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Background/Introduction The potential impact of telemedicine (TM) in the monitoring of heart failure (HF) patients is still uncertain, largely due to the heterogeneity of the studies published to date. A subgroup of patients in which its key role is particularly uncertain is that of the frailest patients mainly due to TM-based strategies have been often discouraged on the basis of a foreseeable limited benefit in them. Purpose The aim of this study was to define the efficacy of a TM-based managed care solution across different HF patient frailty phenotypes in a cohort of HF patients recruited in a randomized clinical trial (The Insuficiència Cardíaca Optimitzaciό Remota [iCOR] study) evaluating the efficacy of a TM-based management compared to usual care (UC) in the early post-discharge period. Methods Five previously described frailty clusters were analysed. Cox proportional-hazards regression models were used to evaluate the effect of each cluster and group of treatment (and its interaction) on a series of endpoints (the incidence of non-fatal HF events as primary endpoint and all-cause hospitalization, all-cause death and the composite endpoint combining of all-cause death or non-fatal HF events as secondary endpoints). The incidence proportion of the first occurrence of each of the study endpoints was calculated for each study arm and for cluster, and these compared using χ2 tests. Additionally, a survival analysis was conducted using Cox regression to describe the event-free survival experience of the combination of the clusters with each of the 2 treatment groups for the study endpoints evaluated, and p-value was used to compare the different curves. Results 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). As shown for the primary endpoint, the positive effect of TM compared to UC strategy was consistent across all frailty phenotypes also for the secondary endpoints (all p-value for interaction >0.05). Likewise, the risk of all-cause hospitalization or the composite end-point of all-cause death or non-fatal HF event was significantly lower in patients that underwent allocation to the TM arm compared to UC (p-value=0.030 and 0.016 respectively). However, the risk of all-cause death did not differ across subgroup strata (p-value>0.05). Conclusion(s) This study showed that non-invasive TM-based follow-up tools are effective compared to UC in preventing fatal and non-fatal adverse events in the early post-discharge period, regardless of the 5 different frailty phenotypes. Importantly, when comparing TM-based follow-up with UC management in patients belonging to equal frailty cluster, those who were followed-up by eHealth had a considerably lower risk of non-fatal HF events, hospitalization or death. Funding Acknowledgement Type of funding sources: None. Cox regression non-fatal HF eventsCox regression all-cause hospitalization

Background/Introduction Several variables such as clinical, socioeconomic, functional or cognitive, among others can have an impact on the prognosis of heart failure (HF) patients despite the optimisation of follow-up strategies (e.g. telemedicine [TM] solutions). The clustering of HF patients may to identify different patient frailty phenotypes. Purpose The aim of this study was to perform a machine learning-based clustering analysis to identify different patient frailty phenotypes in a cohort of HF patients recruited in a randomized clinical trial (The Insuficiència Cardíaca Optimitzaciό Remota [iCOR] study). Methods We performed the clustering analysis on the basis of 8 frailty-related dimensions. To define the number of clusters, dissimilarity matrix was calculated with Gower's distance. Then, hierarchical divisive clustering was performed. Using then Elbow and Silhouette to analyse how the within sum of squares changes for the different number of clusters, the final number of clusters were chosen. The incidence proportion of the each of the study endpoints (non-fatal HF events as primary endpoint and all-cause hospitalization, all-cause death and the composite endpoint combining of all-cause death or non-fatal HF events as secondary endpoints) was calculated for cluster. Results 5 different frailty phenotypes were identified. Cluster 1 (29 patients, 16%) comprised patients with the best reported self-perceived health status (QoL), fair emotional-affective status, but low levels of self-care. Cluster 2 (41 patients, 23%) included the youngest patients with the highest level of education and a better level of cognition. Cluster 3 (68, 38%) encompassed the patients who had the best level of self-care behaviour (18.9±9.8), greater physical and instrumental functioning for activities of daily living (ADL) and a lower rate of comorbidities. Patients in the Cluster 4 (30 patients, 17%) tended to be elderly females with poor health-related QoL, and a higher level of functional dependence. Finally, Cluster 5 was the smallest group (10 patients, 6%), encompassing the oldest patients with low level of education, a worse affective-emotional state, a significant cognitive decline and a higher proportion of comorbidities compared to the other clusters. Cluster 4 had the highest incidence rate of the primary endpoint (57 per 100 patient-years at risk, 95% CI [37.4–74–5]) and a higher incidence of all-cause hospitalization and of the combined variable of all-cause of death or non-fatal HF events. Conclusion(s) Using the cluster analysis, we were able to stratify HF patients according to the stage of their impairment and vulnerability in each of the different frailty domains. This will allow clinicians to incorporate holistic multi-domain assessments in HF programmes to identify patients' needs and provide each patient with personalised and structured follow-up programme according to patient's needs (personalised and precision medicine). Funding Acknowledgement Type of funding sources: None. Radar chart to compare frailty clustersClinical endpoints according clusters