Comparison of liver disease state progression in patients with eradication of versus persistent infection with hepatitis C virus: Markov chain analysis

Characterization of sleep efficiency transitions in family caregivers

Family caregivers are at high risk of psychological distress and low sleep efficiency resulting from their caregiving responsibilities. Although psychological symptoms are associated with sleep efficiency, there is limited knowledge about the association of psychological distress with variations in sleep efficiency. We aimed to characterize the short- and long-term patterns of caregivers' sleep efficiency using Markov chain models and compare these patterns between groups with high and low psychological symptoms (i.e., depression, anxiety, and caregiving stress). Based on 7-day actigraphy data from 33 caregivers, we categorized sleep efficiency into three states, < 75% (S1), 75-84% (S2), and ≥ 85% (S3), and developed Markov chain models. Caregivers were likely to maintain a consistent sleep efficiency state from one night to the next without returning efficiently to a normal state. On average, it took 3.6-5.1 days to return to a night of normal sleep efficiency (S3) from lower states, and the long-term probability of achieving normal sleep was 42%. We observed lower probabilities of transitioning to or remaining in a normal sleep efficiency state (S3) in the high depression and anxiety groups compared to the low symptom groups. The differences in the time required to return to a normal state were inconsistent by symptom levels. The long-term probability of achieving normal sleep efficiency was significantly lower for caregivers with high depression and anxiety compared to the low symptom groups. Caregivers' sleep efficiency appears to remain relatively consistent over time and does not show rapid recovery. Caregivers with higher levels of depression and anxiety may be more vulnerable to sustained suboptimal sleep efficiency.

Impact of antiviral therapy for disease progression and non-invasive liver fibrosis index in patients with chronic hepatitis C: Markov chain model analysis

BACKGROUND/AIM

Antiviral therapy advancements resulted in an era in which eradication of hepatitis C has become a goal, however, there are few reports on the long-term course of liver disease progression with antiviral therapy. The aim of this study was to use the Markov model to analyze disease progression and non-invasive liver fibrosis index in hepatitis C Patients.

METHODS

Patients with chronic hepatitis C (n = 1432) were diagnosed between January 2012 and May 2021 in the Musashino Red Cross Hospital. Patients with other hepatitis virus co-infection, chronic liver disease, and hepatocellular carcinoma (HCC) at the beginning of the study were excluded. A total of 618 patients with a 1-year or longer observation period were studied. The liver disease state was defined as chronic hepatitis (CH), compensated liver cirrhosis (CLC), decompensated liver cirrhosis (DLC), and HCC.

RESULTS

Cirrhosis and high FIB-4 index (≥3.61) were 42 cases (6.8%) and 208 cases (33.6%), respectively at the start of the study. The 40 years estimated transition analysis of 40-year-old CH low FIB-4 level (<3.61) revealed that the proportion of CH low/high, CLC low/high, DLC low/high, and HCC were 10.83%/10.86%, 0.35%/2.64%, 0%/3.21% 72.11% in untreated unit and 47.83%/9.21%, 6.69%/1.32%, 0.70%/0.99%, 33.27% in treated unit, respectively. Antiviral therapy suppressed liver fibrosis, disease progression, and HCC development significantly.

CONCLUSION

Markov model analysis of hepatitis C virus patients showed the impact of antiviral therapy on the suppression of disease progression in the order of CH, CLC, and DLC.