Prevalence and Prognostic Significance of Frailty in Asian Patients With Heart Failure

Profile and Prognostic Impact of Multimorbidity in Elderly Patients with Heart Failure: Are there Differences between Men and Women?

PURPOSE OF REVIEW

Heart failure (HF) represents a pathology in constant growth, but, despite the fact that a significant proportion of its population is comprised of elderly patients, they are not adequately represented in clinical trials or registries. They constitute a heterogeneous population with their particularities and interaction of the multiple comorbidities that characterize this age group, which makes the clinical course, prognosis and outcomes of the disease different.

RECENT FINDINGS

Compared to men, women with HF tend to be older, with a greater burden of non-cardiovascular comorbidities, less ischemic heart disease and preserved ventricular function in most cases. This fact translates into worse self-perceived quality of life, with lower hospitalization and mortality rates. Moreover, paradoxically, women are less likely to receive treatment recommended by clinical practice guidelines, including revascularization and device placement. As there are not enough representative studies of this population, the reasons for these results with better prognosis and relatively benign impact in the elderly female population are unknown, which is why it is necessary to continue with research in order to obtain greater evidence of the exposed gaps.

The burden of frailty in heart failure: Prevalence, impacts on clinical outcomes and the role of heart failure medications

BACKGROUND

Frailty often coexists with heart failure (HF), which significantly aggravates the clinical outcomes of older adults. However, studies investigating the interplay between frailty and HF in older adults are scarce. We aimed to assess the prevalence of frailty using the cumulative deficit approach and evaluate the impacts of frailty on health utilization, use of HF-related medications and adverse clinical outcomes (all-cause mortality, all-cause readmissions and HF readmissions) among older HF patients.

METHODS

A total of 38 843 newly admitted HF patients were identified from Taiwan's National Health Insurance Research Database and categorized into three frailty subgroups (fit, mild frailty and severe frailty) based on the multimorbidity frailty index. Cox regression models and Fine and Gray subdistribution hazard models were used to estimate the impacts of frailty on clinical outcomes at 1 and 2 years of follow-up. Generalized estimating equation models were further conducted to evaluate the associations between longitudinal and time-varying use of HF-related medications and clinical outcomes among distinct frailty subgroups.

RESULTS

Of 38 843 older HF patients (mean age 80.4 ± 8.5 years, 52.3% females) identified, 68.3% were categorized as frail (47.5% of mild frailty and 20.8% of severe frailty). The median number of readmissions (fit: 1 [inter-quartile range-IQR 2], mild frailty: 1 [IQR 2] and severe frailty: 2 [IQR 3]) increased with the severity of frailty. Only 27.3% of HF patients died of cardiovascular diseases regardless of their frailty status. Compared with the fit group, the severe frailty group was associated with increased risk of all-cause mortality (adjusted hazard ratio 1.16, 95% confidence interval [CI] 1.11-1.21), all-cause readmissions (subdistributional hazard ratio (sHR) 1.21, 95% CI 1.16-1.25) and HF-related readmissions (sHR 1.14, 95% CI 1.09-1.20) at 2 years of follow-up. Those who used triple or more HF-related medications were at lower risk for all-cause readmissions (adjusted odds ratio [aOR] 0.49, 95% CI 0.44-0.54) and HF-related readmissions (aOR 0.42, 95% CI 0.37-0.47) at 2 years of follow-up even in the severe frailty group.

CONCLUSIONS

Frailty is highly prevalent and associated with increased risk of all-cause mortality, all-cause readmissions and HF readmissions among older HF patients. Those who were using triple or more HF-related medications were at lower risk of adverse clinical outcomes across distinct frailty subgroups. Further studies are needed to optimize the treatment strategies for older HF patients with distinct frailty status.

Association between natriuretic peptides and C-reactive protein with frailty in heart failure: a systematic review and meta-analysis

BACKGROUND

Heart failure (HF) and frailty are accompanied by a bidirectional relationship, sharing common risk factors including elevated levels of natriuretic peptides and inflammation. The aim of this study was to compare biomarkers associated with poor clinical outcomes, that is, plasma brain natriuretic peptide (BNP), N-terminal-pro B-type natriuretic peptide (NT-proBNP), and C-reactive protein (CRP) in patients with HF and frailty vs. patients with HF without frailty.

METHODS

From inception until July 2023, PubMed, Scopus, Web of Science, and Cochrane Library a systematic literature search was conducted. To evaluate whether frailty is linked with greater levels of BNP, NT-proBNP, and CRP, a meta-analysis using a random-effects model was used to calculate the pooled effects (CRD42023446607).

RESULTS

Fifty-three studies were included in this systematic review and meta-analysis. Patients with HF and frailty displayed significantly higher levels of BNP (k = 11; SMD: 0.53, 95%CI 0.30-0.76, I2 = 86%, P < 0.01), NT-proBNP (k = 23; SMD: 0.33, 95%CI 0.25-0.40, I2 = 72%, P < 0.01), and CRP (k = 8; SMD: 0.30, 95%CI 0.12-0.48, I2 = 62%, P < 0.01) vs. patients with HF without frailty. Using meta-regression, body mass index (BMI) and age were deemed potential moderators of these findings.

CONCLUSIONS

Frailty in HF is linked to increased concentrations of BNP, NT-proBNP, and CRP, which have been epidemiologically associated with adverse outcomes. The increased risk of NYHA III/IV classification further emphasizes the clinical impact of frailty in this population.

Development and validation of a predictive model for patients with post-extubation dysphagia

BACKGROUND

Swallowing disorder is a common clinical symptom that can lead to a series of complications, including aspiration, aspiration pneumonia, and malnutrition. This study aimed to investigate risk factors of post-extubation dysphagia (PED) in intensive care unit (ICU) patients with endotracheal intubation, and to develop a risk-predictive model for PED, which could serve as an assessment tool for the prevention and control of PED.

METHODS

Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort. Patients recruited from the same hospital from March to June 2022 served as the external validation cohort for the predictive model. We used a combination of variable screening and least absolute shrinkage and selection operator (LASSO) regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance inflation factor method. Multivariate logistic regression analysis was performed to calculate the odds ratio (OR; 95% confidence interval [95% CI]) and P-value for each variable to predict diagnosis. The screened risk factors were introduced into R software to build a nomogram model. The performance of the model, including discrimination ability, calibration, and clinical benefit, was evaluated by plotting the receiver operating characteristic (ROC), calibration, and decision curves.

RESULTS

A total of 305 patients were included in this study. Among them, 235 patients (53 PED vs. 182 non-PED) were enrolled in the derivation cohort, while 70 patients (17 PED vs. 53 non-PED) were enrolled in the validation cohort. The independent predictors included age, pause of sedatives, level of consciousness, activities of daily living (ADL) score, nasogastric tube, sore throat, and voice disorder. These predictors were used to establish the predictive nomogram model. The model demonstrated good discriminative ability, and the area under the ROC curve (AUC) was 0.945 (95% CI 0.904-0.970). Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907 (95% CI 0.831-0.983) and good calibration. The decision-curve analysis of this nomogram showed a net benefit of the model.

CONCLUSION

A predictive model that incorporates age, pause of sedatives, level of consciousness, ADL score, nasogastric tube, sore throat, and voice disorder may have the potential to predict PED in ICU patients.