European Heart Journal (2022) 43, 3041–3052 CLINICAL RESEARCH https://doi.org/10.1093/eurheartj/ehac289 Arrhythmias Arrhythmic risk prediction in arrhythmogenic right ventricular cardiomyopathy: external Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 validation of the arrhythmogenic right ventricular cardiomyopathy risk calculator Paloma Jordà 1,2,3†, Laurens P. Bosman4†, Alessio Gasperetti5, Andrea Mazzanti6,7,8, Jean-Baptiste Gourraud9, Brianna Davies10, Tanja Charlotte Frederiksen11,12, Zoraida Moreno Weidmann13, Andrea Di Marco14, Jason D. Roberts15,16,17, Ciorsti MacIntyre18, Colette Seifer19, Antoine Delinière 20, Wael Alqarawi21, Deni Kukavica 6,7,8, Damien Minois 9, Alessandro Trancuccio 6,7,8, Marine Arnaud 9, Mattia Targetti22, Annamaria Martino23, Giada Oliviero23, Daniel C. Pipilas24, Corrado Carbucicchio25, Paolo Compagnucci26, Antonio Dello Russo26, Iacopo Olivotto22, Leonardo Calò 23, Steven A. Lubitz24, Michael J. Cutler28, Philippe Chevalier 20, Elena Arbelo 2,3,29,30, Silvia Giuliana Priori 6,7,8, Jeffrey S. Healey15,16, Hugh Calkins 5, Michela Casella 27, Henrik Kjærulf Jensen11,12, Claudio Tondo25,31, Rafik Tadros 1, Cynthia A. James5, Andrew D. Krahn10, and Julia Cadrin-Tourigny 1* 1 Cardiovascular Genetics Center, Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada; 2Arrhythmia Section, Cardiology Department, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; 3Institut d’Investigació August Pi i Sunyer (IDIBAPS), Barcelona, Spain; 4Division of Heart and Lungs, Department of Cardiology, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands; 5Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA; 6Department of Molecular Medicine, University of Pavia, Pavia, Italy; 7Department of Molecular Cardiology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy; 8Department of Molecular Cardiology, Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain; 9Department of Cardiology, Centre Hospitalier Universitaire Nantes, Nantes, France; 10Centre for Cardiovascular Innovation, Division of Cardiology, University of British Columbia, Vancouver, BC, Canada; 11Department of Cardiology, Aarhus University Hospital, Aarhus N, Denmark; 12 Department of Clinical Medicine, Health, Aarhus University, Aarhus N, Denmark; 13Department of Cardiology, Hospital de la Santa Creu i Sant Pau, IIB Sant Pau, Universitat Autònoma de Barcelona, CIBERCV, Barcelona, Spain; 14Arrhythmia Unit, Department of Cardiology, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain; 15 Population Health Research Institute, McMaster University, Hamilton, ON, Canada; 16Division of Cardiology, Hamilton Health Sciences, Hamilton, ON, Canada; 17Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, ON, Canada; 18Cardiac Electrophysiology Service, Quenn Elisabeth II Health Sciences Center, Dalhousie University, Halifax, NS, Canada; 19St-Boniface Hospital, University of Manitoba, Winnipeg, MB, Canada; 20National Reference Center for Inherited Arrhythmias of Lyon, Louis Pradel Cardiovascular Hospital, Hospices Civils de Lyon, Lyon, France; 21Cardiac Electrophysiology Service, Ottawa Heart Institute, University of Ottawa, Ottawa, ON, Canada; 22Cardiomyopathy Unit, Department of Cardiology, Careggi University Hospital, Florence, Italy; 23Department of Cardiology, Policlinico Casilino, Rome, Italy; 24Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA; 25Department of Clinical Electrophysiology and Cardiac Pacing Centro Cardiologico Monzino, IRCCSC, Milan, Italy; 26Department of Biomedical Sciences and Public Health, Cardiology and Arrhythmology Clinic, University Hospital Umberto I-Salesi- Lancisi, Marche Polytechnic University, Ancona, Italy; 27Department of Clinical, Special and Dental Sciences, Cardiology and Arrhythmology Clinic, University Hospital Umberto I-Salesi- Lancisi, MarchePolytechnic University, Ancona, Italy; 28Intermountain Medical Center Heart Institute, Intermountain Medical Center, Murray, UT, USA; 29Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; 30European Reference Network for rare, low prevalence and complex diseases of the heart - ERN GUARD-Heart; and 31Department of Biomedical, Surgical and Dentistry Sciences, University of Milan, Milan, Italy Received 17 November 2021; revised 16 April 2022; accepted 18 May 2022; online publish-ahead-of-print 29 June 2022 See the editorial comment for this article ‘Arrhythmogenic right ventricular cardiomyopathy: the never-ending quest for a risk calcu- lator’, by Estelle Gandjbakhch and Annina S. Vischer, https://doi.org/10.1093/eurheartj/ehac324. * Corresponding author. Tel: +1 514 376 3330, Fax: +1 514 593 2496, Email: julia.cadrin-tourigny@umontreal.ca † Share first authorship. © The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com 3042 P. Jordà et al. Abstract Aims Arrhythmogenic right ventricular cardiomyopathy (ARVC) causes ventricular arrhythmias (VAs) and sudden cardiac death (SCD). In 2019, a risk prediction model that estimates the 5-year risk of incident VAs in ARVC was developed (ARVCrisk.com). This study aimed to externally validate this prediction model in a large international multicentre cohort Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 and to compare its performance with the risk factor approach recommended for implantable cardioverter-defibrillator (ICD) use by published guidelines and expert consensus. ......................................................................................................................................................................................... Methods In a retrospective cohort of 429 individuals from 29 centres in North America and Europe, 103 (24%) experienced sus- and results tained VA during a median follow-up of 5.02 (2.05–7.90) years following diagnosis of ARVC. External validation yielded good discrimination [C-index of 0.70 (95% confidence interval-CI 0.65–0.75)] and calibration slope of 1.01 (95% CI 0.99– 1.03). Compared with the three published consensus-based decision algorithms for ICD use in ARVC (Heart Rhythm Society consensus on arrhythmogenic cardiomyopathy, International Task Force consensus statement on the treatment of ARVC, and American Heart Association guidelines for VA and SCD), the risk calculator performed better with a su- perior net clinical benefit below risk threshold of 35%. ......................................................................................................................................................................................... Conclusion Using a large independent cohort of patients, this study shows that the ARVC risk model provides good prognostic in- formation and outperforms other published decision algorithms for ICD use. These findings support the use of the mod- el to facilitate shared decision making regarding ICD implantation in the primary prevention of SCD in ARVC. Structured Graphical Abstract Validation of the arrhythmogenic right ventricular cardiomyopathy (ARVC) risk calculator in a distinct cohort. AHA, American Heart Association; ECG, electrocardiogram; HRS, Heart Rhythm Society; ICD, implantable cardioverter-defibrillator; ITFC, International Task Force Criteria; NSVT, non-sustained ventricular tachycardia; PVC, premature ventricular complex; VA, ventricular arrhythmia. ......................................................................................................................................................................................... External validation of the ARVC arrhythmic risk calculator 3043 Keywords Arrhythmogenic right ventricular cardiomyopathy • Implantable cardioverter-defibrillator • Sudden cardiac death • Ventricular arrhythmias • Risk stratification • Genetic cardiomyopathies study (Montreal Heart Institute and Johns Hopkins Hospital) were in- Introduction Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 cluded (52 patients; 12% of the cohort). No patients in the current co- Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a sig- hort were included in the original ARVC derivation cohort. From each nificant cause of sustained ventricular arrhythmia (VA) and sudden site, consistent with the derivation cohort, consecutive patients who (i) were diagnosed with definite ARVC as per 2010 Task Force cardiac death (SCD), especially in young individuals and athletes. Criteria (TFC),13 (ii) were alive at presentation, and (iii) had not experi- Preventing this catastrophic outcome through the prophylactic use enced spontaneous sustained VA or sudden cardiac arrest (SCA) at diag- of implantable cardioverter-defibrillators (ICDs) is a cornerstone nosis were included. The study conforms to the Helsinki declaration and of the disease management. Given the significant drawbacks asso- was approved by local ethics and/or institutional review boards. To main- ciated with ICDs in this young and active population, appropriate pa- tain patient confidentiality, data, and study materials will not be made tient selection is essential. available to other researchers for purposes of replicating the results. A Over the past 25 years, numerous studies have identified predic- limited dataset may be made available upon request. tors of sustained VA and SCD in ARVC and consensus documents have integrated these in decision algorithms for ICD use.1–3 Data collection Building on this knowledge, a risk prediction model for sustained Data were collected independently by each of the participating centres VA and SCD in ARVC was recently developed in a multinational co- using uniform definitions. A complete list of variables and their definitions hort (n = 528, designed as the derivation cohort) mostly including can be found in Supplementary material online, Table S2. Genetic var- high volume referral centres for ARVC.4 This prediction model pro- iants were reviewed according to the American College of Medical vides individualized prediction of the risk of VA in patients with Genetics and Genomics guidelines by cardiologists specialized in ARVC without a prior history of sustained VA. Since its online pub- cardiovascular genetics (R.T. and J.C.T.).14 lication, the risk calculator’s official site (http://www.ARVCrisk.com) has been used 20 000 times illustrating its uptake in clinical practice. Missing data The model has been internally and externally validated in small Patients with .50% of predictors missing were excluded from the ana- studies.4–9 However, adequately powered external validation is still lysis. Missingness was assumed to be at random and imputed using mul- lacking,10 yet is paramount to confirm the reproducibility, generaliz- tiple imputation by chained equations.15 Missing quantitative values for ability, and need to update the model in an independent population. right ventricular ejection fraction (RVEF) and left ventricular ejection fraction (LVEF) were imputed manually when only qualitative assessment The aims of the present study are thus (i) to conduct external val- was available as done previously4 and detailed in Supplementary material idation of the published risk calculator in a distinct, adequately pow- online, Table S2. The multiple imputation model included all pre-specified ered, and geographically diverse cohort including patients from six predictors, proband status and genotype together with the outcome, countries across North America and Europe and (ii) to compare and cumulative baseline hazard estimation.16 A total of 25 imputed data- the performance of the risk prediction model with other published sets were generated, and the final inference estimations were combined guidelines and expert consensus recommendations for ICD use. using Rubin’s rules.17 During the current validation study, our group detected an inaccur- acy in the formula of the original ARVC risk calculator published in Study outcomes 2019. It was corrected both on the website (ARVCrisk.com) and In accordance with the published ARVC risk prediction model which this in the published manuscript.11 We base the present study on the study aims to validate, the primary outcome was the first sustained VA corrected risk calculator. following the definite diagnosis as per the TFC. Sustained VA was defined as a composite of the occurrence of SCD, SCA, spontaneous sustained ventricular tachycardia (VT; lasting ≥30 s at ≥100 b.p.m. or with haemo- Methods dynamic compromise requiring cardioversion), ventricular fibrillation/ flutter, or appropriate ICD intervention. Heart transplantation, cardio- Study design vascular mortality, and all-cause mortality were also collected. We conducted an observational, retrospective, longitudinal cohort study in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Predictor variables and risk calculator The same candidate predictors as those selected in the published model statement.12 based on prior literature were considered.18–20 These include sex, age, recent cardiac syncope (here defined as transient loss of consciousness Study population and postural tone with spontaneous recovery with a likely arrhythmic The study population was derived from 29 centres (see Supplementary mechanism, within a year of diagnosis), non-sustained VT (NSVT: defined material online, Table S1) in six European and North American countries. as hemodynamically stable VT at ≥100 b.p.m., for ≥3 beats ,30 s), num- This current cohort will be designated as the ‘validation cohort’ while the ber of premature ventricular complexes (PVCs) on 24-h Holter moni- cohort leading to the published model will be designated as the ‘deriv- toring, the extent of T-wave inversion (TWI) on anterior and inferior ation cohort’. New patients from two centres participating in the original leads, and RVEF. Each predictor variable was determined at the time of 3044 P. Jordà et al. diagnosis, defined as 1 year before to 1 year after the date of diagnosis as allows testing whether a model specifically fitted to the validation dataset per 2010 TFC and prior to the occurrence of the primary outcome. performs better than the original model in the validation dataset. The 5-year risk of sustained VA for an individual patient as per the pub- lished model is calculated using the following equation4: Subgroup analyses We visually explored the performance of the model specifically in differ- P(VA at 5 years) = 1 − 0.8396exp (LP) ent populations of interest by comparing calibration plots for these sub- groups. We stratified the cohort by geographic origin (Europe vs. North where the linear predictor (LP) was calculated according to the equation: Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 America), by proband status and by plakophilin 2 carrier status (PKP2; causal variant carrier vs. non-carrier). We did not report quantitative LP = 0.488 × male sex − 0.022 × age + 0.657 markers of performance such as the C-statistic as this study was not × history of recent cardiac syncope + 0.811 × history of NSVT powered adequately for these subgroups. To assess the impact of carrying an ICD on prediction accuracy, we + 0.170 × ln(24-h PVC count) + 0.113 also presented calibration plots based on ICD carrier status at baseline × sum of anterior and inferior leads with TWI − 0.025 defined as ICD implantation prior to a year following diagnosis and first × RVEF. VA outcome, whichever came first. Of note, the baseline hazard for 5-year prediction (0.8396) has been cor- Clinical utility rected since the initial publication in 2019.11 To assess the relative clinical utility of the risk prediction model, it was compared to three other published expert consensus algorithms for ICD implantation in ARVC: the 2015 International Task Force Statistical analysis Consensus for the treatment of ARVC (ITFC),18 the 2017 American Analyses were performed with RStudio version 1.3.1093 (Boston, MA, Heart Association (AHA) guidelines for the management of VA and pre- USA). Continuous variables were expressed as mean + standard devi- vention of SCD,2 and the 2019 Heart Rhythm Society (HRS) consensus ation or median [interquartile range (IQR)] and compared using either on arrhythmogenic cardiomyopathy (excluding programmed ventricular the independent sample t-test or the Mann–Whitney U test. stimulation)22 through decision curve analysis. In a decision curve ana- Categorical variables were presented as frequencies (%) and compared lysis,23 the clinical benefit is assessed by the ‘net benefit’ representing using the Fisher’s exact test. Follow-up duration was calculated as the the balance between useful (i.e. in patients with events) vs. useless (i.e. time interval between the time of definite diagnosis according to TFC in patients without events) ICD placement at 5 years weighted according and the endpoint or censoring. Censoring was defined as death from to the threshold used for ICD implantation. More specifically, the deci- any other cause, heart transplantation or the most recent follow-up visit sion curve uses the following formula: at which the endpoint could be ascertained. Event-free survival probabil- ity was estimated using the Kaplan–Meier method and Cox proportional hazard regression analysis. True positives/total sample size − false positives/total sample size × (pt/1 − pt) Model validation where ‘pt’ represents threshold probability, in the current case, The approach to external validation follows the method suggested by threshold for ICD implantation. Therefore, the higher the threshold Royston and Altman for Cox prognostic models.21 First, the overall dis- used, the greater the harm of useless ICD use (i.e. false positive) is va- criminative performance of the model was measured using Harrell’s lued. Higher values indicate greater benefit while a value of 0 indicates C-statistic, and the model fit by calculating the calibration slope, the re- no benefit. gression of the LP (i.e. the product of the variable part of the Cox model) To present the consequence of setting different thresholds for ICD in the current cohort (validation cohort). Graphical evaluation of calibra- implantation, we evaluated and plotted the proportion of patients who tion was performed by plotting the predicted risk against the observed would receive ICDs and the proportion of treated and missed events risk of sustained VA, using grouped Kaplan–Meier estimates and the con- at each threshold. We compared these with the recommendations for tinuous hazard regression function. The choice of the number of groups ICD use by the three published consensus mentioned above [ITFC(1), presented was based on the balance between providing sufficient spread AHA (2), HRS(3)]. in group risk, while maintaining adequate group sizes for precision. For the complete cohort, five groups are presented while for subgroup ana- lyses, four groups are presented. Results Subsequently, a more in-depth analysis of the model fit was performed by a Cox’s model including the same predictor variables in combination The study population included 429 definite ARVC patients without a with the LP of the original model (as an offset variable) to evaluate po- history of sustained VA or SCA at the time of diagnosis aged 43.1+ tential differences in the regression coefficients of each individual predict- 15.8 years and slightly more than half (n = 235, 54.8%) were male. or. The result indicating the validity of the model would be that if all Probands accounted for two-thirds of the cohort (n = 278, coefficients ß* equalled 0, reflecting that all the variability in the validation 64.8%). Half (n = 198, 46.6%) of patients had a pathogenic or likely sample is accounted for by the published model. In addition, the baseline pathogenic variant in a gene with definite or moderate association survival function of the validation dataset was compared to that of the derivation dataset to see if the overall predictions need to be globally with ARVC,24 which represents 70% (198 patients) of the 282 pa- shifted upward or downward. Lastly, a new prediction model using the tients for whom the complete genetic information was available. same predictor variables was fitted to the validation dataset and com- PKP2 was the most common genotype, carried by 111 patients pared to the fit of the original model using the Akaike information criter- (26%) followed by DSP in 38 patients (9%). Compared to PKP2 pa- ion (AIC), with a difference of .2 defined as statistically significant. This tients, DSP patients were more likely to have a decrease in LVEF , External validation of the ARVC arrhythmic risk calculator 3045 Table 1 Baseline clinical characteristics Overall (n = 429) Non-sustained VA (n = 326) Sustained VA (n = 103) P-value ................................................................................................................................................................................. Demographics and genetics Age at diagnosis (years) 43.1 + 15.8 44.1 + 15.7 40.1 + 16.0 0.025 Male sex 235 (54.8) 159 (48.8) 76 (73.8) ,0.001 Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 Proband status 278 (64.8) 197 (60.4) 81 (78.6) 0.001 (Likely) pathogenic variants (n = 282) 198 (46.2) 150 (46.0) 48 (46.6) 0.480 Genotype 0.302 PKP2 111 (25.6) 84 (25.8) 27 (26.2) DSP 38 (8.9) 33 (10.1) 5 (4.9) DSG2 27 (6.3) 22 (6.7) 5 (4.9) DSC2 3 (0.7) 1 (0.3) 2 (1.9) JUP 0 (0.0) 0 (0.0) 0 (0.0) TMEM43 10 (2.3) 4 (1.2) 6 (5.8) PLN 3 (0.7) 3 (0.9) 0 (0.0) Multiple mutations 6 (1.4) 3 (0.9) 3 (2.9) Clinical history Recent cardiac syncope (n = 424) 37 (8.6) 16 (4.9) 21 (20.4) ,0.001 ECG/continuous ECG monitoring TWI in ≥3 precordial leads (n = 409) 250 (58.3) 187 (57.4) 63 (61.2) 0.295 TWI in ≥2 inferior leads (n = 403) 109 (25.4) 81 (24.8) 28 (27.2) 0.589 PVC count (n = 324) 1434 (439–3601) 1354 (400–3719) 1676 (602–3492) 0.160 NSVT (n = 359) 148 (34.5) 105 (32.2) 43 (41.7) 0.001 Imaging RVEF (%) (n = 410) 45 (36–53) 47 (38–53) 40 (35–48.5) ,0.001 LVEF (%) (n = 404) 57 (51–60) 57 (51–61) 57 (50–60) 0.049 Treatment at baseline ICD 175 (40.8) 113 (34.7) 62 (60.2) ,0.001 Anti-arrhythmic drugs (n = 408) 0.041 Amiodarone 23 (6.0) 16 (4.9) 10 (9.8) Sotalol 79 (18.4) 55 (16.9) 24 (23.3) Propafenone/flecainide 15 (3.5) 9 (2.8) 6 (5.8) β-blockers (n = 407) 206 (48.0) 156 (47.9) 50 (48.5) 0.50 Follow-up 5.02 (2.05–7.90) 4.48 (1.86–7.32) 6.12 (2.60–10.08) 0.002 Variables are expressed as frequency (%), mean + standard deviation, or median (interquartile range). Total number of patients with available data for a given variable are mentioned in parenthesis for variables with missing data. DSC2, desmocollin-2; DSG2, desmoglein-2; DSP, desmoplakin; ICD, implantable cardioverter-defibrillator; LVEF, left ventricular ejection fraction; NSVT, non-sustained ventricular tachycardia; JUP, junction plakoglobin; PKP2, plakophilin-2; PLN, phospholamban; PVC, premature ventricular complex; RVEF, right ventricular ejection fraction; TMEM43, transmembrane protein 43; TWI, T-wave inversion; VA, ventricular arrhythmia. 50% (44.7% vs. 6.4%) but less likely to have VA events at follow-up Supplementary material online, Table S4, and a comparison of the (13.2% vs. 24.5%). Baseline characteristics according to genotype derivation and validation cohort populations is presented in are presented in Supplementary material online, Table S3. Other clin- Supplementary material online, Table S5. ical and demographic characteristics are summarized in Table 1. Overall, 299 (70.0%) patients had complete data for the pre- Baseline characteristics by country of origin are presented in specified predictors. Six of the eight predictors had missing data: 3046 P. Jordà et al. recent cardiac syncope (n = 5, 1.17%), NSVT (70 = 16.32%), PVC event rate of 4.98% [95% confidence interval (CI) 4.07–6.04]. Figure 1 count (n = 105, 24.48%), extent of leads with TWI (n = 26, 6.06%) shows the cumulative survival free from first sustained VA. and RVEF (n = 19, 4.43%). From an initial cohort of 433 patients, Among patients who experienced sustained VA during follow-up, four patients were excluded as .50% of their predictors were miss- the most common events were ICD treated VAs, which represented ing (four predictors or more). 59.2% of events (n = 61), followed by sustained VT (n = 32, 31.1%), SCA (n = 7, 6.8%), and SCD (n = 3, 2.9%). In patients with sustained Outcomes or ICD treated VT events, the median cycle length (available in 57/93 Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 During a median follow-up of [5.02 (2.05–7.90)] years, 103 patients events) was 280 ms (IQR: 246–315) which corresponds to (24%) experienced sustained VA events corresponding to an annual 214 b.p.m. (190–243). At last follow-up, 9 (2.1%) patients had died, including 2 from non- cardiac causes, and 7 (1.6%) had undergone heart transplantation. External validation Model validation revealed a Harrell C-index of 0.70 (95% CI 0.65– 0.75). The calibration slope was 1.01 (95% CI 0.99–1.03) showing no significant difference in discrimination. The calibration of the model is graphically presented in Figure 2 demonstrating good overall agreement between predicted and observed shorter-term (1 year) and longer-term durations (5 year) with no significant over or under prediction across the complete risk spectrum. The distribution of pa- tients according to their risk is presented in Supplementary material online, Figure S1 and calibration plots for intermediate durations (1, 2, 3, and 5 years) in Supplementary material online, Figure S2. Two different aspects of the model fit or potential misspecification were evaluated. First, the assessment of individual predictor coeffi- cients (Figure 3A) all showed no significant diversion from the original model in this cohort. This finding means that none of the individual coefficient would benefit from being modified from their original va- Figure 1 Survival free from sustained ventricular arrhythmia at follow-up. The cumulative event-free survival for any ventricular ar- lues to improve prediction in this cohort. rhythmia with 95% confidence intervals (shaded area) is plotted. Second, the baseline survival function (i.e. predictors-adjusted sur- vival) was assessed through the comparison of the baseline survival A B 0.6 0.6 0.5 0.5 Observed 5–year risk Observed 1–year risk 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 Total (n) = 429 Total (n) = 429 Events = 103 Events = 103 Avg. group size = 86 0.0 0.0 Avg. group size = 86 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Predicted 1–year risk Predicted 5–year risk Figure 2 Calibration plots presenting the agreement between predicted (x-axis) and observed (y-axis) 1-year (Panel A) and 5-year (Panel B) risk of ventricular arrhythmia. Triangles represent binned Kaplan–Meier estimates with 95% confidence intervals for quintiles of predicted risk. The straight line is the continuous calibration hazard regression with the dotted line represents optimal calibration (i.e. perfect correspondence between pre- dictions and observations across the risk spectrum). The calibration is shown to be acceptable across the risk spectrum with no significant under or over prediction in any risk category. VA, ventricular arrhythmia. External validation of the ARVC arrhythmic risk calculator 3047 Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 Figure 3 Assessment of the model fit. Assessment of the individual predictors (A) show an absence of diversion from the initial model as all coeffi- cients are non-significantly different from 0. Compared survival probability of the derivation and validation cohorts (B) and baseline survival hazard (i.e. predictors-adjusted survival) presented as survival curves (C) both show similar expected survival. NSVT, non-sustained ventricular tachycardia; PVC, premature ventricular complex; TWI, T-wave inversion; RVEF, right ventricular ejection fraction. probabilities (i.e. predictors-adjusted survival) in the derivation and from VA. As an example, a threshold of 15% would results in im- the validation cohorts at different time points showing similar ex- planting 59.4% of patients with ICDs while protecting 85.7% of pa- pected survival curves as shown numerically and visually in tients with incident VA events. Figure 3B and C. These findings suggest that the survival function does not need to be modified to improve prediction in this cohort. Subgroups analyses Finally, the potential need to update the model was assessed by The performance in subgroups of interest was visually explored by comparing the fit of the published model with the derivation of a calibration plots presented in Supplementary material online, new model in the validation cohort. The AIC of the published model Figure S4. This cohort was not sufficiently powered to provide defin- in the current cohort (1059.14) and of a model derived in this cohort ite answers in these subgroups. (1060.93) were not significantly different (absolute difference in AIC Calibration appeared acceptable in patients from both Europe and of 1.79) indicating the absence of significant improvement in predic- North America, although this analysis had low precision in the North tions when fitting a model to this population. American population due to its smaller size. As a sensitivity analysis, we repeated the process in patients with The model performed well both in probands and family members complete data (n = 299) resulting in a similar C-statistic, calibration with a possible trend toward overestimation in family members in slope, baseline risk, and calibration plot (see Supplementary the lower risk spectrum. The calibration was also visually acceptable material online, Figure S3). both in PKP2 carriers and non-carriers. Calibration plots according to the presence of an ICD show an ac- Clinical utility ceptable agreement between predictions and observations with a We compared the performance of the risk calculator with published tendency towards overestimation in non-ICD carriers and under- consensus-based decision algorithms for ICD use in ARVC. As illu- estimation in ICD carriers in the higher risk spectrum (see strated in Figure 4, the risk calculator generally had a superior net clin- Supplementary material online, Figure S5). ical benefit when compared to the other published algorithms for ICD use. Its performance becomes similar to the HRS consensus Discussion above a risk of 35%. Finally, we graphically presented the impact of different threshold In this study, we validated the published ARVC risk calculator in an for ICD implantation on the proportion of ICD use and the protec- independent cohort of patients from 29 centres in 6 countries in tion rate and compared to the published decision algorithms North America and Europe. Since its publication in 2019, the risk cal- (Figure 5). Higher thresholds result in less ICD use but less protection culator had a significant uptake in clinical practice. Ensuring its 3048 P. Jordà et al. Comparison of the internal and external 0.20 None validation populations All While based on the same inclusion criteria (i.e. definite diagnosis of Risk calc ARVC and no prior history of sustained VA at the time of diagno- 0.15 ITFC AHA sis), the initial risk calculator included a high proportion of patients HRS treated at highly specialized ARVC referral centres. Thus, a signifi- Net benefit 0.10 cant concern regarding this population is a possible selection bias Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 due to the preferential referral of patients for adverse disease pro- 0.05 gression (i.e. recurrent VA referred for ablation and severe heart failure for advanced therapies). This could potentially hamper ex- –0.05 0.00 ternal validity. The present cohort derived from 29 different cen- tres in 6 countries is thus likely to reflect a more diverse ARVC population. Expectedly, the annual event rate in this validation co- 0.0 0.1 0.2 0.3 0.4 0.5 hort (4.98%, 95% CI 4.07–6.04) was slightly lower, although non- Threshold probability significantly, than in the derivation population (5.6%, 95% CI 4.7– 6.6) during a similar follow-up period [5.02 (2.05–7.90) years in Figure 4 Decision curve analysis comparing the clinical utility of our the validation versus 4.83 (2.44–9.33) years in the derivation co- model (dashed thick black line) with the 2015 International Task Force hort]. This reflects the overall high risk of VA events in definite Consensus Statement algorithm for the treatment of arrhythmogenic ARVC patients such as those included in this study which is consist- right ventricular cardiomyopathy (dashed red line), the 2017 American ent with prior literature and often preceding structural Heart Association algorithm for the management of ventricular ar- changes.4,19,25–28 rhythmia and prevention of sudden cardiac death (dashed green line) and the 2019 Heart Rhythm Society consensus on arrhythmo- Some differences between the two cohorts (shown in genic cardiomyopathy with exclusion of the Programmed ventricular Supplementary material online, Table S4) might have limited the stimulation (dashed blue line). The clinical utility of each treatment potential discrepancy in event rates, such as a higher proportion of strategy is compared by plotting the net benefit (y-axis) for a range probands (64.8% vs. 49.8%, P , 0.001) and males (54.8% vs. 44.7%, of possible implantable cardioverter-defibrillator placement thresh- P = 0.002) in the current cohort. Conversely, patients in the present olds based on the 5-year risk of ventricular arrhythmia (x-axis). cohort were slightly older (43.1 vs. 38.2 years of age, P , 0.001), had Higher net benefit values indicate greater benefit while a value of 0 in- less recent cardiac syncope and NSVT. The proportion of patients dicates no benefit. The published risk calculator depicted a better net with decreased LVEF (,50%) was also higher in this cohort (17.7 benefit than the other published algorithms for implantable vs. 12.7, P = 0.002). Although individuals in the current population cardioverter-defibrillator implantation thresholds below a 35%. were more likely to receive anti-arrhythmic drugs (P , 0.001) and Above this threshold its performance was similar to the Heart β-blockers (48.0 vs. 37.9, P = 0.001), the proportion of ICD carriers Rhythm Society consensus algorithm. ICD, implantable cardioverter- defibrillator; ARVC, arrhythmogenic right ventricular cardiomyopathy, at baseline was similar (41.1 vs. 40.8 P = 0.98). Finally, while still re- VA ventricular arrhythmia, SCD sudden cardiac death. presenting the predominant genotype, the proportion of patients with PKP2 causal variants was lower than in the derivation cohort (39.4% vs. 51.1% of tested patients) factoring that the current cohort has a lower proportion of patients with known genetic information. This predominance of PKP2 genotype is consistent with prior litera- ture including patients with definite ARVC diagnosis.29 The propor- reproducibility and accuracy in an independent patient population is tion of patients with DSP causal variants was also higher (8.9% vs. crucial to ensure both usefulness and safety. 4.4%) than in the derivation cohort. The main findings are as follows: (1) Demonstration that the model is accurate in its predictions with Model performance an adequate discrimination and calibration in a cohort with a suf- The current validation cohort included 429 patients, of whom 103 ficient sample size.10,25 The performance of the risk calculator had events. This met the minimally recommended sample size of was indeed comparable to what was reported initially and its pre- 100 patients with and 100 patients without events to attain sufficient diction accuracy in this cohort would not be improved by power for external validation.30 The initial study and internal valid- recalibration.21 ation using bootstrapping yielded an optimism corrected (2) Demonstration that the risk calculator generally outperforms C-statistic of 0.77 (95% CI 0.73–0.81) and a calibration slope of various risk factor approaches recommended in published 0.93 (95% CI 0.92–0.95). In the current study, we obtained compar- consensus-based algorithms for ICD use in ARVC. able results with a slightly lower C-statistic of 0.70 (95% CI 0.65– 0.75) showing acceptable discrimination and a calibration slope of These findings thus support the clinical use of this risk prediction 1.01 (95% CI 0.99–1.03) demonstrating almost perfect agreement model as a valuable tool for sustained VA and SCD risk stratification between predictions and observations for sustained VA. As illu- in definite ARVC and, consequently, for guiding decisions about pri- strated in the calibration plot, this concordance between observa- mary prevention ICD indications (Structured Graphical abstract). tions and predictions was consistent across the risk spectrum External validation of the ARVC arrhythmic risk calculator 3049 Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 Figure 5 Impact of implantable cardioverter-defibrillator use threshold on clinical outcomes. The potential impact of different thresholds for im- plantable cardioverter-defibrillator use according to the model is presented on the left side and the proportion of patients who would get an im- plantable cardioverter-defibrillator according to the different consensus statements is presented on the right side. For each threshold (x-axis) the proportion of patients (y-axis) who have events (red) who do not have events (blue), who would receive an implantable cardioverter-defibrillator (solid colours) or not receive one (hashed colours) are presented. The triangles represent the number of implantable cardioverter-defibrillator needed per event prevented for each threshold (right-sided y-axis). The numerical values are presented in the table below. Implantable cardioverter- defibrillator:ventricular arrhythmia, ratio of implantable cardioverter-defibrillator placements required to protect one patient developing ventricular arrhythmia; other abbreviations as in figure 4. (Figure 2). Calibration in subgroups based on geographical origin, Clinical utility pedigree position, and genotype did not reveal major discrepancies The model generally showed a superior net clinical benefit when although the study was not adequately powered to arrive at defini- compared to a risk factor approach as recommended in the three tive conclusions in these subgroups. published consensus documents.2,18,22 The model was similarly The results of the current study are consistent with five small stud- shown to outperform the ITFC and HRS consensus in two separate ies which have addressed the external validation of the ARVC risk cohorts.5,6 These studies, however, suggested highly different thresh- calculator since its publication. The risk calculator was shown to per- olds for ICD implantation (10% and 37%), assuming an equal weight form well in patients with a definite diagnosis of ARVC5,6,8 and re- to unprotected VA and unnecessary ICDs. We did not present such gardless of their exercise status.7 The validation study by an analysis as we do not propose that these adverse events are Baudinaud et al.,6 on a cohort of 115 patients, only 15 with VA equivalent and rather preferred the use of the weighted analysis events, of whom only one had an ICD at baseline, reported a along with the graphical presentation of the clinical implications of C-statistic of 0.84 (CI 0.74–0.93) while reporting an overestimation the different threshold. The question of the threshold for ICD im- of the risk in lower risk patients. plantation is a legitimate concern when using the risk calculator. 3050 P. Jordà et al. Establishing a single perfect threshold is a delicate undertaking as recently proposed for patients with phospholamban associated dis- every cut-off point comes with a trade-off between unnecessary ease.41 Finally, additional invasive parameters such as programmed ICDs with their potential complications versus the potential for un- ventricular stimulation42,43 might add additional accuracy in protected SCA. The relative weight of these opposing undesirable intermediate-risk cases. Moreover, the model is based on prediction events varies significantly from one individual to another. In the indi- of risk from the time of diagnosis of ARVC; a time-updated model for vidualized decision-making process; however, a few points should be repeated risk prediction may have practical clinical utility. considered when reflecting on the threshold for ICD use. First, when Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 tempted to use a similar threshold as suggested by the guidelines for the hypertrophic cardiomyopathy (HCM) risk calculator (i.e. ≥6% within 5 years),31,32 the breakdown of the type of events is relevant. Limitations In ARVC cohorts, including the current study and in the derivation In this study, the majority of sustained VA outcomes are ICD treated cohort, most events were either ICD treated events or sustained events. While this fact is not possible to overcome in most modern VA, while most events in the cohort leading to the HCM risk calcu- ARVC populations and while most would agree that these still re- lator cohort were SCD or SCA.33 Although most clinicians agree present significant events, they do not directly represent the under- that sustained or ICD treated VAs represent significant events, sup- lying risk of SCD. However, this is a limitation shared with most of ported by guidelines,2,34 the exact number of treated VA events cor- previous studies in this field, including most of those used to elabor- responding to a potential SCD is unknown in ARVC. Another ate prior consensus-based risk stratification algorithms. While important aspect to consider is that none of these studies are pro- underpowered for events, calibration plots by ICD carrier status spective evaluations of the role of ICDs in SCD prevention. Such show acceptable correlation between predictions and observations. an undertaking would not be feasible in contemporary high-risk This reflects both that ICDs are implanted in patients believed to be ARVC populations. However, from such prior studies in the general at higher risk (selection bias), but also increase the detection of some cardiomyopathy population the one which established a benefit for arrhythmia that might have gone undetected otherwise (information primary prevention ICDs with the lowest annual risk of mortality, bias) (see Supplementary material online, Figure S4). While family SCD-HeFT, had an annual risk of SCD of 3.5%.35 Finally, the cost members are well represented in the derivation cohort (50.2%), of ICDs is rarely a significant determinant nowadays in countries they are less prevalent in the current cohort (35.2%), and contribute where ICDs can be considered in primary prevention.36 Factoring to a lower proportion of events (21.1%). The calibration plot in this the low number of ICDs needed to treat one VA event in ARVC, de- specific subgroup, although underpowered, suggests possible over- creases in the cost of devices, the lifespan of modern ICDs reaching estimation in the lower risk patients which should be taken in consid- 10 years, and the potential number of quality-adjusted live years eration when using the model. (QALY) saved in this young, usually otherwise healthy population Missing data also represent a limitation of this retrospective co- (only five individuals had non-arrhythmic death during follow-up in hort. Although a complete case analysis reassuringly demonstrates this cohort), the common, although debated thresholds for a similar results with regard to performance, missing data could influ- QALY between 50 000 and 100 000 USD37 remains far of reach. ence the relative benefit of the model over consensus-based Conversely, the rate of short- and long-term complications of methods. ICDs remain significant in ARVC patients (annual rate of complica- Finally, this validation only applies to patients who were well re- tions of 4.2% and of inappropriate shocks of 3.9%),38 and although presented in the derivation and validation cohorts. The model should subcutaneous-ICDs have become an appealing alternative, there is thus not be used in patients who do not meet definite ARVC diagno- no evidence of a lesser risk.39,40 sis as per 2010 TFC such as those with left dominant forms and in Thus, in light of these different considerations, we do believe that patients with rare malignant genotypes such as TMEM43-p.S358L, the best use of the risk calculator is as a shared decision making tool of which only 10 patients were included in this cohort. balancing the opposing risks of SCD and ICD use. It appears reason- able that the predicted 5-year risk threshold for recommending an ICD would range from 5% to 25%, depending on the patient’s values and preferences, and the clinician’s judgement. We acknowledge that Conclusion the threshold may change in the future with advances in non-invasive treatments and innovations in ICD technology which may lower risks In this external validation study, we demonstrated that the published associated with devices. ARVC risk prediction model not only provides accurate prognostic information in patients with ARVC without a prior history of sus- Future improvements in the model tained VA at diagnosis, but also performs generally better than other While the model demonstrated a better performance compared to published decision algorithms. These findings support its clinical use other published decision algorithms, it remains imperfect as illu- as a valuable tool for risk stratification enabling consistent and effect- strated by a C-statistic of 0.70. While it is unlikely that any risk strati- ive shared decision making for ICD implantation. fication tool for SCD could predict the totality of these events, different elements could potentially improve prediction in the future. The addition of more refined parameters indicating left ventricular Supplementary material involvement, including late gadolinium enhancement were recently suggested.9 Genotype may also improve SCD risk prediction as Supplementary material is available at European Heart Journal online. External validation of the ARVC arrhythmic risk calculator 3051 Acknowledgements novel prediction model: does the subtype really matter. Europace 2020; 22: 797–805. We thank the ARVC patients and families who have made this work 9. Aquaro GD, De Luca A, Cappelletto C, Raimondi F, Bianco F, Botto N, et al. possible. Prognostic value of magnetic resonance phenotype in patients with arrhythmogenic right ventricular cardiomyopathy. J Am Coll Cardiol 2020; 75: 2753–2765. 10. Collins GS, Ogundimu EO, Altman DG. Sample size considerations for the external validation of a multivariable prognostic model: a resampling study. Stat Med 2016;35: Funding 214–226. P.J. is supported by the Daniel Bravo Foundation grant and Spanish 11. Corrigendum to: A new prediction model for ventricular arrhythmias in arrhythmo- Downloaded from https://academic.oup.com/eurheartj/article/43/32/3041/6619318 by Fundacio Biblioteca Josep Laporte user on 09 October 2023 Society of Cardiology Magda Heras mobility grant, A.G. by the Wilton genic right ventricular cardiomyopathy. Eur Heart J 2022;43:2712. W. Webster Fellow of Heart Rhythm Society, The Johns Hopkins 12. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. ARVD/C Programme by the Leonie-Wild Foundation, Leyla Erkan Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162: Family Fund for ARVD Research, The Hugh Calkins, Marvin W1–73. H. Weiner, and Jacqueline J. Bernstein Cardiac Arrhythmia Center, Dr 13. Marcus FI, McKenna WJ, Sherrill D, Basso C, Bauce B, Bluemke DA, et al. Diagnosis of Francis P. Chiramonte Private Foundation, Dr Satish, Rupal, and Robin arrhythmogenic right ventricular cardiomyopathy/dysplasia: proposed modification Shah ARVD Fund at Johns Hopkins, Bogle Foundation, Campanella of the Task Force Criteria. Eur Heart J 2010; 31: 806–814. Family, Patrick J. Harrison Family, Peter French Memorial Foundation, 14. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guide- lines for the interpretation of sequence variants: a joint consensus recommendation and Wilmerding Endowments, H.K.J. by grants from Novo Nordisk of the American College of Medical Genetics and Genomics and the Association for Foundation, Denmark (NNF18OC0031258 and NNF20OC0065151), Molecular Pathology. Genet Med 2015; 17: 405–424. S.A.L. by NIH grant 1R01HL139731 and American Heart Association 15. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pres- 18SFRN34250007, R.T. by the Canada Research Chairs programme, sure covariates in survival analysis. Stat Med 1999;18:681–694. J.C.T. by the Philippa and Marvin Carsley Cardiology Research Chair. 16. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med 2011;30:377–399. S.G.P. receives support from Ricerca Corrente funding scheme of the 17. Rubin DB. Multiple Imputation for Nonresponse in Surveys. 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