ABSTRACT
PURPOSE: To evaluate magnetic resonance elastography (MRE)-based liver stiffness measurement as a biomarker to predict the onset of cirrhosis in early-stage alcohol-related liver disease (ALD) patients, and the transition from compensated to decompensated cirrhosis in ALD. METHODS: Patients with ALD and at least one MRE examination between 2007 and 2020 were included in this study. Patient demographics, liver chemistries, MELD score (within 30 days of the first MRE), and alcohol abstinence history were collected from the electronic medical records. Liver stiffness and fat fraction were measured. Disease progression was assessed in the records by noting cirrhosis onset in early-stage ALD patients and decompensation in those initially presenting with compensated cirrhosis. Nomograms and cut-off values of liver stiffness, derived from Cox proportional hazards models were created to predict the likelihood of advancing to cirrhosis or decompensation. RESULTS: A total of 182 patients (132 men, median age 57 years) were included in this study. Among 110 patients with early-stage ALD, 23 (20.9%) developed cirrhosis after a median follow-up of 6.2 years. Among 72 patients with compensated cirrhosis, 33 (45.8%) developed decompensation after a median follow-up of 4.2 years. MRE-based liver stiffness, whether considered independently or adjusted for age, alcohol abstinence, fat fraction, and sex, was a significant and independent predictor for both future cirrhosis (Hazard ratio [HR] = 2.0-2.2, p = 0.002-0.003) and hepatic decompensation (HR = 1.2-1.3, p = 0.0001-0.006). Simplified Cox models, thresholds, and corresponding nomograms were devised for practical use, excluding non-significant or biased variables. CONCLUSIONS: MRE-based liver stiffness assessment is a useful predictor for the development of cirrhosis or decompensation in patients with ALD.
ABSTRACT
BACKGROUND: Multiple chronic conditions (multimorbidity) are becoming more prevalent among aging populations. Digital health technologies have the potential to assist in the self-management of multimorbidity, improving the awareness and monitoring of health and well-being, supporting a better understanding of the disease, and encouraging behavior change. OBJECTIVE: The aim of this study was to analyze how 60 older adults (mean age 74, SD 6.4; range 65-92 years) with multimorbidity engaged with digital symptom and well-being monitoring when using a digital health platform over a period of approximately 12 months. METHODS: Principal component analysis and clustering analysis were used to group participants based on their levels of engagement, and the data analysis focused on characteristics (eg, age, sex, and chronic health conditions), engagement outcomes, and symptom outcomes of the different clusters that were discovered. RESULTS: Three clusters were identified: the typical user group, the least engaged user group, and the highly engaged user group. Our findings show that age, sex, and the types of chronic health conditions do not influence engagement. The 3 primary factors influencing engagement were whether the same device was used to submit different health and well-being parameters, the number of manual operations required to take a reading, and the daily routine of the participants. The findings also indicate that higher levels of engagement may improve the participants' outcomes (eg, reduce symptom exacerbation and increase physical activity). CONCLUSIONS: The findings indicate potential factors that influence older adult engagement with digital health technologies for home-based multimorbidity self-management. The least engaged user groups showed decreased health and well-being outcomes related to multimorbidity self-management. Addressing the factors highlighted in this study in the design and implementation of home-based digital health technologies may improve symptom management and physical activity outcomes for older adults self-managing multimorbidity.
Subject(s)
Digital Health , Multimorbidity , Aged , Humans , Aging , Cluster Analysis , Data Accuracy , Aged, 80 and overABSTRACT
Background: Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions. Methods: The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement. Results: Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements. Conclusions: The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.
