Individual patient data network meta-analysis of mortality effects of implantable cardiac devices.

B Woods; N Hawkins; S Mealing; A Sutton; WT Abraham; JF Beshai; H Klein; M Sculpher; CJ Plummer; MR Cowie; (2015) Individual patient data network meta-analysis of mortality effects of implantable cardiac devices. Heart (British Cardiac Society), 101 (22). pp. 1800-1806. ISSN 1355-6037 DOI: 10.1136/heartjnl-2015-307634
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OBJECTIVE: Implantable cardioverter defibrillators (ICD), cardiac resynchronisation therapy pacemakers (CRT-P) and the combination therapy (CRT-D) have been shown to reduce all-cause mortality compared with medical therapy alone in patients with heart failure and reduced EF. Our aim was to synthesise data from major randomised controlled trials to estimate the comparative mortality effects of these devices and how these vary according to patients' characteristics. METHODS: Data from 13 randomised trials (12 638 patients) were provided by medical technology companies. Individual patient data were synthesised using network meta-analysis. RESULTS: Unadjusted analyses found CRT-D to be the most effective treatment (reduction in rate of death vs medical therapy: 42% (95% credible interval: 32-50%), followed by ICD (29% (20-37%)) and CRT-P (28% (15-40%)). CRT-D reduced mortality compared with CRT-P (19% (1-33%)) and ICD (18% (7-28%)). QRS duration, left bundle branch block (LBBB) morphology, age and gender were included as predictors of benefit in the final adjusted model. In this model, CRT-D reduced mortality in all subgroups (range: 53% (34-66%) to 28% (-1% to 49%)). Patients with QRS duration ≥150 ms, LBBB morphology and female gender benefited more from CRT-P and CRT-D. Men and those <60 years benefited more from ICD. CONCLUSIONS: These data provide estimates for the mortality benefits of device therapy conditional upon multiple patient characteristics. They can be used to estimate an individual patient's expected relative benefit and thus inform shared decision making. Clinical guidelines should discuss age and gender as predictors of device benefits.


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