Effect of Renal Dysfunction on Risk of Sudden Cardiac Death in Patients With Hypertrophic Cardiomyopathy

Machine Learning-Based Discrimination of Cardiovascular Outcomes in Patients With Hypertrophic Cardiomyopathy

Background

Current risk stratification strategies for patients with hypertrophic cardiomyopathy (HCM) are limited to traditional methodologies.

Objectives

The authors aimed to establish machine learning (ML)-based models to discriminate major cardiovascular events in patients with HCM.

Methods

We enrolled consecutive HCM patients from 2 tertiary referral centers and used 25 clinical and echocardiographic features to discriminate major adverse cardiovascular events (MACE), including all-cause death, admission for heart failure (HF-adm), and stroke. The best model was selected for each outcome using the area under the receiver operating characteristic curve (AUROC) with 20-fold cross-validation. After testing in the external validation cohort, the relative importance of features in discriminating each outcome was determined using the SHapley Additive exPlanations (SHAP) method.

Results

In total, 2,111 patients with HCM (age 61.4 ± 13.6 years; 67.6% men) were analyzed. During the median 4.0 years of follow-up, MACE occurred in 341 patients (16.2%). Among the 4 ML models, the logistic regression model achieved the best AUROC of 0.800 (95% CI: 0.760-0.841) for MACE, 0.789 (95% CI: 0.736-0.841) for all-cause death, 0.798 (95% CI: 0.736-0.860) for HF-adm, and 0.807 (95% CI: 0.754-0.859) for stroke. The discriminant ability of the logistic regression model remained excellent when applied to the external validation cohort for MACE (AUROC = 0.768), all-cause death (AUROC = 0.750), and HF-adm (AUROC = 0.806). The SHAP analysis identified left atrial diameter and hypertension as important variables for all outcomes of interest.

Conclusions

The proposed ML models incorporating various phenotypes from patients with HCM accurately discriminated adverse cardiovascular events and provided variables with high importance for each outcome.

Mildly Reduced Renal Function Is Associated With Increased Heart Failure Admissions in Patients With Hypertrophic Cardiomyopathy

BACKGROUND

The association between renal dysfunction and cardiovascular outcomes has yet to be determined in patients with hypertrophic cardiomyopathy (HCM). We aimed to investigate whether mildly reduced renal function is associated with the prognosis in patients with HCM.

METHODS

Patients with HCM were enrolled at two tertiary HCM centers. Patients who were on dialysis, or had a previous history of heart failure (HF) or stroke were excluded. Patients were categorized into 3 groups by estimated glomerular filtration rate (eGFR): stage I (eGFR ≥ 90 mL/min/1.73 m², n = 538), stage II (eGFR 60-89 mL/min/1.73 m², n = 953), and stage III-V (eGFR < 60 mL/min/1.73 m², n = 265). Major adverse cardiovascular events (MACEs) were defined as a composite of cardiovascular death, hospitalization for HF (HHF), or stroke during median 4.0-year follow-up. Multivariable Cox regression model was used to adjust for covariates.

RESULTS

Among 1,756 HCM patients (mean 61.0 ± 13.4 years; 68.1% men), patients with stage III-V renal function had a significantly higher risk of MACEs (adjusted hazard ratio [aHR], 2.71; 95% confidence interval [CI], 1.39-5.27; P = 0.003), which was largely driven by increased incidence of cardiovascular death and HHF compared to those with stage I renal function. Even in patients with stage II renal function, the risk of MACE (vs. stage I: aHR, 2.21' 95% CI, 1.23-3.96; P = 0.008) and HHF (vs. stage I: aHR, 2.62; 95% CI, 1.23-5.58; P = 0.012) was significantly increased.

CONCLUSION

This real-world observation showed that even mildly reduced renal function (i.e., eGFR 60-89 mL/min/1.73 m²) in patients with HCM was associated with an increased risk of MACEs, especially for HHF.

Risk factors of sudden cardiac death in hypertrophic cardiomyopathy

PURPOSE OF REVIEW

Hypertrophic cardiomyopathy (HCM) is one of the leading causes of sudden cardiac death (SCD) in younger people and athletes. It is crucial to identify the risk factors for SCD in individuals with HCM. This review, based on recent systematic literature studies, will focus on the risk factors for SCD in patients with HCM.

RECENT FINDINGS

An increasing number of studies have further explored the risk factors for SCD in patients with HCM, and new risk markers have emerged accordingly. In addition, more accurate SCD risk estimation and stratification methods have been proposed and continuously improved.

SUMMARY

The identification of independent risk factors for HCM-related SCD would likely contribute to risk stratification. However, it is difficult to predict SCD with absolute certainty, as the annual incidence of SCD in adult patients with HCM is approximately 1%. The review discusses the established risk factors, such as a family history of SCD, unexplained syncope and some new risk factors. Taken together, the findings of this review demonstrate that there is a need for further research on individual risk factors and that SCD risk stratification in HCM patients remains a clinical challenge.

Age, creatinine clearance, and ejection fraction (mACEF) score predicts long-term cardiac mortality in patients with hypertrophic obstructive cardiomyopathy treated non-invasively

OBJECTIVE

Presently, an effective model to predict long-term cardiac mortality in patients with hypertrophic obstructive cardiomyopathy (HOCM) is lacking. Therefore, the objective of this study was to evaluate the predictive value of the modified Age, Creatinine clearance, and Ejection Fraction (mACEF) score for long-term cardiac mortality in patients with HOCM.

METHODS

Two hundred and ninety two patients with HOCM treated non-invasively were enrolled in this study, all of whom had intact medical information.

RESULTS

Over a median follow-up period of 41.9 months, 28 cardiac deaths occurred. In univariate Cox regression analysis, the mACEF score was associated with long-term cardiac death [hazard ratio (HR)=1.795, 95% confidence interval (CI) 1.518-2.124, p<0.001]. Multiple Cox regression analysis identified the mACEF score as an independent risk factor for long-term cardiac death (adjusted HR=1.372, 95% CI 1.076-1.749, p=0.011). Analysis of the receiver operating characteristic (ROC) for long-term cardiac death showed that the mACEF score had a considerable predictive value (area under ROC 0.844, sensitivity 89.29%, specificity 75.00%) with an optimum cut-off value of 0.96. The study population was divided into high-risk (mACEF score ≥0.96, n=91) and low-risk (mACEF score <0.96, n=201) groups according to the optimum cut-off value. Kaplan-Meier survival analysis was performed and showed a dramatic higher rate of long-term cardiac mortality in the high-risk group than in the low-risk group (27.4% vs. 1.7%, p<0.001 by log-rank test).

CONCLUSION

The mACEF score has a considerable predictive value for long-term cardiac mortality in patients with HOCM treated non-invasively. A mACEF score ≥0.96 could be considered as a sign of poor prognosis in patients with HOCM.