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Background Poor self-care behaviour is associated with a higher risk of readmission and mortality in patients with heart failure (HF). Psychosocial factors are key determinants of patient-reported outcomes (PROMs) such as health-realted quality of life (QoL). Little is known about the interplay between self-care and psychosocial factors on PROMs in HF. Purpose To describe the interaction of self-care and psycho-social factors on QoL in a real-world cohort of HF patients. Methods We conducted an observational, prospective, cohort study of 1120 consecutive patients with chronic. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care (inverted and standardised: higher scores indicate better self-care). To assess QoL, we used Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). General linear models (GLM) were constructed to explore the interaction between psychosocial factors and poor self-care (defined as EHFSCBS-9 ≤lower tertile) on adjusted MLHFQ scores. To determine the impact of self-care, multivariate linear regressions models to predict QoL were constructed stratified according to selected psycho-social determinants. All models were adjusted by age, sex and psycho-social determinants of QoL including literacy, cognitive function (Pfeiffer test), dependency (Barthel and Lawton & Brody test), social support (Duke UNC and APGAR Familyfunction), depressive symptoms (15-item Geriatric Depression Scale) and fragility (Barber test scores ≥1). Results A total of 484 patients (43%) were women, mean age was 72±11 years, and mean LVEF was 45±17%. Mean MLHFQ scores were 45±24. Mean EHFSCBS-9 scores were 69±28. There was a significant inverse correlation between EHFScBS-9 and MLHFQ scores (r=−0.158; p-value Conclusions We have shown that self-care behaviour is an independent predictor of PROMs in HF regardless the presence of important psycho-social determinants of QoL only when fragility status is taken into account. Fragility modulates the influence of self-care and QoL, including each one of its dimensions. Funding Acknowledgement Type of funding sources: None.

Background Poor self-care behaviour is associated with a higher risk in heart failure (HF). Gender is a determinant of patient-reported outcomes (PROMs) such as health-realted quality of life (QoL) in HF. Female gender is consistently associated with worse QoL. Whether gender may influence the effect of self-care on PROMS is not known. Purpose Describe the effect of self-care on QoL according to gender in a real world cohort of HF patients. Methods 1120 patients with chronic HF were analysed. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care (inverted and standardised: higher scores indicate better self-care). To assess QoL, we used Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). General linear models (GLM) were constructed to explore the interaction between gender and poor self-care (defined as EHFSCBS-9 ≤lower tertile) on adjusted MLHFQ scores. To determine the impact of global self-care and its dimensions, multivariate linear regressions models to predict QoL were constructed stratified according to gender. All models were adjusted by determinants of HF severity. Results Mean age was 72±11 years, 43% were women and mean LVEF was 45±17%. There was a significant inverse correlation between EHFScBS-9 and MLHFQ scores (r=−0.158; p-value Conclusions Self-care is an independent predictor of health-related QoL in HF, regardless of the level of disease severity. Global self-care, autonomy-based adherence and consulting behaviour were particularly associated with global QoL and emotional and social dimensions of QoL in women and men. In both genders, the effect of self-care on QoL was driven by its effect on emotional and social dimensions. Provider-based adherence did not influence QoL. Funding Acknowledgement Type of funding sources: None.

Background Poor self-care behaviour is associated with a higher risk of readmission and mortality in patients with heart failure (HF). The interplay between self-care and patient-reported outcomes such as health-related quality of life (QoL) has not been fully established. Purpose To describe the association between self-care and QoL in real-world cohort of HF patients. Methods We conducted an observational, prospective, cohort study of 1120 consecutive patients with chronic HF. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care. Scores were inverted and standardized (higher scores indicate better selfcare). To assess QoL, we used the Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). Written informed consent, complete clinical and psychosocial information of patients were obtained at baseline. To determine the impact of global self-care and its dimensions (autonomy-based adherence, consulting behaviour and provider-based adherence), multivariate linear regressions models (backwards stepwise methods) to predict QoL were constructed. The multivariate parametric and non-parametric associations between EHFSCBS-9 scoresand the β estimated risk of impairment of QoL (defined as MLHFQ scores ≥ median values) were explored using General Additive Models (GAM). All models were adjusted by age, sex and prognostic factors such as LVEF, NYHA, NT-proBNP levels and recent hospitalization among other well-known determinants of HF severity. Results A total of 484 patients (43%) were women, mean age was 72±11 years, and mean LVEF was 45±17%. Mean MLHFQ scores were 45±24. Mean EHFSCBS-9 scores were 69±28. There was a significant inverse correlation between EHFScBS-9 and MLHFQ scores (r=−0.158; p-value Conclusions We have shown that self-care behaviour is an independent predictor of health related QoL in HF regardless the level of disease severity. Global self-care and consulting behaviour were particularly and significantly associated with global QoL and emotional and social dimensions of QoL. Interestingly the relationship between self-care scores and the risk of impaired global QoL was non-linear showing a “U-shape” association pointing out that patients with extreme levels of self-care were more likely to experience impairments in QoL. Funding Acknowledgement Type of funding sources: None.

Background Poor self-care is associated with a poorer outcomes in patients with heart failure (HF). Psycho-social factors are determinants of patient-reported outcomes (PROMs) such as health-realted quality of life (QoL). However, little is known about the interplay between self-care and psychosocial factors such as cognitive function on PROMs in patients with HF. Purpose Describe the effect of self-care on QoL across cognitive function and explore whether the interaction between self-care and cognitive function may account for this effect. Methods 1120 patients with chronic HF from DAMOCLES study were analyzed. We used the modified European Heart Failure Self-care Behaviour Scale 9-item version (EHFSCBS-9) to measure self-care (inverted and standardised: higher scores indicate better self-care). To assess QoL, we used Minnesota Living with Heart Failure Questionnaire (MLHFQ) (higher scores indicate worse QoL). Cognitive function was evaluated using Mini-Mental State Examination (MMSE) and Short Portable Mental State Questionnaire (SPMSQ) Tests. Cognitive impairment was defined as abnormal age-and-literacy-adjusted scores. General linear models (GLM) were constructed to explore the interaction between cognitive function and poor self-care (defined as EHFSCBS-9 ≤lower tertile) on adjusted MLHFQ scores. To determine the impact self-care, multivariate linear regressions models to predict QoL were constructed stratified according to cognitive function. All models were adjusted by determinants of HF severity. Results Mean age was 72±11 years, 43% were women and mean LVEF was 45±17%. Lower MMSE scores (β=−0,163; p-value 3 errors in the SPMSQ indicating cognitive impairment (β=0.071; p-value = 0.023) were associated with higher scores in the MLHFQ. These associations were confirmed in multivariare linear regression models (β=0.062; p-value = 0.038). In adjusted GLM, poor global self-care (F=4.8; p-value = 0.029) was an independent predictor of higher adjusted scores in the MLHFQ. Interaction term poor self-care by cognitive impairment was significantly associated with MLHFQ scores (F=6.0; p-value=0.014; Figure 1). Multivariate linear regresion models (Table 1) stratified according the absence (panel A) or the presence (panel B) of cognitive impairment showed that cognitive function modulates the association between self-care behaviour and QoL. This effect was only seen in patients without cognitive impairment. In patients with cognitive dysfunction, self-care behaviour did not longer influence self-perceived health status in patients with HF. Conclusions Self-care behaviour is an independent predictor of PROMs in HF regardless cognitive disfunction. Cognitive function modulates 1) influence of global self-care and each one of its dimensions on emotional QoL and 2) impact of global self-care and consulting behaviour on global and social QoL. Funding Acknowledgement Type of funding sources: None.

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