ratio 2.3 [2.1-2.4], and inverse negative likelihood ratio 2.6 [2.4-2.7]. CONCLUSIONS The standard 12-lead ECG analysed by A-ECG analysis can predict the presence of CAC with good diagnostic performance. A-ECG may hold clinical utility as a low-cost and widely available initial screening test for the presence of CAC and cardiac risk prediction.
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Abstract

BACKGROUND Coronary artery calcification (CAC) has been linked to an increased risk of cardiovascular events. Its detection in asymptomatic individuals is valuable for reclassifying cardiac risk and informing management strategies. We hypothesised that an advanced electrocardiography (A-ECG) score derived from the standard 12-lead ECG predicts CAC with good diagnostic accuracy compared to computed tomography (CT).

Methods

This retrospective study included patients that had undergone a 12-lead ECG and CT CAC scoring demonstrating either the absence (n=265) or presence (n=255) of any CAC. Multivariable elastic net logistic regression was used to generate an A-ECG score validated by nested resampling.

Results

An A-ECG score for detecting CAC comprised of age, sex, and four ECG measures encompassing the duration of the Q wave in lead I, vectorcardiographic measures derived from the 12-lead ECG related to the spatial direction of the QRS complex (two measures) and the magnitude of the ST segment (one measure). Nested resampling estimated performance for predicting the presence of any CAC with an area under the receiver operating characteristics curve [95% confidence interval] of 0.78 [0.77-0.79], sensitivity 73 [72-75]%, specificity 66 [65-68]%, positive predictive value 70 [68-71]%, negative predictive value 71 [69-72]%, positive likelihood ratio 2.3 [2.1-2.4], and inverse negative likelihood ratio 2.6 [2.4-2.7].

Conclusions

The standard 12-lead ECG analysed by A-ECG analysis can predict the presence of CAC with good diagnostic performance. A-ECG may hold clinical utility as a low-cost and widely available initial screening test for the presence of CAC and cardiac risk prediction. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of Northern Sydney Local Health District Human Research Ethics Committee gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present work are contained in the manuscript

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