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. 2021 Jun 7;23(6):887-897.
doi: 10.1093/europace/euab012.

Development and external validation of prediction models to predict implantable cardioverter-defibrillator efficacy in primary prevention of sudden cardiac death

Tom E Verstraelen  1 , Marit van Barreveld  1   2 , Pascal H F M van Dessel  3 , Lucas V A Boersma  1   4 , Peter-Paul P H M Delnoy  5 , Anton E Tuinenburg  6 , Dominic A M J Theuns  7 , Pepijn H van der Voort  8 , Gerardus P Kimman  9 , Erik Buskens  10 , Michiel Hulleman  1 , Cornelis P Allaart  11 , Sipke Strikwerda  12 , Marcoen F Scholten  3 , Mathias Meine  6 , René Abels  13 , Alexander H Maass  14 , Mehran Firouzi  15 , Jos W M G Widdershoven  16 , Jan Elders  17 , Marco W F van Gent  18 , Muchtiar Khan  19 , Kevin Vernooy  20 , Robert W Grauss  21 , Raymond Tukkie  22 , Lieselot van Erven  23 , Han A M Spierenburg  24 , Marc A Brouwer  25 , Gerard L Bartels  26 , Nick R Bijsterveld  27 , Alida E Borger van der Burg  28 , Mattheus W Vet  29 , Richard Derksen  30 , Reinoud E Knops  1 , Frank A L E Bracke  8 , Markus Harden  31 , Christian Sticherling  32 , Rik Willems  33 , Tim Friede  31 , Markus Zabel  34   35 , Marcel G W Dijkgraaf  2 , Aeilko H Zwinderman  2 , Arthur A M Wilde  1
Affiliations

Development and external validation of prediction models to predict implantable cardioverter-defibrillator efficacy in primary prevention of sudden cardiac death

Tom E Verstraelen et al. Europace. .

Abstract

Aims: This study was performed to develop and externally validate prediction models for appropriate implantable cardioverter-defibrillator (ICD) shock and mortality to identify subgroups with insufficient benefit from ICD implantation.

Methods and results: We recruited patients scheduled for primary prevention ICD implantation and reduced left ventricular function. Bootstrapping-based Cox proportional hazards and Fine and Gray competing risk models with likely candidate predictors were developed for all-cause mortality and appropriate ICD shock, respectively. Between 2014 and 2018, we included 1441 consecutive patients in the development and 1450 patients in the validation cohort. During a median follow-up of 2.4 (IQR 2.1-2.8) years, 109 (7.6%) patients received appropriate ICD shock and 193 (13.4%) died in the development cohort. During a median follow-up of 2.7 (IQR 2.0-3.4) years, 105 (7.2%) received appropriate ICD shock and 223 (15.4%) died in the validation cohort. Selected predictors of appropriate ICD shock were gender, NSVT, ACE/ARB use, atrial fibrillation history, Aldosterone-antagonist use, Digoxin use, eGFR, (N)OAC use, and peripheral vascular disease. Selected predictors of all-cause mortality were age, diuretic use, sodium, NT-pro-BNP, and ACE/ARB use. C-statistic was 0.61 and 0.60 at respectively internal and external validation for appropriate ICD shock and 0.74 at both internal and external validation for mortality.

Conclusion: Although this cohort study was specifically designed to develop prediction models, risk stratification still remains challenging and no large group with insufficient benefit of ICD implantation was found. However, the prediction models have some clinical utility as we present several scenarios where ICD implantation might be postponed.

Keywords: Implantable cardioverter-defibrillator; Mortality; Prediction models; Primary prevention; Risk factors; Sudden cardiac death.

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Figures

Figure 1
Figure 1
Flowchart of the participants of the development (DO-IT) cohort.
Figure 2
Figure 2
Cumulative incidence curves of appropriate ICD shock and mortality plotted with the Fine and Gray competing risk method. Follow-up duration is shown on the x-axis, and probability of either event on the y-axis. The upper right part of the figure contains the cumulative incidence curves with a probability range from 0 to 0.3. ICD, implantable cardioverter-defibrillator.
Figure 3
Figure 3
Cumulative incidence curves of appropriate ICD shock and mortality plotted with the Fine and Gray competing risk method stratified by LVEF and cardiomyopathy subgroups. Follow-up duration is shown on the x-axis, and probability of either event on the y-axis with a probability range of 0–0.3 shown. ICD, implantable cardioverter-defibrillator; ICM, ischaemic cardiomyopathy; LVEF, left ventricular ejection fraction; NICM, non-ischaemic cardiomyopathy.
Figure 4
Figure 4
Scenario analysis with the ICD shock model in the DO-IT (A), and EU-CERT-ICD (B) cohort. The effects of applying the model to the DO-IT and EU-CERT population where patients with a lower predicted probability of ICD shock than the risk threshold would not be treated with an ICD are shown. Risk threshold is on the x-axis, the number of patients on the y-axis. Each bar represents the complete cohort, where the different colours represent the proportion of carriers experiencing the event (appropriate ICD shock) as well as the placement or non-placement of an ICD. Number needed to treat (NNT) refers to the number of patients needed to treat to protect one patient who would have received an ICD shock in 2 years for the group below the risk threshold. ICD, implantable cardioverter-defibrillator.
Figure 5
Figure 5
Example prediction of all-cause mortality and ICD shock in two patients. With the prediction models individual estimations of all-cause mortality and ICD shock risk can be made. In combination with an appropriate ICD shock risk threshold, these estimations can be used to guide ICD implantation for primary prevention of SCD. ICD, implantable cardioverter-defibrillator.
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