Untangling the Web of Malnutrition, Sarcopenia, and Frailty in Chronic Liver Disease

Does the change in Liver Frailty Index over the first week of hospitalisation predict mortality in patients with acute-on-chronic liver failure? A prospective cohort study from a Slovak liver centre

OBJECTIVE

Hospital admissions for advanced chronic liver disease (ACLD) are associated with increased mortality, disability, a decline in quality of life and significant economic costs. Being admitted to the hospital usually indicates a triggering event that disrupted a previously stable condition, leading to decompensation or complications of ACLD. The most acute and severe manifestation of this imbalance is acute-on-chronic liver failure (ACLF), a syndrome representing a critical juncture. Reliable prognostic stratification of patients admitted with ACLF could facilitate the systematic delivery of tailored care, ranging from palliative care to intensive interventions like extracorporeal liver support devices and prioritised liver transplantation. Disease-specific prognostic tools, such as the Model for End-Stage Liver Disease score, are effective but have limitations, particularly in reflecting a patient's potential for recovery. The concept of the body's functional reserve in the context of ACLD/ACLF is gaining attention, with the Liver Frailty Index (LFI) potentially emerging as a recommended diagnostic tool.

METHODS

Patients were selected from our cirrhosis registry (RH7). The LFI serves as an indicator of the patient's prognosis. The LFI measurement takes place at two time intervals: on the patient's admission and after 7 days of hospitalisation.

RESULTS

Our RH7 registry included 154 patients (15.1%) who were diagnosed with ACLF. The primary cause of the underlying ACLD was alcohol-associated liver disease in the majority (79.8%) of cases. The mean value of LFI at admission was 4.50 (± 0.94). When patients with liver cirrhosis were categorised into three subgroups based on the LFI on day 7, survival exhibited a statistically significant decrease (p≤0.05) across all three ACLF grades. This decline in survival was observed from the 'improved LFI' cohort, through the 'stable LFI' group, to the 'worsened LFI' group.

CONCLUSION

The impact of day 7 LFI on the survival of patients with ACLF is notable. Nevertheless, it does not markedly enhance the predictive capability of the LFI assessed on admission. Consequently, the initial LFI on day 1 continues to be the most valuable and commonly used instrument for promptly recognising individuals with ACLF.

Construction and Verification of a Frailty Risk Prediction Model for Elderly Patients with Coronary Heart Disease Based on a Machine Learning Algorithm

Background

This study aimed to develop a machine learning-based predictive model for assessing frailty risk among elderly patients with coronary heart disease (CHD).

Methods

From November 2020 to May 2023, a cohort of 1170 elderly patients diagnosed with CHD were enrolled from the Department of Cardiology of a tier-3 hospital in Anhui Province, China. Participants were randomly divided into a development group and a validation group, each containing 585 patients in a 1:1 ratio. Least absolute shrinkage and selection operator (LASSO) regression was employed in the development group to identify key variables influencing frailty among patients with CHD. These variables informed the creation of a machine learning prediction model, with the most accurate model selected. Predictive accuracy was subsequently evaluated in the validation group through receiver operating characteristic (ROC) curve analysis.

Results

LASSO regression identified the activities of daily living (ADL) score, hemoglobin, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), depression, cardiac function classification, cerebrovascular disease, diabetes, solitary living, and age as significant predictors of frailty among elderly patients with CHD in the development group. These variables were incorporated into a logistic regression model and four machine learning models: extreme gradient boosting (XGBoost), random forest (RF), light gradient boosting machine (LightGBM), and adaptive boosting (AdaBoost). AdaBoost demonstrated the highest accuracy in the development group, achieving an area under the ROC curve (AUC) of 0.803 in the validation group, indicating strong predictive capability.

Conclusions

By leveraging key frailty determinants in elderly patients with CHD, the AdaBoost machine learning model developed in this study has shown robust predictive performance through validated indicators and offers a reliable tool for assessing frailty risk in this patient population.

Frailty and Sarcopenia Assessment in Patients with Advanced Chronic Liver Disease in a Tertiary Center in Romania

Background/Objectives: Sarcopenia and frailty are both multidimensional and interrelated problems for patients with cirrhosis and require prompt assessment and appropriate management because of their impact on disease outcomes. Our purpose is to identify the prevalence of sarcopenia and frailty in patients with advanced liver disease. Furtherksdnvk more, our purpose is to explore the association between sarcopenia, frailty, and various complications and the impact of these conditions on short- and long-term hospital survival rates. Methods: A prospective, observational, unicentric study was conducted in an emergency university hospital in Romania between January 2021 and December 2023 that included patients with advanced liver diseases. The patients with sarcopenia and frailty were selected using measurements of handgrip strength (HGS), Short Physical Performance Battery (SPPB), liver frailty index (LFI), and skeletal muscle index (SMI). Patients were divided into four groups based on the presence of sarcopenia and/or frailty. Results: This study included 128 patients. Younger patients associated with both sarcopenia and frailty (55.76 ± 10.46 years). Most males were without sarcopenia and frailty (63.93%) compared to those with both sarcopenia and frailty (36.07%). The Child-Pugh score C was identified in the majority of those with both sarcopenia and frailty (69.70%). Higher values for MELD-Na scores were obtained in the group with sarcopenia and frailty (25.45 ± 6.924). Biomarkers like albumin, sodium, C-reactive protein, bilirubin, and platelets were statistically significant as mortality predictors in all four groups. Patients with both sarcopenia and frailty presented more often with encephalopathy and spontaneous bacterial peritonitis. Survival rates in the short and long term were lower for the patients who associated both sarcopenia and frailty compared to those without sarcopenia and frailty. Conclusions: The presence of sarcopenia and frailty significantly impacts outcomes in patients with decompensated advanced liver disease. When both conditions coexist in the same patient, they markedly increase in-hospital mortality, as well as short- and long-term survival rates.

A short history of sarcopenia and frailty and their impact on advanced chronic liver disease

Sarcopenia, first introduced as a concept by I. Rosenberg in 1989, has since been extensively studied, particularly in its correlation with chronic diseases. In recent years, sarcopenia has been increasingly associated with advanced chronic liver disease, leading to a lower quality of life and poor outcomes for these patients. Studies have shown that sarcopenia has a prevalence of 33% in individuals with advanced chronic liver disease, impacting not only the patient's health but also contributing to increased healthcare costs. The prevalence of frailty in patients with advanced chronic liver disease is 27%. Given the high prevalence of sarcopenia and frailty in this population, early diagnosis and treatment are crucial to improving patient quality of life outcomes and reducing the strain on healthcare systems globally.

Fat mass: a novel digital biomarker for remote monitoring that may indicate risk for malnutrition and new complications in decompensated cirrhosis

BACKGROUND

Cirrhosis is associated with sarcopaenia and fat wasting, which drive decompensation and mortality. Currently, nutritional status, through body composition assessment, is not routinely monitored in outpatients. Given the deleterious outcomes associated with poor nutrition in decompensated cirrhosis, there is a need for remotely monitoring this to optimise community care.

METHODS

A retrospective analysis was conducted on patients monitored remotely with digital sensors post hospital discharge, to assess outcomes and indicators of new cirrhosis complications. 15 patients had daily fat mass measurements as part of monitoring over a median 10 weeks, using a Withing's bioimpedance scale. The Clinical Frailty Score (CFS) was used to assess frailty and several liver disease severity scores were assessed.

RESULTS

73.3% (11/15) patients were male with a median age of 63 (52-68). There was a trend towards more severe liver disease based on CLIF-Consortium Acute Decompensation (CLIF-C AD) scores in frail patients vs. those not frail (53 vs 46, p = 0.072). When the cohort was split into patients who gained fat mass over 8 weeks vs. those that lost fat mass, the baseline CLIF-C AD scores and WBC were significantly higher in those that lost fat (58 vs 48, p = 0.048 and 11.2 × 109 vs 4.7 × 109, p = 0.031).

CONCLUSIONS

This proof-of-principle study shows feasibility for remote monitoring of fat mass and nutritional reserve in decompensated cirrhosis. Our results suggest fat mass is associated with greater severity of acute decompensation and may serve as an indicator of systemic inflammatory response. Further prospective studies are required to validate this digital biomarker.