Related factors of fear of disease progression in patients with liver failure based on principal component analysis

Objective To analyze the related influencing factors of fear of disease progression(FoP)in patients with liver failure using a modified Logistic regression model based on principal component analysis.

Methods A total of 122 patients with liver failure were included in the study. The family support, health literacy, negative emotions, psychological resilience, and social support were evaluated using the Family Resilience Scale(FHI), Chinese version of the Comprehensive Health Literacy Survey Scale, Positive and Negative Affect Scale(PANAS), Connor Davidson Resilience Scale(CD-RISC) and MOS Social Support Scale (MOS-SSS). Fear of Progression Questionnaire-Short Form(FoP-Q-SF) was used to evaluate FoP condition, and the patients were divided into FoP group (n=74) and control group (n=48) with a FOP-Q-SF score of 34 as the critical value. The original variables were diagnosed collinearly, and the related factors affecting FoP in patients with liver failure were analyzed by using the Logistic regression model improved by principal component analysis.

Results PANAS score and liver failure stage in the FoP group were higher than those in the control group, while the age, monthly income, employee medical insurance ratio, health literacy score, CD-RISC score, FHI score, and MOS-SSS score were lower than those in the control group (P < 0.05). Five independent related factors were analyzed using improved Logistic regression using principal component analysis. Among them, liver failure stage(advanced stage) (OR=3.903) was an independent risk factor for FoP in patients with liver failure, and age (OR=0.901), monthly income>5 000 Yuan RMB (OR=0.171), higher CD-RISC score (OR=0.874), and higher FHI score (OR=0.927) were independent protective factors for FoP in patients with liver failure (P < 0.05).

Conclusion The occurrence of FoP in patients with liver failure is the result of a combination of multiple factors. The improved Logistic regression model using principal component analysis can eliminate the collinearity relationship among different factors and obtain a more scientific model.