Heart failure & cardiomyopathies
Which frailty tool best predicts morbidity and mortality in ambulatory patients with heart failure? A prospective study
Authors' conclusion
When added to a base model for predicting mortality at 1 year including NYHA, NT-proBNP, sodium, and AF, CFS provides comparable prognostic information with assessment tools taking longer to perform.
Study context
- Population
- Ambulatory patients with heart failure (HF) attending a routine follow-up visitmedian follow-up 554 days
- Participants
- 467 patients
- Exposure
- Prognostic value of frailty tools added to a base model
- Outcome
- All-cause mortality prediction (C-statistic)
- Follow-up
- 1 year
Methodological detailsconsecutive patients
- Consecutive patients
Main finding
As reported in the abstract
C-statistic for 1-year mortality
Base model
0.75
C-statistic
Clinical frailty scale (CFS)
0.84
C-statistic
Deficit index (DI)
0.83
C-statistic
5MWT
0.80
C-statistic
- CFS, DI and 5MWT each increased model performance most vs base model (best of screening, assessment and physical tools respectively)
- P < 0.05 for all
Observational data: associations, not effects.
Further findings
Death and death or hospitalisation · median follow-up 554 days
- Deaths
- 82 (18%)
- Hospitalised or died
- 201 (43%)
Independent associations with all-cause death (multivariable)
- Only log[NT-proBNP] and frailty independently associated with all-cause death
- Corrected for age, Charlson score, haemoglobin, renal function, sodium, NYHA, atrial fibrillation (AF) and BMI
Original publication
Which frailty tool best predicts morbidity and mortality in ambulatory patients with heart failure? A prospective study. Eur Heart J Qual Care Clin Outcomes. Published 17 November 2022. doi:10.
Summarised from the published abstract; every number is checked against it