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

Aung, Than Htike; Qin, Yan; Tay, Wan Ting; Bamadhaj, Nurul Sahiddah Binte Salahudin; Chandramouli, Chanchal; Ouwerkerk, Wouter; Tromp, Jasper; Anand, Inder; Richards, A. Mark; Hung, Chung‐Lieh; Teramoto, Koji; Teng, Tiew-Hwa Katherine; Lam, Carolyn S.P.

doi:10.1016/j.jacasi.2021.09.006

Cited by 7 publications

(12 citation statements)

References 40 publications

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“…Using the mFI-v10, we found that 68.3% of older HF patients were frail (47.5% of mild frailty and 20.8% of severe frailty), which were lower to a previous study using the same cumulative deficit approach (83%) 11 but similar to a registry-based study (69%). 21 The high prevalence of frailty may be attributed to the advanced age of our participants (mean age 80.4 years) or the nature of HF. One of our major merits is the nationwide population-based cohort design, which minimized potential selection bias and enabled precise estimation of healthcare utilization.…”

Section: Discussionmentioning

confidence: 90%

“…17,20 Nevertheless, studies using FI to categorize the health risk and to explore the impacts of frailty by FI on the use of HF-related medications and clinical outcomes among older HF adults are very limited. To the best of our knowledge, only one registry-based study focused on symptomatic HF has been reported, 21 and no population-based frailty assessment in Asian HF patients has been published before, not to mention the clinical im-pacts of the interplay between frailty and use of HF-related mediations. The lack of practice guidelines for coexisting frailty and HF results in fluctuating attitudes and suboptimal treatment of HF that hinders its prognosis.…”

Section: Introductionmentioning

confidence: 99%

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

Lai,

Huang,

Anker

et al. 2024

J cachexia sarcopenia muscle

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BackgroundFrailty 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.MethodsA 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.ResultsOf 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.ConclusionsFrailty 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.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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

Lai,

Huang,

Anker

et al. 2024

J cachexia sarcopenia muscle

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“…Risk factors were considered based on the following: (1) risk factors for PED identified in previous studies; (2) bedside evaluation completed within 4-6 h after extubation; and (3) variables observed by clinicians or nurses before the onset of dysphagia. Based on these criteria, a total of 35 risk factors were considered, including demographic characteristics (age, gender, body mass index [BMI], smoking history); admission diagnosis and pre-admission complications; surgical data (e.g., operation time, aortic occlusion time, esophageal ultrasound); intubation conditions (e.g., time, model, depth, air pressure, urgent tracheal intubation, repeated intubation); treatment (nasogastric tube, nasogastric tube insertion length, use of sedatives, use of analgesic drugs, pause of sedatives, pause of analgesic drugs); level of consciousness (somnolence); and various evaluations after extubation (e.g., activities of daily living [ADL] score, Acute Physiology and Chronic Health Evaluation II [APACHE II] score, Confusion Assessment Method for the Intensive Care Unit [CAM-ICU], New York Heart Association (NYHA) classifi cation, [12] integrity of oral mucosa, voice disorder, sore throat).…”

Section: Risk Factorsmentioning

confidence: 99%

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

Tang¹,

Feng²,

Huang³

et al. 2023

World Journal of Emergency Medicine

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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 infl ation 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 benefi t, 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 benefi t 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.

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“…2 In 2 recent studies of “real-world” as opposed to trial patients, the age gradient related to frailty was small and similar to that observed in our analysis of the DELIVER trial. 3,4…”

Section: In Responsementioning

confidence: 99%

“…2 In 2 recent studies of "real-world" as opposed to trial patients, the age gradient related to frailty was small and similar to that observed in our analysis of the DELIVER trial. 3,4 We do not agree that trials include few elderly patients. The proportion of patients ≥80 years of age in PARAGON-HF was 22.4%, and in DELIVER, it was 21.4%.…”

mentioning

confidence: 97%

Response by Butt et al to Letter Regarding Article, “Efficacy and Safety of Dapagliflozin According to Frailty in Patients With Heart Failure: A Prespecified Analysis of the DELIVER Trial”

2023

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

Cited by 7 publications

References 40 publications

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

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

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

Response by Butt et al to Letter Regarding Article, “Efficacy and Safety of Dapagliflozin According to Frailty in Patients With Heart Failure: A Prespecified Analysis of the DELIVER Trial”

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