The value of the transthoracic echocardiography score for screening significant coronary artery disease in patients with hypertrophic cardiomyopathy

Introduction

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant inherited disease characterized by myocardial hypertrophy, primarily caused by mutations in genes encoding sarcomere-associated proteins. The clinical phenotype, individual symptoms and prognosis of HCM exhibit significant heterogeneity (1). The annual mortality rate for patients with HCM at tertiary care centers is approximately 2–4% (2). Notably, most deaths in these patients are not directly related to HCM itself. Instead, noncardiac conditions or coexisting cardiac issues pose the greatest challenge to survival, particularly in elderly patients (3). Coronary artery disease (CAD) is a relatively common comorbid cardiac condition in patients with HCM. The risk of developing CAD increases with age, making it an important factor that influences the prognosis of older patients with HCM. The incidence of CAD in these patients varies significantly, ranging from about 11% to 53%, depending on the specific population studied and the diagnostic criteria used (4-7). Moreover, HCM patients with severe CAD are exposed to a higher risk of death compared to those with mild or moderate CAD. Therefore, it is crucial to identify and manage CAD in HCM patients to reduce the likelihood of adverse outcomes.

Despite guideline recommendations for coronary angiography (CAG) or computed tomographic angiography (CTA) in HCM patients with angina or myocardial ischemia, HCM itself mimics these symptoms. The high prevalence of normal coronary arteries in symptomatic HCM patients necessitates noninvasive screening tools to accurately identify concomitant CAD, thereby avoiding unnecessary invasive procedures and optimizing resource utilization. Echocardiography serves as the primary imaging modality for HCM assessment, delivering precise quantification of ventricular wall thickness, comprehensive evaluation of cardiac function, and detailed characterization of hemodynamic parameters including left ventricular outflow tract (LVOT) obstruction and mitral regurgitation (8). This study established a scoring model for transthoracic echocardiography score (TTES) and integrated clinical parameters to evaluate the probability of significant CAD in patients with HCM. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-698/rc).

Methods

Study populations and definitions

We conducted a single-center retrospective cohort study consisting of 392 HCM patients who were diagnosed with CAD by invasive CAG or CTA at Renmin Hospital of Wuhan University from January 2019 to June 2024. The diagnosis of HCM was based on a wall thickness of ≥15 mm in one or more left ventricular myocardial segments as measured by transthoracic echocardiography (TTE) or cardiac magnetic resonance (CMR) imaging that could not be explained by abnormal loading conditions or systemic, metabolic disease. LVOT obstruction was defined as a peak LVOT gradient of ≥30 mmHg in the resting state (8). Significant CAD was defined as ≥50% luminal stenosis via invasive CAG or CTA in patients (9). The definition of severe stenosis CAD was at least 75% luminal stenosis in one or more major epicardial coronary artery branches. The definition of mild CAD was a luminal stenosis of less than 50% in one or more major epicardial coronary artery branches. Patients with acute myocardial infarction or a history of percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) were excluded from the cohort study. The final study population comprised 392 patients (Figure 1).

Figure 1 Flowchart for patient selection. CABG, coronary artery bypass grafting; CAG, coronary angiography; CTA, computed tomographic angiography; PCI, percutaneous coronary intervention.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No. 2023L-Y193) and individual consent for this retrospective analysis was waived.

Echocardiographic studies

Left ventricular ejection fraction (LVEF), interventricular septum thickness (IVST), left ventricular posterior wall thickness (LVPWT), left ventricular end-diastolic diameter (LVEDD), and left atrial end-systolic diameter (LAESD) could be acquired by previous standard process (10). The peak transient gradients in the LVOT were estimated by employing continuous-wave Doppler examination from the apical three-chamber view (Figure 2). These gradients were then calculated using the modified Bernoulli equation (gradient = 4V²), where V shows the average maximum velocity of LVOT. The estimation of pulmonary artery systolic pressure (PASP) was conducted in accordance with the 2015 European Society of Cardiology (ESC) Guidelines (11). PASP was calculated based on the peak velocity of the tricuspid regurgitant jet and the estimated right atrial pressure (RAP), using the formula: PASP = RAP + maximum pressure gradient (PG max). The calculation of PG max utilized a modified Bernoulli’s equation: PG max = 4V², where V represents the measured peak velocity of the tricuspid regurgitation jet obtained from an apical four-chamber view using continuous wave Doppler. RAP was estimated by assessing changes in inferior vena cava (IVC) size during respiration. None of the patients had any obstruction in their right ventricular outflow tract in the cohort study. The evaluation of mitral regurgitation remains in accordance with the 2015 ESC Guidelines (11).

Figure 2 Representative echocardiographic views in patient with hypertrophic cardiomyopathy. (A) Left ventricular wall asymmetrical hypertrophy in parasternal left ventricular long axis view. (B) Color Doppler flow of left ventricular outflow tract from the apical three-chamber view. (C) Continuous-wave Doppler of left ventricular outflow tract from the apical three-chamber view.

Clinical data collection

The clinical information, including demographics, family history of HCM, age at diagnosis, laboratory tests, imaging, clinical outcomes, and medical history, was searched through a database at Renmin Hospital of Wuhan University. Well-trained investigators collected the data in accordance with a consistent standard.

Statistical analysis

Data analysis was performed using SPSS (Version 27.0) and R software (Version 3.6.1). Continuous variables, denoted with mean values and standard deviations or median values plus interquartile ranges, were compared between groups using analysis of variance or the Kruskal-Wallis test, as appropriate. Categorical variables, presented by absolute numbers with percentages, were compared between the groups using the chi-square or Fisher’s exact test. The novel TTES was calculated based on the β coefficients from the multivariable logistic analysis and then divided into two groups according to the optimal cutoff value. The least absolute shrinkage and selection operator (LASSO) regression identified significant factors associated with substantial CAD in HCM patients by penalizing coefficient magnitude to select robust predictors from 28 candidate variables, thereby mitigating overfitting in high-dimensional data. Then, univariate and multivariable logistic regression analyses were used to build a predicting model by introducing the feature selected by LASSO regression in a forward stepwise procedure. A nomogram merging the important factors related to significant CAD in HCM patients was constructed using R software. Moreover, the receiver operator characteristic (ROC) curve was used to evaluate whether the TTES could improve the predictive value of clinical features for significant CAD in HCM patients. A value of P<0.05 was considered significant.

Results

Demographic characteristics

A total of 392 HCM patients were ultimately included in this study. Of those, 106 patients (27%) with significant CAD. The mean age was 59.4±12.0 years, and 114 patients (29.1%) were male. HCM patients with significant CAD were older and more likely to have hypertension and diabetes than those without significant CAD. Moreover, the blood lipid level involving triglyceride, total cholesterol, low-density lipoprotein cholesterol, and cardiac biomarkers including creatine kinase isoenzyme MB (CK-MB), cardiac troponin I (cTnI), N-terminal pro brain natriuretic peptide (NT-pro BNP)/cTnI, high sensitivity C-reactive protein (hs-CRP) were prescribed significantly higher in patients concomitant with significant CAD (Table 1).

Echocardiographic characteristics and TTES

Echocardiographic characteristics are shown in Table 1. The mean LAESD and IVST in the CAD group were 43.2±5.3 and 17.9±4.9 mm, significantly exceeding patients in HCM without CAD. E/e' and left ventricular outflow tract pressure gradient (LVOTPG) showed significant differences between the two groups. In addition, HCM patients with significant CAD accounted for more severe mitral regurgitation than patients without CAD. Systolic anterior motion (SAM) of the mitral valve was present in 11% of the cases, and there was a higher percentage of SAM in HCM with CAD patients. No significant differences were observed between the two groups in terms of LVEF, LVEDD, or other echocardiographic parameters.

TTES was derived from multivariable analysis of 14 initial parameters, with only 3 retained in the final simplified formula: LAESD, E/e' and LVOT obstruction status. As a consequence, the TTES was calculated as follows: −5.419 + 0.705 × LAESD (mm) + 0.114 × E/e' + 2.583 × obstruction of LVOT or not (yes = 1, no = 0). All patients were scored on the basis of a formula by joining significant factors that influenced HCM patients with CAD. Thus, TTES were divided into two groups based on the optimal cutoff value (−1.07) obtained by the Youden index. Compared with the low TTES group, the incidence of CAD patients, severe coronary artery stenosis of CAD, and 3-vessel disease of CAD were elevated in the high TTES group (P<0.01) (Table 2). Patients in the high TTES group had a higher risk of HCM with coronary artery disease (HCM-CAD) [odds ratio (OR), 6.46, 95% confidence interval (CI): 3.66–11.41, P<0.001]. Furthermore, patients with TTES ≥−1.07 had a higher risk of HCM-CAD than those with TTES <−1.07 in all subgroups (Figure 3).

Figure 3 The forest plot revealed the results of subgroup analysis for HCM with significant CAD based on low and high TTES groups in the crude cohort. CAD, coronary artery disease; CI, confidence interval; cTnI, cardiac troponin I; HCM, hypertrophic cardiomyopathy; hs-CRP, high sensitivity C-reactive protein; NT-pro BNP, N-terminal pro brain natriuretic peptide; OR, odds ratio; TTES, transthoracic echocardiography score.

Model construction established by LASSO regression on the basis of TTES and clinical features

Among the 28 variables including TTES and clinical features, 7 prospective predictors were identified by LASSO regression in the research (Figure 4A,4B). Univariate and multivariable logistic regression analysis based on the variables selected by LASSO regression were performed to construct the model and generate the nomogram, allowing clinicians to get easy access for accurate evaluation of HCM patients with significant CAD. Consequently, age, total cholesterol, triglyceride, cTnI, hs-CRP and TTES ultimately demonstrated a remarkable correlation with HCM-CAD (Table 3). Meanwhile, an HCM-CAD risk nomogram was taking shape and displayed in Figure 5.

Figure 4 Selection of significant factors associated with significant CAD with HCM by LASSO logistic regression model. (A) Identification of tuning parameters (λ) in the LASSO model. (B) Profiles of LASSO coefficients for clinical features. CAD, coronary artery disease; HCM, hypertrophic cardiomyopathy; LASSO, least absolute shrinkage and selection operator.

Figure 5 The predictive nomogram for HCM with significant CAD. CAD, coronary artery disease; cTnI, cardiac troponin I; HCM, hypertrophic cardiomyopathy; hs-CRP, high sensitivity C-reactive protein; TC, total cholesterol; TG, triglyceride; TTES, transthoracic echocardiography score.

ROC curve analysis of the value detection in HCM with CAD using different models

In the ROC curve analysis, the TTES showed a favorable predictive efficacy, with an area under the curve (AUC) of 0.755 (95% CI: 0.703–0.808; sensitivity, 67.0%, specificity, 68.2%) was superior to the clinic model separately with an AUC of 0.722 (95% CI: 0.668–0.776; sensitivity, 84.0%, specificity, 55.2%). Nonetheless, the clinic model combined with TTES indicated valuable predictive efficacy to the HCM-CAD, with an AUC of 0.834 (95% CI: 0.791–0.876; sensitivity, 84.0%, specificity, 68.9%) (Figure 6).

Figure 6 ROC curve analysis in detecting significant CAD with HCM using models. CAD, coronary artery disease; HCM, hypertrophic cardiomyopathy; ROC, receiver operator characteristic; TTES, transthoracic echocardiography score.

Discussion

Our study investigated 392 HCM patients who underwent CAG or CTA and revealed the following: (I) the prevalence of significant CAD, severe stenosis CAD and multi-vessel CAD was found to be 27.0%, 11.9%, and 12.5%, respectively; (II) Conventional echocardiography indices involving LAESD, E/e', and obstruction of LVOT were identified as independent predictors of significant CAD in patients with HCM. Additionally, TTES was established as a novel parameter for predicting significant CAD in HCM patients, demonstrating that it significantly enhances the predictive value of clinical features (3). A cutoff value of −1.07 was identified as a favorable threshold for assessing the risk of significant CAD in all subgroups of HCM patients (4). A nomogram based on TTES provided a refined prediction method for identifying significant CAD in patients with HCM.

HCM is the most common inherited heart disease, with a prevalence ranging from 1 in 500 to 1 in 200 individuals (1). The condition exhibits a significant degree of heterogeneity in clinical manifestations and progression among different patients. These manifestations may include LVOT obstruction, mitral regurgitation, diastolic dysfunction, arrhythmias, autonomic dysfunction, and myocardial ischemia (12). Among these complications, myocardial ischemia has been linked to adverse left ventricular remodeling and poor clinical outcomes in early studies of HCM (13). Myocardial ischemia in HCM arises from multiple complex pathophysiological mechanisms. Similar to the general population, epicardial coronary stenoses are significant contributor to myocardial ischemia in these patients. Moreover, sarcomeric mutations, microvascular remodeling, hypertrophy, extravascular compression, and LVOT obstruction contribute to myocardial ischemia (14), especially in severe hypertrophy. This condition reduces end-diastolic volume and cardiac output, further impairing myocardial perfusion. The prevalence of CAD in HCM patients varies widely based on the population studied and the diagnostic criteria used. Previous studies have reported that the occurrence rate of CAD among patients with HCM ranges from 11% to 53% (4-7). For instance, a retrospective study by Wu et al. involving 461 patients with HCM found that 235 had concomitant CAD (51%). Out of these, 87 patients (19%) were diagnosed with severe CAD, defined as having at least 70% luminal stenosis in one or more major epicardial coronary artery branches or at least 50% luminal stenosis in the left main coronary artery (15). Similarly, a study by Shin et al. (9) involving 98 consecutive patients with apical HCM reported CAD in 31 of the patients (31.6%). In this study, significant CAD was defined as luminal narrowing greater than 50% in at least one coronary artery. Moreover, a case-control study conducted by van der Velde et al. (7) evaluated coronary CTA in patients with HCM and found obstructive CAD rates of 18% among HCM patients, which were comparable to 19% in the control group. Our own research indicated that the prevalence of significant CAD among our patients was 27%. This increased incidence may be attributed to the older average age of our patients and a higher prevalence of risk factors for CAD, such as hypertension and diabetes.

Invasive CAG has long been regarded as the gold standard for diagnosing CAD. Recently, CTA has emerged as a non-invasive alternative for CAD detection. However, CTA has its drawbacks, including limitations in detecting diffuse calcification, exposure to radiation, and the potential for motion artifacts (16). Additionally, there is a growing trend of using the cardiac catheterization laboratory for interventional procedures after diagnosing CAD through non-invasive imaging techniques rather than solely relying on it as a diagnostic tool to confirm or deny the presence of CAD (17). The American Heart Association/American College of Cardiology Joint Committee recommends TTE as the preferred method for the initial evaluation of patients suspected of HCM (8). Consequently, conventional TTE is a practical and cost-effective imaging modality that is non-invasive and radiation-free, making it essential and possible for predicting CAD in patients with HCM. In our research, we established TTES to predict significant CAD in patients with HCM. Though LAESD, E/e', and LVOT obstruction are recognized HCM risk markers, TTES is the first model to combine these into a quantitative score specifically calibrated to predict significant CAD, enhancing risk stratification beyond isolated parameters. The ROC curve analysis demonstrated that TTES increased the AUC of the clinical model for predicting HCM concomitant with significant CAD from 0.722 to 0.834. Additionally, we categorized TTES into high and low groups based on a cutoff value of −1.07. Our analysis revealed that high TTES significantly raised the risk of HCM concomitant with significant CAD across all subgroups. Therefore, TTES ≥−1.07 identifies HCM patients at high CAD risk who may benefit from CAG. We propose a screening pathway utilizing TTE to calculate TTES. For patients with TTES ≥−1.07 and concomitant clinical risk factors, CTA or invasive CAG should be considered. Conversely, patients exhibiting TTES <−1.07 may safely defer invasive testing unless clinical symptoms progress.

While HCM and CAD frequently coexist, our data suggest this association is driven primarily by aging and traditional cardiovascular risk factors (e.g., hypertension, dyslipidemia) rather than direct pathophysiological links. However, the mechanism by which sarcomere hypertrophy directly impairs coronary artery function remains unclear. Sarcomeric hypertrophy alone may not inherently cause epicardial CAD. Instead, it can exacerbate ischemia in the presence of pre-existing coronary disease. Meanwhile, mediators of inflammation may play a role in the progression of CAD to some extent. Guler et al. (18) carried out research on the influence of cardiac biomarkers in predicting significant CAD and found that individuals with significant CAD had higher levels of high-sensitivity cardiac troponin T (hs-TnT) compared to those without CAD. Interestingly, it was also noticed that cTnI and hs-CRP were identified as strong independent risk factors for significant CAD in HCM in our research.

Myocardial ischemia and coronary microvascular dysfunction, which are key pathophysiological features of HCM, can be exacerbated by concomitant CAD. Furthermore, studies suggest a close association between these microvascular impairments and the development of diastolic dysfunction (19,20). In our study, we observed that the indicator of cardiac diastolic function involving E/e' and LAESD was an independent predictor of significant CAD in patients with HCM. Similarly, Jinno et al. (21) conducted an observational cohort study involving 142 patients who had undergone both CTA and echocardiography within a three-month period to evaluate significant CAD. They found that left atrial volume index and average E/e' were significantly lower in non-significant CAD group compared to the significant CAD group (23.5±7.6 vs. 33.6±7.4 mL/m², P<0.001; 8.1±2.6 vs. 9.9±3.0, P=0.004). Further analysis indicated that left atrial volume index (LAVI) cutoff of 29.0 mL/m² was the best predictor for excluding significant CAD, with a negative predictive value of 80.8%. Moreover, our study manifested that the obstruction of LVOT was also an independent predictor of significant CAD in HCM patients. However, there were some controversies about the relationship between LVOT obstruction and CAD. For instance, Yakupoglu et al. (22) reported on a 51-year-old man with HCM who underwent PCI and displayed myocardial hypokinesia in certain segments of the left ventricular wall. This occurred when the gradient of LVOT was increased from 17 to 132 mmHg during stress echocardiography. In contrast, Park et al. (23) explored the relationship between dynamic LVOT obstruction and the presence of CAD by dobutamine stress echocardiography. Their findings showed no significant correlation between dynamic LVOT obstruction and CAD (9 of 19 patients with dynamic LVOT obstruction in CAD vs. 43 of 83 patients with dynamic LVOT obstruction in no CAD, P=0.80). A recent retrospective study conducted in a community-based setting revealed that the prevalence of CAD in patients with obstructive HCM was found to be as high as 30% (24). It remains to be resolved whether the presence of obstruction influences the development of CAD in HCM, and further research is needed to address this issue.

Study limitations

First of all, the present study, similar to other retrospective studies, has inherent limitations due to selective bias. Exclusively symptomatic patients referred for angiography may overestimate CAD prevalence. Secondly, the lack of strain imaging limited mechanical parameter assessment and subclinical dysfunction evaluation with conventional echocardiography, necessitating deformation analysis in future studies. Thirdly, while diagnostic heterogeneity across techniques was observed in the study population, the established high negative and positive predictive values of coronary CTA justify its application in this setting. Finally, our research did not include data on the dynamic obstruction of the LVOT, which may underreport obstruction prevalence.

Conclusions

In this study, we initially established TTES as a readily accessible and cost-effective index. By integrating TTES with key clinical features into a predictive nomogram, high-risk CAD in HCM patients can be efficiently identified. This screening approach guides selective coronary angiography, optimizing resource allocation while enhancing CAD detection efficiency.

Acknowledgments

We wish to thank the members of the Ultrasound Imaging Team at Renmin Hospital of Wuhan University for their clinical support and assistance throughout this study.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-698/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-698/dss

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-698/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No. 2023L-Y193) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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