Gaps in Evidence for Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy

HomeCirculationVol. 143, No. 2Gaps in Evidence for Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy Free AccessArticle CommentaryPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessArticle CommentaryPDF/EPUBGaps in Evidence for Risk Stratification for Sudden Cardiac Death in Hypertrophic Cardiomyopathy Francesco Pelliccia, MD Bernard J. Gersh, MB, ChB, DPhil Paolo G. CamiciMD Francesco PellicciaFrancesco Pelliccia Francesco Pelliccia, MD, PhD, Sapienza University of Rome, Viale del Policlinico 155, 00166 Rome, Italy. Email E-mail Address: [email protected] https://orcid.org/0000-0003-1260-1308 Department of Cardiovascular Sciences, Sapienza University, Rome, Italy (F.P.). Search for more papers by this author , Bernard J. GershBernard J. Gersh Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN (B.J.G.). Search for more papers by this author , and Paolo G. CamiciPaolo G. Camici https://orcid.org/0000-0001-5584-0750 Vita-Salute University and San Raffaele Hospital, Milan, Italy (P.G.C.). Search for more papers by this author Originally published11 Jan 2021https://doi.org/10.1161/CIRCULATIONAHA.120.051968Circulation. 2021;143:101–103Sudden cardiac death (SCD) is the most dramatic and catastrophic complication of hypertrophic cardiomyopathy (HCM),1 with an annual rate of >1%.1 The identification of those patients most likely to benefit from prophylactic implantable cardioverter defibrillators remains challenging in children and adults. The positive predictive value for appropriate discharges is low (≈20%), and the complication rates in this younger, active population is substantial, including lead problems, infections, inappropriate discharges, and psychological issues.1Prediction of SCDThe assessment of SCD risk in HCM is discordant between the 2 sides of the Atlantic. In the 2011 North American guidelines,1 implantable cardioverter defibrillator is said to be a reasonable option (Class IIa recommendation) for patients with family history of SCD, unexplained syncope, or maximal wall thickness >30 mm. The indication for the device is uncertain (Class IIb) in those who have only nonsustained ventricular tachycardia or an abnormal blood pressure response with exercise in the absence of any other risk factor for SCD. Validation of this approach resulted in an area under the receiver operating characteristic curve for prediction of SCD of 0.64 at 5 years, which suggests limited power to discriminate high from low risk. The 2014 European guidelines recommend risk stratification using the HCM risk tool to stratify the 5-year risk of SCD.1 This model includes some factors already considered by the North American approach (wall thickness, family history, and syncope) with the addition of 3 variables: age, outflow tract gradient, and left atrial diameter. With this approach, patients can be grouped in three 5-year SCD risk categories (<4%, 4% to 6%, and >6%). Implantable cardioverter defibrillators are not recommended for the lowest-risk group, may be considered for the intermediate-risk group, and are mandatory for the highest-risk group. The European score was originally shown to have a good discriminatory power, despite suffering from the fact that some variables (ie, nonsustained ventricular tachycardia, outflow gradient, and left atrial size) are dynamic in nature and show significant variability.During 2019, however, 3 large investigations demonstrated that the score has a very low sensitivity and positive predictive value. First, Wang et al2 published the results of a systematic review in 9651 patients showing that the sensitivity of the HCM score ranges between 41% and 71%, and suggesting that the model may have different power for predicting SCD risk in different regions, especially in North America. In addition, Choi et al3 performed a longitudinal cohort study in 730 Korean patients and found that the score had a sensitivity of 36%. Of note, 7 of the 11 SCDs (64%) occurred in patients initially deemed low risk. Maron et al4 published results obtained in 2094 patients with HCM consecutively evaluated over 17 years. The European risk score had a sensitivity of 34%, consistent with recognizing fewer high-risk patients. A further reason for concern lies in the fact that the European score has recently been incorporated into a risk prediction model for sports activity. The 2020 European guidelines on exercise in patients with cardiovascular disease state that systematic restriction from competitive sport in all affected individuals with HCM is probably unjustified and that participation in all competitive sports may be reasonable after careful evaluation in those who have mild clinical expressions of disease and a low European risk score.Novel Predictors of Risk StratificationThe evidence that factors currently included in prediction of SCD perform poorly suggests the critical need to supplement traditional factors with novel markers of the basic mechanisms leading to SCD.1 Ventricular arrhythmogenesis in HCM relates to the combination of abnormal cellular substrate, ventricular anatomy, dynamic changes in hemodynamics, rhythm disturbances, and myocardial ischemia (Figure).Download figureDownload PowerPointFigure. Potential mechanisms of ventricular arrhythmia and sudden cardiac death (SCD) in hypertrophic cardiomyopathy. Ventricular arrhythmogenesis relates to the combination of abnormal cellular substrate, myocardial ischemia, ventricular anatomy, hemodynamics, known rhythm disturbances, and family history. EX indicates exercise; LA, left atrium; LV, left ventricular hypertrophy; and VT, ventricular tachycardia.Areas of disorganized architecture and myocardial fibrosis, which are common findings in HCM, might predispose patients to arrhythmic events resulting from a reentry circuit mechanism. These pathologic characteristics are now easily identified as late gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR). Analysis of LGE images can be performed automatically using commercially available software that has been demonstrated to allow reproducible and reliable LGE quantification also in nonreferral centers. Evidence exists that LGE can provide additional information for assessing SCD risk among patients with HCM, particularly those otherwise considered to be at low to intermediate risk.1 The North American recommendations recognize LGE as a modifying factor; the European guidelines do not take into consideration LGE as a prognostic marker.1In recent years, CMR has allowed more frequent identification of a subset of patients with thin-walled, left ventricular apical aneurysms, often associated with regional scarring and muscular midcavity obstruction.1 Left ventricular apical aneurysm represents an important example of a site of anatomic reentry for monomorphic ventricular tachycardia, which appears consistently at the junction of the scarred aneurysm rim and adjacent myocardium and has the potential to become a novel risk marker in HCM.Sixty years after the original recognition that myocardial disarray is the histologic hallmark of HCM, this characteristic chaotic architectural pattern of cardiac myocellular alignment should be recognized as another potential determinant of arrhythmogenesis in HCM. It is now possible to infer cardiac microstructural abnormalities by mapping the preferential diffusion of water along cardiac muscle fibers using diffusion tensor CMR. With this technique, a close association between higher burden of ventricular arrhythmias and greater extent of differing myocyte orientation as indicated by lower values of fractional anisotropy can be noted.Myocardial ischemia is a predictive factor that always should be taken into consideration in HCM.1 Myocardial ischemia is an established pathophysiologic feature of the disease, and its role as a strong, independent predictor of clinical deterioration and death has been clearly demonstrated. Marked structural abnormalities of the small intramural coronary arteries, including medial hypertrophy, intimal hyperplasia, and decreased luminal size, are the pathologic basis for coronary microvascular dysfunction and contribute to ischemia in HCM. Similar to what occurs in patients with coronary atherosclerosis, there is now agreement that chronic or recurrent ischemic injury might promote deposition of collagen leading to replacement fibrosis, a pathologic finding distinct from the progressive reactive interstitial fibrosis typically found in HCM.Prediction of SCD in 2021: A Way ForwardGuidelines on HCM are being revised and updated in light of recent evidence, including the emerging potential of individual sarcomere mutations to predict arrhythmic events and SCD.1 A possible contribution might be provided by the final results of the Hypertrophic Cardiomyopathy Registry, in which CMR markers, genotyping, and serum biomarkers are assessed to understand the relationship among these risk markers. The multifactorial pathophysiology of SCD including genotyping, biomarkers, CMR imaging, microvascular and autonomic dysfunction, exercise-induced ischemia, and myocardial disarray introduce considerable complexity in the development of risk stratification models. This should lend itself to the exploration of machine learning analytics. Preliminary studies suggest its role in the identification of patients with HCM, but further validation in different ethnic groups and athletes is needed.5 The next step will be to apply these analytic approaches to the development of models predicting SCD. There will be no lack of data with regard to HCM; the challenge will be to incorporate the information into a useful clinical tool.Disclosures Dr Gersh cochaired the 2011 American College of Cardiology Foundation/American Heart Association guidelines for the diagnosis and treatment of hypertrophic cardiomyopathy. The other authors report no conflicts.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.https://www.ahajournals.org/journal/circFrancesco Pelliccia, MD, PhD, Sapienza University of Rome, Viale del Policlinico 155, 00166 Rome, Italy. Email f.[email protected]itReferences1. Geske JB, Ommen SR, Gersh BJ. Hypertrophic cardiomyopathy: clinical update.J. A. C. C. Heart Fail. 2018; 6:364–375. doi: 10.1016/j.jchf.2018.02.010Google Scholar2. Wang J, Zhang Z, Li Y, Xu Y, Wan K, Chen Y. Variable and limited predictive value of the European Society of Cardiology hypertrophic cardiomyopathy sudden-death risk model: a meta-analysis.Can J Cardiol. 2019; 35:1791–1799. doi: 10.1016/j.cjca.2019.05.004CrossrefMedlineGoogle Scholar3. Choi YJ, Kim HK, Lee SC, Park JB, Moon I, Park J, Kim YJ, Sohn DW, Ommen S. Validation of the hypertrophic cardiomyopathy risk-sudden cardiac death calculator in Asians.Heart. 2019; 105:1892–1897. doi: 10.1136/heartjnl-2019-315160CrossrefMedlineGoogle Scholar4. Maron MS, Rowin EJ, Wessler BS, Mooney PJ, Fatima A, Patel P, Koethe BC, Romashko M, Link MS, Maron BJ. Enhanced American College of Cardiology/American Heart Association strategy for prevention of sudden cardiac death in high-risk patients with hypertrophic cardiomyopathy.JAMA Cardiol. 2019; 4:644–657. doi: 10.1001/jamacardio.2019.1391CrossrefMedlineGoogle Scholar5. Ko WY, Siontis KC, Attia ZI, Carter RE, Kapa S, Ommen SR, Demuth SJ, Ackerman MJ, Gersh BJ, Arruda-Olson AM, et al.. Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram.J Am Coll Cardiol. 2020; 75:722–733. doi: 10.1016/j.jacc.2019.12.030CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails January 12, 2021Vol 143, Issue 2Article InformationMetrics Download: 2,212 © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.120.051968PMID: 33428428 Originally publishedJanuary 11, 2021 Keywordsriskdeath, sudden, cardiacmachine learningprevention & controlcardiomyopathy, hypertrophicPDF download SubjectsCardiomyopathyRisk FactorsMachine Learning and Artificial IntelligencePrimary PreventionSudden Cardiac Death