Arrhythmic risk prediction in arrhythmogenic
right ventricular cardiomyopathy: external
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*
1Cardiovascular 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;
12Department 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;
15Population 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
European Heart Journal (2022) 43, 3041–3052
https://doi.org/10.1093/eurheartj/ehac289
CLINICAL RESEARCH
Arrhythmias
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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
and to compare its performance with the risk factor approach recommended for implantable cardioverter-defibrillator
(ICD) use by published guidelines and expert consensus.
Methods
and results
In a retrospective cohort of 429 individuals from 29 centres in North America and Europe, 103 (24%) experienced sus-
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.
3042 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
Keywords Arrhythmogenic right ventricular cardiomyopathy • Implantable cardioverter-defibrillator • Sudden cardiac death •
Ventricular arrhythmias • Risk stratification • Genetic cardiomyopathies
Introduction
Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a sig-
nificant cause of sustained ventricular arrhythmia (VA) and sudden
cardiac death (SCD), especially in young individuals and athletes.
Preventing this catastrophic outcome through the prophylactic use
of implantable cardioverter-defibrillators (ICDs) is a cornerstone
of the disease management. Given the significant drawbacks asso-
ciated with ICDs in this young and active population, appropriate pa-
tient selection is essential.
Over the past 25 years, numerous studies have identified predic-
tors of sustained VA and SCD in ARVC and consensus documents
have integrated these in decision algorithms for ICD use.1–3
Building on this knowledge, a risk prediction model for sustained
VA and SCD in ARVC was recently developed in a multinational co-
hort (n= 528, designed as the derivation cohort) mostly including
high volume referral centres for ARVC.4 This prediction model pro-
vides individualized prediction of the risk of VA in patients with
ARVC without a prior history of sustained VA. Since its online pub-
lication, the risk calculator’s official site (http://www.ARVCrisk.com)
has been used20 000 times illustrating its uptake in clinical practice.
The model has been internally and externally validated in small
studies.4–9 However, adequately powered external validation is still
lacking,10 yet is paramount to confirm the reproducibility, generaliz-
ability, and need to update the model in an independent population.
The aims of the present study are thus (i) to conduct external val-
idation of the published risk calculator in a distinct, adequately pow-
ered, and geographically diverse cohort including patients from six
countries across North America and Europe and (ii) to compare
the performance of the risk prediction model with other published
guidelines and expert consensus recommendations for ICD use.
During the current validation study, our group detected an inaccur-
acy in the formula of the original ARVC risk calculator published in
2019. It was corrected both on the website (ARVCrisk.com) and
in the published manuscript.11 We base the present study on the
corrected risk calculator.
Methods
Study design
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)
statement.12
Study population
The study population was derived from 29 centres (see Supplementary
material online, Table S1) in six European and North American countries.
This current cohort will be designated as the ‘validation cohort’while the
cohort leading to the published model will be designated as the ‘deriv-
ation cohort’. New patients from two centres participating in the original
study (Montreal Heart Institute and Johns Hopkins Hospital) were in-
cluded (52 patients; 12% of the cohort). No patients in the current co-
hort were included in the original ARVC derivation cohort. From each
site, consistent with the derivation cohort, consecutive patients who
(i) were diagnosed with definite ARVC as per 2010 Task Force
Criteria (TFC),13 (ii) were alive at presentation, and (iii) had not experi-
enced spontaneous sustained VA or sudden cardiac arrest (SCA) at diag-
nosis were included. The study conforms to the Helsinki declaration and
was approved by local ethics and/or institutional review boards. Tomain-
tain patient confidentiality, data, and study materials will not be made
available to other researchers for purposes of replicating the results. A
limited dataset may be made available upon request.
Data collection
Data were collected independently by each of the participating centres
using uniform definitions. A complete list of variables and their definitions
can be found in Supplementary material online, Table S2. Genetic var-
iants were reviewed according to the American College of Medical
Genetics and Genomics guidelines by cardiologists specialized in
cardiovascular genetics (R.T. and J.C.T.).14
Missing data
Patients with .50% of predictors missing were excluded from the ana-
lysis. Missingness was assumed to be at random and imputed using mul-
tiple imputation by chained equations.15 Missing quantitative values for
right ventricular ejection fraction (RVEF) and left ventricular ejection
fraction (LVEF) were imputed manually when only qualitative assessment
was available as done previously4 and detailed in Supplementary material
online, Table S2. The multiple imputation model included all pre-specified
predictors, proband status and genotype together with the outcome,
and cumulative baseline hazard estimation.16 A total of 25 imputed data-
sets were generated, and the final inference estimations were combined
using Rubin’s rules.17
Study outcomes
In accordance with the published ARVC risk prediction model which this
study aims to validate, the primary outcome was the first sustained VA
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-
dynamic compromise requiring cardioversion), ventricular fibrillation/
flutter, or appropriate ICD intervention. Heart transplantation, cardio-
vascular mortality, and all-cause mortality were also collected.
Predictor variables and risk calculator
The same candidate predictors as those selected in the published model
based on prior literature were considered.18–20 These include sex, age,
recent cardiac syncope (here defined as transient loss of consciousness
and postural tone with spontaneous recovery with a likely arrhythmic
mechanism, within a year of diagnosis), non-sustained VT (NSVT: defined
as hemodynamically stable VT at≥100 b.p.m., for≥3 beats,30 s), num-
ber of premature ventricular complexes (PVCs) on 24-h Holter moni-
toring, the extent of T-wave inversion (TWI) on anterior and inferior
leads, and RVEF. Each predictor variable was determined at the time of
External validation of the ARVC arrhythmic risk calculator 3043
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
diagnosis, defined as 1 year before to 1 year after the date of diagnosis as
per 2010 TFC and prior to the occurrence of the primary outcome.
The 5-year risk of sustained VA for an individual patient as per the pub-
lished model is calculated using the following equation4:
P(VA at 5 years) = 1− 0.8396exp(LP)
where the linear predictor (LP) was calculated according to the equation:
LP = 0.488×male sex− 0.022× age+ 0.657
× history of recent cardiac syncope+ 0.811× history of NSVT
+ 0.170× ln(24-h PVC count)+ 0.113
× sum of anterior and inferior leads with TWI− 0.025
× RVEF.
Of note, the baseline hazard for 5-year prediction (0.8396) has been cor-
rected since the initial publication in 2019.11
Statistical analysis
Analyses were performed with RStudio version 1.3.1093 (Boston, MA,
USA). Continuous variables were expressed as mean+ standard devi-
ation or median [interquartile range (IQR)] and compared using either
the independent sample t-test or the Mann–Whitney U test.
Categorical variables were presented as frequencies (%) and compared
using the Fisher’s exact test. Follow-up duration was calculated as the
time interval between the time of definite diagnosis according to TFC
and the endpoint or censoring. Censoring was defined as death from
any other cause, heart transplantation or the most recent follow-up visit
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.
Model validation
The approach to external validation follows the method suggested by
Royston and Altman for Cox prognostic models.21 First, the overall dis-
criminative performance of the model was measured using Harrell’s
C-statistic, and the model fit by calculating the calibration slope, the re-
gression of the LP (i.e. the product of the variable part of the Cox model)
in the current cohort (validation cohort). Graphical evaluation of calibra-
tion was performed by plotting the predicted risk against the observed
risk of sustained VA, using grouped Kaplan–Meier estimates and the con-
tinuous hazard regression function. The choice of the number of groups
presented was based on the balance between providing sufficient spread
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.
Subsequently, a more in-depth analysis of the model fit was performed
by a Cox’s model including the same predictor variables in combination
with the LP of the original model (as an offset variable) to evaluate po-
tential differences in the regression coefficients of each individual predict-
or. The result indicating the validity of the model would be that if all
coefficients ß* equalled 0, reflecting that all the variability in the validation
sample is accounted for by the published model. In addition, the baseline
survival function of the validation dataset was compared to that of the
derivation dataset to see if the overall predictions need to be globally
shifted upward or downward. Lastly, a new prediction model using the
same predictor variables was fitted to the validation dataset and com-
pared to the fit of the original model using the Akaike information criter-
ion (AIC), with a difference of .2 defined as statistically significant. This
allows testing whether a model specifically fitted to the validation dataset
performs better than the original model in the validation dataset.
Subgroup analyses
We visually explored the performance of the model specifically in differ-
ent populations of interest by comparing calibration plots for these sub-
groups. We stratified the cohort by geographic origin (Europe vs. North
America), by proband status and by plakophilin 2 carrier status (PKP2;
causal variant carrier vs. non-carrier). We did not report quantitative
markers of performance such as the C-statistic as this study was not
powered adequately for these subgroups.
To assess the impact of carrying an ICD on prediction accuracy, we
also presented calibration plots based on ICD carrier status at baseline
defined as ICD implantation prior to a year following diagnosis and first
VA outcome, whichever came first.
Clinical utility
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
Consensus for the treatment of ARVC (ITFC),18 the 2017 American
Heart Association (AHA) guidelines for the management of VA and pre-
vention of SCD,2 and the 2019 Heart Rhythm Society (HRS) consensus
on arrhythmogenic cardiomyopathy (excluding programmed ventricular
stimulation)22 through decision curve analysis. In a decision curve ana-
lysis,23 the clinical benefit is assessed by the ‘net benefit’ representing
the balance between useful (i.e. in patients with events) vs. useless (i.e.
in patients without events) ICD placement at 5 years weighted according
to the threshold used for ICD implantation. More specifically, the deci-
sion curve uses the following formula:
True positives/total sample size
− false positives/total sample size× (pt/1− pt)
where ‘pt’ represents threshold probability, in the current case,
threshold for ICD implantation. Therefore, the higher the threshold
used, the greater the harm of useless ICD use (i.e. false positive) is va-
lued. Higher values indicate greater benefit while a value of 0 indicates
no benefit.
To present the consequence of setting different thresholds for ICD
implantation, we evaluated and plotted the proportion of patients who
would receive ICDs and the proportion of treated and missed events
at each threshold. We compared these with the recommendations for
ICD use by the three published consensus mentioned above [ITFC(1),
AHA (2), HRS(3)].
Results
The study population included 429 definite ARVC patients without a
history of sustained VA or SCA at the time of diagnosis aged 43.1+
15.8 years and slightly more than half (n= 235, 54.8%) were male.
Probands accounted for two-thirds of the cohort (n= 278,
64.8%). Half (n= 198, 46.6%) of patients had a pathogenic or likely
pathogenic variant in a gene with definite or moderate association
with ARVC,24 which represents 70% (198 patients) of the 282 pa-
tients for whom the complete genetic information was available.
PKP2 was the most common genotype, carried by 111 patients
(26%) followed by DSP in 38 patients (9%). Compared to PKP2 pa-
tients, DSP patients were more likely to have a decrease in LVEF,
3044 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
50% (44.7% vs. 6.4%) but less likely to have VA events at follow-up
(13.2% vs. 24.5%). Baseline characteristics according to genotype
are presented in Supplementary material online, Table S3. Other clin-
ical and demographic characteristics are summarized in Table 1.
Baseline characteristics by country of origin are presented in
Supplementary material online, Table S4, and a comparison of the
derivation and validation cohort populations is presented in
Supplementary material online, Table S5.
Overall, 299 (70.0%) patients had complete data for the pre-
specified predictors. Six of the eight predictors had missing data:
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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
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.
External validation of the ARVC arrhythmic risk calculator 3045
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
recent cardiac syncope (n= 5, 1.17%), NSVT (70= 16.32%), PVC
count (n= 105, 24.48%), extent of leads with TWI (n= 26, 6.06%)
and RVEF (n= 19, 4.43%). From an initial cohort of 433 patients,
four patients were excluded as.50% of their predictors were miss-
ing (four predictors or more).
Outcomes
During a median follow-up of [5.02 (2.05–7.90)] years, 103 patients
(24%) experienced sustained VA events corresponding to an annual
event rate of 4.98% [95% confidence interval (CI) 4.07–6.04]. Figure 1
shows the cumulative survival free from first sustained VA.
Among patients who experienced sustained VA during follow-up,
the most common events were ICD treated VAs, which represented
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
or ICD treated VT events, the median cycle length (available in 57/93
events) was 280 ms (IQR: 246–315) which corresponds to
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-
lues to improve prediction in this cohort.
Second, the baseline survival function (i.e. predictors-adjusted sur-
vival) was assessed through the comparison of the baseline survival
Figure 1 Survival free from sustained ventricular arrhythmia at
follow-up. The cumulative event-free survival for any ventricular ar-
rhythmia with 95% confidence intervals (shaded area) is plotted.
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
O
bs
er
ve
d 
1–
ye
a
r 
ris
k
Total (n) = 429
Events = 103
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 5–year risk
O
bs
er
ve
d 
5–
ye
a
r 
ris
k
Total (n) = 429
Events = 103
Avg. group size = 86
A B
Figure 2Calibration 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.
3046 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
probabilities (i.e. predictors-adjusted survival) in the derivation and
the validation cohorts at different time points showing similar ex-
pected survival curves as shown numerically and visually in
Figure 3B and C. These findings suggest that the survival function
does not need to be modified to improve prediction in this cohort.
Finally, the potential need to update the model was assessed by
comparing the fit of the published model with the derivation of a
new model in the validation cohort. The AIC of the published model
in the current cohort (1059.14) and of a model derived in this cohort
(1060.93) were not significantly different (absolute difference in AIC
of 1.79) indicating the absence of significant improvement in predic-
tions when fitting a model to this population.
As a sensitivity analysis, we repeated the process in patients with
complete data (n= 299) resulting in a similar C-statistic, calibration
slope, baseline risk, and calibration plot (see Supplementary
material online, Figure S3).
Clinical utility
We compared the performance of the risk calculator with published
consensus-based decision algorithms for ICD use in ARVC. As illu-
strated in Figure 4, the risk calculator generally had a superior net clin-
ical benefit when compared to the other published algorithms for
ICD use. Its performance becomes similar to the HRS consensus
above a risk of 35%.
Finally, we graphically presented the impact of different threshold
for ICD implantation on the proportion of ICD use and the protec-
tion rate and compared to the published decision algorithms
(Figure 5). Higher thresholds result in less ICD use but less protection
from VA. As an example, a threshold of 15% would results in im-
planting 59.4% of patients with ICDs while protecting 85.7% of pa-
tients with incident VA events.
Subgroups analyses
The performance in subgroups of interest was visually explored by
calibration plots presented in Supplementary material online,
Figure S4. This cohort was not sufficiently powered to provide defin-
ite answers in these subgroups.
Calibration appeared acceptable in patients from both Europe and
North America, although this analysis had low precision in the North
American population due to its smaller size.
The model performed well both in probands and family members
with a possible trend toward overestimation in family members in
the lower risk spectrum. The calibration was also visually acceptable
both in PKP2 carriers and non-carriers.
Calibration plots according to the presence of an ICD show an ac-
ceptable agreement between predictions and observations with a
tendency towards overestimation in non-ICD carriers and under-
estimation in ICD carriers in the higher risk spectrum (see
Supplementary material online, Figure S5).
Discussion
In this study, we validated the published ARVC risk calculator in an
independent cohort of patients from 29 centres in 6 countries in
North America and Europe. Since its publication in 2019, the risk cal-
culator had a significant uptake in clinical practice. Ensuring its
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.
External validation of the ARVC arrhythmic risk calculator 3047
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
reproducibility and accuracy in an independent patient population is
crucial to ensure both usefulness and safety.
The main findings are as follows:
(1) Demonstration that the model is accurate in its predictions with
an adequate discrimination and calibration in a cohort with a suf-
ficient sample size.10,25 The performance of the risk calculator
was indeed comparable to what was reported initially and its pre-
diction accuracy in this cohort would not be improved by
recalibration.21
(2) Demonstration that the risk calculator generally outperforms
various risk factor approaches recommended in published
consensus-based algorithms for ICD use in ARVC.
These findings thus support the clinical use of this risk prediction
model as a valuable tool for sustained VA and SCD risk stratification
in definite ARVC and, consequently, for guiding decisions about pri-
mary prevention ICD indications (Structured Graphical abstract).
Comparison of the internal and external
validation populations
While based on the same inclusion criteria (i.e. definite diagnosis of
ARVC and no prior history of sustained VA at the time of diagno-
sis), the initial risk calculator included a high proportion of patients
treated at highly specialized ARVC referral centres. Thus, a signifi-
cant concern regarding this population is a possible selection bias
due to the preferential referral of patients for adverse disease pro-
gression (i.e. recurrent VA referred for ablation and severe heart
failure for advanced therapies). This could potentially hamper ex-
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-
hort (4.98%, 95% CI 4.07–6.04) was slightly lower, although non-
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
the validation versus 4.83 (2.44–9.33) years in the derivation co-
hort]. This reflects the overall high risk of VA events in definite
ARVC patients such as those included in this study which is consist-
ent with prior literature and often preceding structural
changes.4,19,25–28
Some differences between the two cohorts (shown in
Supplementary material online, Table S4) might have limited the
potential discrepancy in event rates, such as a higher proportion of
probands (64.8% vs. 49.8%, P, 0.001) and males (54.8% vs. 44.7%,
P= 0.002) in the current cohort. Conversely, patients in the present
cohort were slightly older (43.1 vs. 38.2 years of age, P, 0.001), had
less recent cardiac syncope and NSVT. The proportion of patients
with decreased LVEF (,50%) was also higher in this cohort (17.7
vs. 12.7, P= 0.002). Although individuals in the current population
were more likely to receive anti-arrhythmic drugs (P, 0.001) and
β-blockers (48.0 vs. 37.9, P= 0.001), the proportion of ICD carriers
at baseline was similar (41.1 vs. 40.8 P= 0.98). Finally, while still re-
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-
tion of patients with DSP causal variants was also higher (8.9% vs.
4.4%) than in the derivation cohort.
Model performance
The current validation cohort included 429 patients, of whom 103
had events. This met the minimally recommended sample size of
100 patients with and 100 patients without events to attain sufficient
power for external validation.30 The initial study and internal valid-
ation using bootstrapping yielded an optimism corrected
C-statistic of 0.77 (95% CI 0.73–0.81) and a calibration slope of
0.93 (95% CI 0.92–0.95). In the current study, we obtained compar-
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
1.01 (95% CI 0.99–1.03) demonstrating almost perfect agreement
between predictions and observations for sustained VA. As illu-
strated in the calibration plot, this concordance between observa-
tions and predictions was consistent across the risk spectrum
0.0 0.1 0.2 0.3 0.4 0.5
–
0.
05
0.
00
0.
05
0.
10
0.
15
0.
20
Threshold probability
N
et
 b
en
ef
it
None
All
Risk calc
ITFC
AHA
HRS
Figure 4 Decision curve analysis comparing the clinical utility of our
model (dashed thick black line) with the 2015 International Task Force
Consensus Statement algorithm for the treatment of arrhythmogenic
right ventricular cardiomyopathy (dashed red line), the2017American
Heart Association algorithm for the management of ventricular ar-
rhythmia and prevention of sudden cardiac death (dashed green
line) and the 2019 Heart Rhythm Society consensus on arrhythmo-
genic cardiomyopathy with exclusion of the Programmed ventricular
stimulation (dashed blue line). The clinical utility of each treatment
strategy is compared by plotting the net benefit (y-axis) for a range
of possible implantable cardioverter-defibrillator placement thresh-
olds based on the 5-year risk of ventricular arrhythmia (x-axis).
Higher net benefit values indicate greater benefit while a value of 0 in-
dicates no benefit. The published risk calculator depicted a better net
benefit than the other published algorithms for implantable
cardioverter-defibrillator implantation thresholds below a 35%.
Above this threshold its performance was similar to the Heart
Rhythm Society consensus algorithm. ICD, implantable cardioverter-
defibrillator;ARVC, arrhythmogenic right ventricular cardiomyopathy,
VA ventricular arrhythmia, SCD sudden cardiac death.
3048 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
(Figure 2). Calibration in subgroups based on geographical origin,
pedigree position, and genotype did not reveal major discrepancies
although the study was not adequately powered to arrive at defini-
tive conclusions in these subgroups.
The results of the current study are consistent with five small stud-
ies which have addressed the external validation of the ARVC risk
calculator since its publication. The risk calculator was shown to per-
form well in patients with a definite diagnosis of ARVC5,6,8 and re-
gardless of their exercise status.7 The validation study by
Baudinaud et al.,6 on a cohort of 115 patients, only 15 with VA
events, of whom only one had an ICD at baseline, reported a
C-statistic of 0.84 (CI 0.74–0.93) while reporting an overestimation
of the risk in lower risk patients.
Clinical utility
The model generally showed a superior net clinical benefit when
compared to a risk factor approach as recommended in the three
published consensus documents.2,18,22 The model was similarly
shown to outperform the ITFC and HRS consensus in two separate
cohorts.5,6 These studies, however, suggested highly different thresh-
olds for ICD implantation (10% and 37%), assuming an equal weight
to unprotected VA and unnecessary ICDs. We did not present such
an analysis as we do not propose that these adverse events are
equivalent and rather preferred the use of the weighted analysis
along with the graphical presentation of the clinical implications of
the different threshold. The question of the threshold for ICD im-
plantation is a legitimate concern when using the risk calculator.
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.
External validation of the ARVC arrhythmic risk calculator 3049
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
Establishing a single perfect threshold is a delicate undertaking as
every cut-off point comes with a trade-off between unnecessary
ICDs with their potential complications versus the potential for un-
protected SCA. The relative weight of these opposing undesirable
events varies significantly from one individual to another. In the indi-
vidualized decision-making process; however, a few points should be
considered when reflecting on the threshold for ICD use. First, when
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.
In ARVC cohorts, including the current study and in the derivation
cohort, most events were either ICD treated events or sustained
VA, while most events in the cohort leading to the HCM risk calcu-
lator cohort were SCD or SCA.33 Although most clinicians agree
that sustained or ICD treated VAs represent significant events, sup-
ported by guidelines,2,34 the exact number of treated VA events cor-
responding to a potential SCD is unknown in ARVC. Another
important aspect to consider is that none of these studies are pro-
spective evaluations of the role of ICDs in SCD prevention. Such
an undertaking would not be feasible in contemporary high-risk
ARVC populations. However, from such prior studies in the general
cardiomyopathy population the one which established a benefit for
primary prevention ICDs with the lowest annual risk of mortality,
SCD-HeFT, had an annual risk of SCD of 3.5%.35 Finally, the cost
of ICDs is rarely a significant determinant nowadays in countries
where ICDs can be considered in primary prevention.36 Factoring
the low number of ICDs needed to treat one VA event in ARVC, de-
creases in the cost of devices, the lifespan of modern ICDs reaching
10 years, and the potential number of quality-adjusted live years
(QALY) saved in this young, usually otherwise healthy population
(only five individuals had non-arrhythmic death during follow-up in
this cohort), the common, although debated thresholds for a
QALY between 50 000 and 100 000 USD37 remains far of reach.
Conversely, the rate of short- and long-term complications of
ICDs remain significant in ARVC patients (annual rate of complica-
tions of 4.2% and of inappropriate shocks of 3.9%),38 and although
subcutaneous-ICDs have become an appealing alternative, there is
no evidence of a lesser risk.39,40
Thus, in light of these different considerations, we do believe that
the best use of the risk calculator is as a shared decision making tool
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
the threshold may change in the future with advances in non-invasive
treatments and innovations in ICD technology which may lower risks
associated with devices.
Future improvements in the model
While the model demonstrated a better performance compared to
other published decision algorithms, it remains imperfect as illu-
strated by a C-statistic of 0.70. While it is unlikely that any risk strati-
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
involvement, including late gadolinium enhancement were recently
suggested.9 Genotype may also improve SCD risk prediction as
recently proposed for patients with phospholamban associated dis-
ease.41 Finally, additional invasive parameters such as programmed
ventricular stimulation42,43 might add additional accuracy in
intermediate-risk cases. Moreover, the model is based on prediction
of risk from the time of diagnosis of ARVC; a time-updated model for
repeated risk prediction may have practical clinical utility.
Limitations
In this study, the majority of sustained VA outcomes are ICD treated
events. While this fact is not possible to overcome in most modern
ARVC populations and while most would agree that these still re-
present significant events, they do not directly represent the under-
lying risk of SCD. However, this is a limitation shared with most of
previous studies in this field, including most of those used to elabor-
ate prior consensus-based risk stratification algorithms. While
underpowered for events, calibration plots by ICD carrier status
show acceptable correlation between predictions and observations.
This reflects both that ICDs are implanted in patients believed to be
at higher risk (selection bias), but also increase the detection of some
arrhythmia that might have gone undetected otherwise (information
bias) (see Supplementary material online, Figure S4). While family
members are well represented in the derivation cohort (50.2%),
they are less prevalent in the current cohort (35.2%), and contribute
to a lower proportion of events (21.1%). The calibration plot in this
specific subgroup, although underpowered, suggests possible over-
estimation in the lower risk patients which should be taken in consid-
eration when using the model.
Missing data also represent a limitation of this retrospective co-
hort. Although a complete case analysis reassuringly demonstrates
similar results with regard to performance, missing data could influ-
ence the relative benefit of the model over consensus-based
methods.
Finally, this validation only applies to patients who were well re-
presented in the derivation and validation cohorts. The model should
thus not be used in patients who do not meet definite ARVC diagno-
sis as per 2010 TFC such as those with left dominant forms and in
patients with rare malignant genotypes such as TMEM43-p.S358L,
of which only 10 patients were included in this cohort.
Conclusion
In this external validation study, we demonstrated that the published
ARVC risk prediction model not only provides accurate prognostic
information in patients with ARVC without a prior history of sus-
tained VA at diagnosis, but also performs generally better than other
published decision algorithms. These findings support its clinical use
as a valuable tool for risk stratification enabling consistent and effect-
ive shared decision making for ICD implantation.
Supplementary material
Supplementary material is available at European Heart Journal online.
3050 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
Acknowledgements
We thank the ARVC patients and families who have made this work
possible.
Funding
P.J. is supported by the Daniel Bravo Foundation grant and Spanish
Society of Cardiology Magda Heras mobility grant, A.G. by the Wilton
W. Webster Fellow of Heart Rhythm Society, The Johns Hopkins
ARVD/C Programme by the Leonie-Wild Foundation, Leyla Erkan
Family Fund for ARVD Research, The Hugh Calkins, Marvin
H. Weiner, and Jacqueline J. Bernstein Cardiac Arrhythmia Center, Dr
Francis P. Chiramonte Private Foundation, Dr Satish, Rupal, and Robin
Shah ARVD Fund at Johns Hopkins, Bogle Foundation, Campanella
Family, Patrick J. Harrison Family, Peter French Memorial Foundation,
and Wilmerding Endowments, H.K.J. by grants from Novo Nordisk
Foundation, Denmark (NNF18OC0031258 and NNF20OC0065151),
S.A.L. by NIH grant 1R01HL139731 and American Heart Association
18SFRN34250007, R.T. by the Canada Research Chairs programme,
J.C.T. by the Philippa and Marvin Carsley Cardiology Research Chair.
S.G.P. receives support from Ricerca Corrente funding scheme of the
Italian Ministry of Health and Italian Ministry of Research and
University Dipartimenti di Eccellenza 2018 to 2022 grant to the
Molecular Medicine Department (University of Pavia).
Conflicts of interest: C.M.: honoraria from Abbott, I.O.: grants, con-
sulting fees or honoraria from BSM, Cytokinetics, Shire, Genzyme,
Amicus, Menarini International, Boston Scientific, and Tenaya, S.A.L.:
grants from BMS/Pfizer, Boehringer Ingelheim, fitbit, IBM, and consulting
fees from BMS/Pfizer, Invitae, and Blackstone, J.S.H.: research grants from
Boston Scientific, Abbott, and Medtronic and is on Scientific Advisory
Board for Boston Scientific, H.K.J.: grant from Novo Nordisk foundation
and honoraria from Abbott and Biosense Webster, H.C.: consultant for
Medtronic Inc., Biosense Webster, Pfizer, and Abbott. He receives re-
search support from Boston Scientific Corp. C.A.J. receives salary sup-
port from this grant and consulting fees from Pfizer, J. C.-T. consulting
fees from BMS/Pfizer and Bayer.
References
1. Corrado D, Wichter T, Link MS, Hauer RN, Marchlinski FE, Anastasakis A et al.
Treatment of arrhythmogenic right ventricular cardiomyopathy/dysplasia: an inter-
national task force consensus statement. Circulation 2015; 132: 441–453.
2. Al-Khatib SM, StevensonWG, Ackerman MJ, Bryant WJ, Callans DJ, Curtis AB et al.
2017 AHA/ACC/HRS guideline for management of patients with ventricular ar-
rhythmias and the prevention of sudden cardiac death: a report of the American
College of Cardiology/American Heart Association Task Force on Clinical Practice
Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2018; 72: e91–e220.
3. Towbin JA, McKenna WJ, Abrams DJ, Ackerman MJ, Calkins H, Darrieux FCC et al.
2019 HRS expert consensus statement on evaluation, risk stratification, andmanage-
ment of arrhythmogenic cardiomyopathy. Heart Rhythm 2019; 16: e301–e372.
4. Cadrin-Tourigny J, Bosman LP, Nozza A,WangW, Tadros R, Bhonsale A et al.A new
predictionmodel for ventricular arrhythmias in arrhythmogenic right ventricular car-
diomyopathy. Eur Heart J 2019; 40: 1850–1858.
5. Aquaro GD, De Luca A, Cappelletto C, Raimondi F, Bianco F, Botto N, et al.
Comparison of different prediction models for the indication of implanted cardio-
verter defibrillator in patients with arrhythmogenic right ventricular cardiomyop-
athy. ESC Heart Fail 2020; 7: 4080–4088.
6. Baudinaud P, Laredo M, Badenco N, Rouanet S, Waintraub X, Duthoit G, et al.
External validation of a risk prediction model for ventricular arrhythmias in arrhyth-
mogenic right ventricular cardiomyopathy. Can J Cardiol 2021; 37: 1263–1266.
7. Gasperetti A, Dello Russo A, Busana M, Dessanai M, Pizzamiglio F, Saguner AM, et al.
Novel risk calculator performance in athletes with arrhythmogenic right ventricular
cardiomyopathy. Heart Rhythm 2020; 17: 1251–1259.
8. Casella M, Gasperetti A, Gaetano F, Busana M, Sommariva E, Catto V, et al.
Long-term follow-up analysis of a highly characterized arrhythmogenic cardiomyop-
athy cohort with classical and non-classical phenotypes-a real-world assessment of a
novel prediction model: does the subtype really matter. Europace 2020; 22:
797–805.
9. Aquaro GD, De Luca A, Cappelletto C, Raimondi F, Bianco F, Botto N, et al.
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:
214–226.
11. Corrigendum to: A new prediction model for ventricular arrhythmias in arrhythmo-
genic right ventricular cardiomyopathy. Eur Heart J 2022;43:2712.
12. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al.
Transparent Reporting of a multivariable prediction model for Individual Prognosis
or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015; 162:
W1–73.
13. Marcus FI, McKennaWJ, Sherrill D, Basso C, Bauce B, BluemkeDA, et al.Diagnosis of
arrhythmogenic right ventricular cardiomyopathy/dysplasia: proposed modification
of the Task Force Criteria. Eur Heart J 2010; 31: 806–814.
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
of the American College of Medical Genetics and Genomics and the Association for
Molecular Pathology. Genet Med 2015; 17: 405–424.
15. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pres-
sure covariates in survival analysis. Stat Med 1999;18:681–694.
16. White IR, Royston P,Wood AM. Multiple imputation using chained equations: issues
and guidance for practice. Stat Med 2011;30:377–399.
17. Rubin DB.Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and
Sons, 1987.
18. Corrado D, Wichter T, Link MS, Hauer R, Marchlinski F, Anastasakis A, et al.
Treatment of arrhythmogenic right ventricular cardiomyopathy/dysplasia: an inter-
national task force consensus statement. Eur Heart J 2015; 36: 3227–3237.
19. Bosman LP, Sammani A, James CA, Cadrin-Tourigny J, Calkins H, van Tintelen JP,
et al. Predicting arrhythmic risk in arrhythmogenic right ventricular cardiomyopathy:
a systematic review and meta-analysis. Heart Rhythm 2018; 15: 1097–1107.
20. Orgeron GM, Te Riele A, Tichnell C, Wang W, Murray B, Bhonsale A, et al.
Performance of the 2015 International Task Force Consensus Statement risk strati-
fication algorithm for implantable cardioverter-defibrillator placement in arrhyth-
mogenic right ventricular dysplasia/cardiomyopathy. Circ Arrhythm Electrophysiol
2018; 11: e005593.
21. Royston P, Altman DG. External validation of a cox prognostic model: principles and
methods. BMC Med Res Methodol 2013; 13: 33.
22. Towbin JA, McKenna WJ, Abrams DJ, Ackerman MJ, Calkins H, Darrieux FCC, et al.
2019 HRS expert consensus statement on evaluation, risk stratification, andmanage-
ment of arrhythmogenic cardiomyopathy. Heart Rhythm 2019; 16: e301–e372.
23. Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpret-
ing decision curve analysis. Diagn Progn Res 2019; 3: 18.
24. James CA, Jongbloed JDH, Hershberger RE, Morales A, Judge DP, Syrris P, et al.
International evidence based reappraisal of genes associated with arrhythmogenic
right ventricular cardiomyopathy using the clinical genome resource framework.
Circ Genom Precis Med 2021; 14: e003273.
25. Bhonsale A, James CA, Tichnell C, Murray B, Gagarin D, Philips B, et al. Incidence and
predictors of implantable cardioverter-defibrillator therapy in patients with arrhyth-
mogenic right ventricular dysplasia/cardiomyopathy undergoing implantable
cardioverter-defibrillator implantation for primary prevention. J Am Coll Cardiol
2011; 58: 1485–1496.
26. Mazzanti A, Ng K, Faragli A, Maragna R, Chiodaroli E, Orphanou N, et al.
Arrhythmogenic right ventricular cardiomyopathy: clinical course and predictors
of arrhythmic risk. J Am Coll Cardiol 2016; 68: 2540–2550.
27. Battipaglia I, Scalone G, Macchione A, Pinnacchio G, Laurito M, Milo M, et al.
Association of heart rate variability with arrhythmic events in patients with arrhyth-
mogenic right ventricular cardiomyopathy/dysplasia. Circ J 2012; 76: 618–623.
28. Santangeli P, Dello Russo A, Pieroni M, Casella M, Di Biase L, Burkhardt JD, et al.
Fragmented and delayed electrograms within fibrofatty scar predict arrhythmic
events in arrhythmogenic right ventricular cardiomyopathy: results from a prospect-
ive risk stratification study. Heart Rhythm 2012; 9: 1200–1206.
29. Groeneweg JA, Bhonsale A, James CA, te Riele AS, Dooijes D, Tichnell C, et al.
Clinical presentation, long-term follow-up, and outcomes of 1001 arrhythmogenic
right ventricular dysplasia/cardiomyopathy patients and family members. Circ
Cardiovasc Genet 2015; 8: 437–446.
30. Vergouwe Y, Steyerberg EW, Eijkemans MJ et al. Substantial effective sample sizes
were required for external validation studies of predictive logistic regression models.
J Clin Epidemiol 2005; 58: 475–483.
31. Authors/Task Force m, Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F,
et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomy-
opathy: the Task Force for the Diagnosis and Management of Hypertrophic
External validation of the ARVC arrhythmic risk calculator 3051
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023
Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J 2014; 35:
2733–2779.
32. O’Mahony C, Jichi F, Pavlou M, Monserrat L, Anastasakis A, Rapezzi C, et al. A novel
clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyop-
athy (HCM risk-SCD). Eur Heart J 2014; 35: 2010–2020.
33. Cadrin-Tourigny J, Tadros R. Predicting sudden cardiac death in genetic heart dis-
ease. Can J Cardiol 2022;38:479–490.
34. Priori SG, Blomstrom-Lundqvist C. European Society of Cardiology Guidelines for
the management of patients with ventricular arrhythmias and the prevention of sud-
den cardiac death summarized by co-chairs. Eur Heart J 2015: 36(41):2757–2759.
35. Poole JE, Olshansky B, Mark DB, Anderson J, Johnson G, Hellkamp AS, et al.
Long-term outcomes of implantable cardioverter-defibrillator therapy in the
SCD-HeFT. J Am Coll Cardiol 2020; 76: 405–415.
36. Smith T, Jordaens L, Theuns DA, van Dessel PF, Wilde AA, Hunink MG. The cost-
effectiveness of primary prophylactic implantable defibrillator therapy in patients
with ischaemic or non-ischaemic heart disease: a European analysis. Eur Heart J
2013; 34: 211–219.
37. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness–the curious re-
silience of the $50,000-per-QALY threshold. N Engl J Med 2014;371:796–797.
38. Nordkamp LR O, Postema PG, Knops RE, van Dijk N, Limpens J, Wilde AA, et al.
Implantable cardioverter-defibrillator harm in young patients with inherited
arrhythmia syndromes: a systematic review and meta-analysis of inappropriate
shocks and complications. Heart Rhythm 2016;13:443–454.
39. Knops RE, Olde Nordkamp LRA, Delnoy PHM, Boersma LVA, Kuschyk J, El-Chami
MF, et al. Subcutaneous or transvenous defibrillator therapy.N Engl J Med 2020;383:
526–536.
40. Orgeron GM, Bhonsale A, Migliore F, James CA, Tichnell C, Murray B, et al.
Subcutaneous implantable cardioverter-defibrillator in patients with arrhythmogenic
right ventricular cardiomyopathy/dysplasia: a transatlantic experience. J Am Heart
Assoc 2018; 7: e008782.
41. Verstraelen TE, van Lint FHM, Bosman LP, de Brouwer R, Proost VM, Abeln BGS,
et al. Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation
carriers-reaching the frontiers of individual risk prediction. Eur Heart J 2021; 42:
2842–2850.
42. McKenna WJ, Asaad NA, Jacoby DL. Prediction of ventricular arrhythmia and sud-
den death in arrhythmogenic right ventricular cardiomyopathy. Eur Heart J 2019;
40:1859–1861.
43. Saguner AM, Medeiros-Domingo A, Schwyzer MA, On CJ, Haegeli LM, Wolber T,
et al. Usefulness of inducible ventricular tachycardia to predict long-term adverse
outcomes in arrhythmogenic right ventricular cardiomyopathy. Am J Cardiol 2013;
111: 250–257.
3052 P. Jordà et al.
D
ow
nloaded from
 https://academ
ic.oup.com
/eurheartj/article/43/32/3041/6619318 by C
entro N
acional de Investigaciones C
ardiovasculares (C
N
IC
) user on 03 April 2023