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.
Objective To explore the latent classes of developmental trajectories and influencing factors of postoperative nausea and vomiting in patients undergoing open posterior spinal surgery. Methods Patients who underwent open posterior spinal surgery at Peking Union Medical College Hospital between November 2024 and October 2025 were enrolled. Demographic data and clinical data, as well as data on nausea, vomiting, and psychological status from postoperative day 1 to 3, were collected. Latent growth mixture model was used to identify developmental trajectories of PONV. Univariate analysis and binary logistic regression were performed to analyze the influencing factors of the latent postoperative nausea and vomiting classes. Results A total of 201 patients were included. Among them, there were 62 males and 139 females. Two distinct developmental trajectories of postoperative nausea and vomiting were identified by latent growth mixture model: the high risk postoperative nausea and vomiting class (84 patients, 41.8%) and the low risk postoperative nausea and vomiting class (117 patients, 58.2%). Logistic regression showed that a history of abdominal surgery, duration of peak pain on postoperative day 1, a history of previous postoperative nausea and vomiting or motion sickness, time from surgery completion to first oral intake, age and hemoglobin concentration on postoperative day 2 were associated with high risk of postoperative nausea and vomiting. Conclusions Two heterogeneous developmental trajectories of postoperative nausea and vomiting in patients undergoing open posterior spinal surgery were identified using latent growth mixture model, and the independent influencing factors were clarified. Medical staff can formulate targeted prevention and control measures based on the influencing factors of the latent classes, optimize management strategies for postoperative nausea and vomiting in spinal surgery patients, and provide a basis for precise clinical prevention and control of postoperative nausea and vomiting.
Objective To identify the potential categories of postoperative ileus (POI) after transforaminal lumbar interbody fusion (TLIF) and their influencing factors, so as to provide references for developing targeted nursing strategies. Methods Patients who underwent TLIF in the Department of Orthopedics, Army Medical University between August 2023 and January 2024 were conveniently selected. Data on patients’ general information, medical history, and preoperative, intraoperative, and postoperative conditions were collected through questionnaires and medical record reviews. Latent class analysis was used to categorize the gastrointestinal symptoms of POI, and multinomial logistic regression was applied to analyze the influencing factors. Results A total of 435 patients were included. The gastrointestinal symptoms of POI were classified into three latent categories: high abdominal pain and bloating class (204 cases), high nausea and vomiting class (46 cases), and weak gastrointestinal response class (185 cases). Multinomial logistic regression analysis showed that history of gastric disease (P=0.006) and time to first postoperative anal exhaust (P<0.001) were influencing factors for patients presenting with high abdominal pain and bloating symptoms. History of gastric disease (P<0.001) and low postoperative hemoglobin level (P=0.008) were influencing factors for patients presenting with high nausea and vomiting symptoms. Conclusions POI after TLIF can be categorized into three types: high abdominal pain and bloating class, high nausea and vomiting class, and weak gastrointestinal response class. Nurses should pay attention to the impact of gastric disease history, anemia, and postoperative flatus time on gastrointestinal function, strive to shorten preoperative fasting time, protect gastric mucosa, strengthen perioperative blood management, and adopt multimodal approaches to promote the recovery of gastrointestinal motility, thereby reducing the incidence of POI after lumbar fusion surgery.
ObjectiveTo explore the dynamic evolution trajectory of symptom clusters and the influencing factors among lung cancer patients during their first chemotherapy cycle, so as to provide evidence for precise and stratified symptom management. MethodsA convenience sampling method was adopted. Incident lung cancer patients were recruited from the Cancer Hospital, Chinese Academy of Medical Sciences between December 2025 and April 2026. Longitudinal surveys were conducted at 1 day before the first chemotherapy, and on days 1, 3, 5 and 7 after chemotherapy using the general information questionnaire, the Chinese version of the M. D. Anderson Symptom Inventory-Lung Cancer Module (MDASI-LC), and the Charlson Comorbidity Index (CCI). Exploratory factor analysis was used to identify the composition of symptom clusters at different time points. Latent class mixed models (LCMM) were applied to fit the developmental trajectories of symptom clusters. The least absolute shrinkage and selection operator (LASSO) regression was used for variable screening, followed by multivariate logistic regression to analyze the influencing factors of each trajectory class. ResultsA total of 133 patients were enrolled, including 97 males and 36 females, aged 37-78 (62.54±9.27) years. Five symptom clusters were identified: respiratory symptom cluster, cough-expectoration symptom cluster, chemotherapy-related symptom cluster, gastrointestinal symptom cluster and psychological symptom cluster. All symptom clusters exhibited significant dynamic changes throughout the first chemotherapy cycle (P<0.05). Trajectory analysis revealed 2 to 3 trajectory subtypes for symptom clusters with marked heterogeneity. Most symptom clusters peaked on days 3 to 5 after chemotherapy. Multivariate logistic regression analysis, with the low-level trajectory group as reference, showed that pathological type (P<0.001) and medical insurance type [OR=0.022, 95%CI (0.000, 0.976), P=0.049] were associated with membership of respiratory symptom cluster trajectories; household per capita monthly income [OR=3.102, 95%CI (1.183, 8.129), P=0.021] and chemotherapy regimen [OR=0.306, 95%CI (0.098, 0.959), P=0.042] were associated with membership of cough-expectoration symptom cluster trajectories; smoking amount [OR=1.039, 95%CI (1.003, 1.075), P=0.011] and clinical stage [OR=0.209, 95%CI (0.045, 0.977), P=0.047] were associated with membership of gastrointestinal symptom cluster trajectories; CCI [OR=0.342, 95%CI (0.131, 0.897), P=0.029] was associated with membership of psychological symptom cluster trajectories. ConclusionSymptom clusters in lung cancer patients demonstrate obvious dynamic evolutionary characteristics and individual heterogeneity during the first chemotherapy cycle. Days 3 to 5 post-chemotherapy serve as the critical window for symptom monitoring and intervention. Medical staff should implement individualized stratified management in consideration of patients’ pathological type, economic status, comorbidity burden and treatment regimen to realize precise early warning and timely intervention of symptoms.
