Influence of healthy candidate bias in assessing clinical effectiveness for implantable cardioverter-defibrillators: Cohort study of older patients with heart failure open access

Soko Setoguchi, Lynne Warner Stevenson, Garrick C. Stewart, Deepak L. Bhatt, Andrew E. Epstein, Manisha Desai, Lauren A. Williams, Chih Ying Chen

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Objective: To assess the potential contribution of unmeasured general health status to patient selection in assessments of the clinical effectiveness of implantable cardioverter-defibrillator (ICD) therapy. Design: Retrospective cohort study. Setting: Linked data from an ICD registry, heart failure registry, and Medicare claims data for ICDs implanted in 2005 through 2009. Participants: 29 426 patients admitted to hospital with heart failure aged 66 years or older and eligible for ICD therapy for primary prevention. Main outcome measures: Non-traumatic hip fracture, admission to a skilled nursing facility, and 30 day mortality-outcomes unlikely to be improved by ICD therapy. Results: Compared with 17 853 patients without ICD therapy, 11 573 patients with ICD therapy were younger and had lower ejection fraction and more cardiac admissions to hospital but fewer non-cardiac admissions to hospital and comorbid conditions. Patients with ICD therapy had greater freedom from unrelated events after adjusting for age and sex: hip fracture (hazard ratio 0.77, 95% confidence interval 0.64 to 0.92), skilled nursing facility admission (0.53, 0.50 to 0.55), and 30 day mortality (0.12, 0.10 to 0.15). Conclusions: Lower risks of measured outcomes likely reflect unmeasured differences in comorbidity and frailty. The findings highlight potential pitfalls of observational comparative effectiveness research and support physician consideration of general health status in selecting patients for ICD therapy.

Original languageEnglish (US)
Article numberg2866
JournalBMJ (Online)
Volume348
DOIs
StatePublished - May 8 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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