Predicting long-term survival after de novo cardioverter-defibrillator implantation for primary prevention: A population based study

Abstract

Background: Implantable cardioverter-defibrillators (ICDs) reduce the risk of sudden cardiac death in patients with left ventricular dysfunction. While short-term mortality benefit of ICD insertion has been established in landmark randomized controlled trials, little is known about the long-term outcomes of patients with ICDs in clinical practice. In this paper, we describe the long-term survival of patients following de novo ICD implantation for primary prevention in clinical practice and determine the factors which help predict survival after ICD implant.

Methods: Retrospective population-based study of all patients receiving a de novo ICD for primary prevention in Ontario, Canada from 2007 to 2011 using the Ontario ICD Database housed within ICES. Simple random selection was used to split the population into a derivation and internal validation cohort in a ratio of 2:1. Cox proportional hazards regression was used to determine predictors of interest and predict 10-year survival, model performance was assessed using calibration and validation.

Results: In the derivation cohort (n = 3399), mean age was 65.3 years (standard deviation [SD] = 11.0), 664 patients were female (19.5 %) and 2344 patients (69.0 %) had ischemic cardiomyopathy. Ten year survival was 45.7 % (95 % confidence interval [CI] 44.0 %-47.4 %). The final prediction model included age, sex, disease factors (ischemic vs nonischemic cardiomyopathy, left ventricular ejection fraction) and patient factors (symptoms, comorbidities), and biomarkers at the time of ICD assessment. This model had good discrimination and calibration in derivation (0.79, 95 % CI 0.77, 0.81) and validation samples (0.78, 95 % CI 0.76, 0.79).

Conclusions: A combination of demographic and clinical factors determined at baseline can be used to predict 10-year survival in patients with implantable cardioverter-defibrillators with good accuracy. Our findings help to identify individuals at risk of long-term mortality and may be useful in targeting future prevention strategies to enhance longevity in this high-risk population.

Keywords: Cardiomyopathy; Heart failure; Implantable cardioverter defibrillator; Long-term follow-up; Long-term outcomes; Population-level research.

Fig. 1

Patient flow diagram Abbreviations: ARVC, arrhythmogenic right ventricular cardiomyopathy; HCM, hypertrophic cardiomyopathy, LQTS, long QT syndrome; ICD, implantable cardioverter defibrillator.

Fig. 2

Survival after ICD insertion for primary prevention.

Fig. 3

Model calibration using observed vs predicted risk of death for the development of the base model. Model name, predictors included in each model, and corresponding derivation and validation calibration plots are indicated in columns a to d respectively. Diagonal dotted line indicates perfect calibration. Seven models were sequentially tested, Model 7 was identified as the final model with the best performance in calibration and discrimination. Abbreviations: ECG = electrocardiogram, LVEF = left ventricular ejection fraction, NYHA = New York Heart Association dyspnea scale ¹Comorbidities: diabetes, hypertension, cerebrovascular accident/transient ischemic attack, current smoker, peripheral vascular disease, chronic obstructive pulmonary disease, dialysis ²Medications: loop diuretic, oral anticoagulant, digoxin, aspirin, clopidogrel ³Biomarkers: sodium, hemoglobin, estimated glomerular filtration, QRS on ECG ⁴Categories for biomarkers: Sodium:≤135 mmol/L, 136–140 mmol/L, >140 mmol/L; Hemoglobin:≤70 g/L, 71–120 g/L, 121–140 g/L, >140 g/L; eGFR:≤60 mL/min/1.73 m², >60 mL/min/1.73 m²; QRS:≤120 ms, 121–160 ms, >160 ms