Highlights
- •Clustering enables to stratify heart failure (HF) patients in 5 frailty clusters.
- •eHealth-based HF-care is effective regardless of the frailty phenotype.
- •Telemedicine (TM) tools in HF are cost-effective in the 5 described frailty strata.
- •Frailty should not be an exclusion criteria to engage patients in TM programmes.
Abstract
Background
The potential impact of telemedicine (TM) in the monitoring of patients with heart
failure (HF) is still uncertain particularly in the frailest patients. The aim of
this study was to define the efficacy of a TM-based managed care solution across different
HF patient frailty phenotypes.
Methods
We performed a clustering analysis on the basis of 8 frailty-related dimensions to
the HF-patients included in the ‘insuficiència Cardíaca Optimització Remota’ (iCOR) randomised study comparing TM vs. usual care (UC) in HF patients. The primary
study endpoint was the incidence of a non-fatal HF event after 6 months of inclusion.
The healthcare-related costs in each study group and cluster were also evaluated.
The event rates of primary and secondary study endpoints were calculated for each
cluster. Cox proportional-hazards regression models were used to evaluate the effect
of cluster, treatment group and the interaction term cluster by treatment group on
study endpoints.
Results
5 different frailty phenotypes were identified. The positive effect of TM compared
to UC strategy was consistent across all frailty phenotypes (p-value for interaction 0.711). The risk of experiencing a primary event was significantly
lower in patients that underwent allocation to the TM arm compared to UC (p-value = 0.016). Ultimately, the healthcare costs were significantly reduced in patients
allocated to the TM compared to UC in all 5 frailty phenotypes (all p-value < 0.05).
Conclusions
Non-invasive TM-based follow-up tools are effective compared to UC follow-up in preventing
HF events in the early post-discharge period, regardless of the 5 frailty phenotypes.
Graphical abstract
Graphical Abstract
Keywords
Abbreviations And Acronyms:
ACEI (angiotensin-converting-enzyme inhibitor), ADL (activities of daily living), AF (atrial fibrillation), APGAR (adaptability partnership growth affection resolve), ARB (angiotensin II receptor blocker), CI (confidence interval), CKD (chronic kidney disease), COPD (chronic obstructive pulmonary disease), COVID (SARS-CoV-2 disease), CV (cardiovascular), EEMs (least-square (marginal) means), eHealth (electronic health), EHFScBS (European heart failure self-care behaviour scale), GDS (geriatric depression scale), HF (heart failure), iCOR (insuficiència Cardíaca Optimització Remota), IQR (interquartile range), LVEF (left ventricular ejection fraction), mHealth (mobile health), MLHFQ (Minnesota living with heart failure questionnaire), MMSE (mini-mental state examination), MRA (mineralocorticoid receptor antagonist), NYHA (New York Heart Association), PROMs (patient reported measures), QoL (quality of life), TEN-HMS (Trans-European Network-Home-Care Management System), SD (standard deviation), SPAN-CHFII (Specialized Primary and Networked Care in Heart Failure), TM (telemedicine), UC (usual care)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: October 13, 2021
Accepted:
September 14,
2021
Received in revised form:
September 2,
2021
Received:
June 15,
2021
Identification
Copyright
© 2021 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
