Objective To explore the potential categories and influencing factors of chronic comorbidity treatment burden in maintenance hemodialysis (MHD) patients. Methods Convenience sampling method was used to select MHD patients between April and May 2023 at Northern Jiangsu People’s Hospital and Jiangdu People’s Hospital as the research subjects. The general information questionnaire, Chronic Disease Comorbidity Treatment Burden Scale, and Health Literacy Scale for Chronic Disease Patients were used for the questionnaire survey. The latent class analysis was used to explore the classification of chronic comorbidity treatment burden in MHD patients, and the multi-class logistic regression analysis was used to explore the influencing factors of comorbidity treatment burden. Results A total of 450 survey questionnaires were distributed, and 406 valid questionnaires were collected, with an effective response rate of 90.22%. According to the latent class analysis results, the comorbidity treatment burden of MHD patients was divided into three potential categories. Among them, there were 26 cases in the low-burden group, 194 cases in the medium-burden group, and 186 cases in the high-burden group. The results of the ordered multi-class logistic regression analysis showed that patient age, educational level, dialysis age, number of comorbidities, and level of economic support were potential factors affecting the comorbidity treatment burden in MHD patients (P<0.05). Conclusions The comorbidity treatment burden of MHD patients can be divided into three potential categories. The age, educational level, dialysis age, number of comorbidities, and level of economic support of patients are potential factors affecting the comorbidity treatment burden in MHD patients.
Objective To investigate the latent categories of symptom cluster characteristics in patients with knee osteoarthritis (KOA) after total knee arthroplasty (TKA), and compare the quality of life between these categories. Methods Patients undergoing TKA for KOA in the joint surgery departments of four tertiary-level A hospitals in Urumqi, Xinjiang between November 2023 and March 2024 were selected for the study using the convenience sampling method. Symptoms of postoperative pain, swelling, anxiety, depression, and sleep disorders were collected from patients for latent class analysis using Mplus 8.3 software, and their influencing factors and differences in quality of life between categories were analyzed using SPSS 26.0 software. Results A total of 380 copies of questionnaire were distributed and 362 valid ones were returned, with a validity rate of 95.3%. Of the 362 patients, 342 (94.5%) had symptom cluster. The 342 patients aged 47-85 years, with a mean age of (65.25±7.03) years; 83 (24.3%) were male and 259 (75.7%) were female. According to the postoperative symptom cluster, the patients could be categorized into 3 latent categories: high-symptomatic group (16.1%), low-symptomatic group (51.8%), and high swelling group (32.2%). Compared to the low-symptomatic group, the current being the first joint surgery was a risk factor for the high-symptomatic group [odds ratio (OR)=2.732, 95% confidence interval (CI) (1.216, 6.139), P=0.015], whereas body mass index between 24.0 and 27.9 kg/m2 was a protective factor for the high-symptomatic group [OR=0.362, 95%CI (0.156, 0.840), P=0.018]; body mass index <24.0 kg/m2 was an independent risk factor for the high swelling group [OR=2.769, 95%CI (1.321, 5.803), P=0.007]. Comparison of the quality of life of patients in the 3 latent categories revealed that the high-symptomatic group had the lowest quality of life scores (P<0.05). Conclusion Post-TKA symptom cluster in patients with KOA can be classified into 3 potential categories, and the quality of life performance is different among different categories, so precise symptom management strategies should be provided according to the symptom characteristics of the patients to improve their quality of life.
Objective To identify potential clinical phenotypes in immunoglobulin G4-related disease (IgG4-RD) and to characterize differences in immune and metabolic profiles across distinct phenotypic subgroups. Methods A total of 125 patients diagnosed with IgG4-RD at West China Hospital, Sichuan University between January 2020 and December 2024 were retrospectively selected, and data regarding 12 organs (including the prostate) were collected. The prostate data were utilized solely for descriptive statistical purposes. Latent class analysis (LCA) was conducted using 11 organ variables, excluding the prostate, to perform a data-driven unsupervised classification of multiple organ involvement patterns. The optimal model was selected by comprehensively considering model fit indices, class size distribution, and clinical interpretability. Serum levels of immunoglobulin (Ig)G4, IgE, and uric acid were compared across different phenotypes. Results Among 125 patients with IgG4-RD, 89 were male (71.2%) and 36 were female (28.8%). Involvement of the pancreas was observed in 25 cases (20.0%), lymph nodes in 23 cases (18.4%), biliary system in 18 cases (14.4%), lungs in 16 cases (12.8%), lacrimal glands in 13 cases (10.4%), salivary glands and retroperitoneal fibrosis in 11 cases each (8.8%), eyes and pituitary glands in 7 cases each (5.6%), kidneys and aorta in 6 cases each (4.8%). LCA supported a three-class solution as optimal. Three distinct clinical phenotypes were identified: Class 1 (pulmonary-involvement type, n=26), characterized by frequent lung and lymph node involvement; Class 2 (mild systemic involvement type, n=89), showing limited multi-organ engagement; and Class 3 (glandular-lymphatic type, n=10), defined by predominant involvement of salivary glands, lacrimal glands, and lymph nodes, features resembling Mikulicz’s disease. Immunological and metabolic analysis revealed that the serum IgG4 level in Group 3 was higher than that in Group 1 and Group 2, but there was no statistically significant difference between the three groups (P>0.05). Spearman’s rank correlation analysis indicated a positive correlation between uric acid and IgG4 levels (r=0.2454, P=0.0067) as well as between IgE and IgG4 levels (r=0.4169, P<0.001). Conclusions Three distinct intrinsic clinical phenotypes of IgG4-RD were identified by LCA. Each phenotype exhibits characteristic patterns of organ involvement, along with specific immune and metabolic profiles. This suggests that interactions between metabolic and immune pathways may contribute to phenotypic differentiation and disease progression.