ObjectiveTo investigate the incidence, severity and longitudinal trajectories of symptoms at various time points in the perioperative period of lung cancer patients, and to provide scientific basis for clinical staff to implement predictive nursing and dynamic management of symptom clusters. MethodsA prospective longitudinal investigation was conducted. The patients with lung cancer who underwent thoracoscopic lung surgery in four wards of the Department of Thoracic Surgery in our hospital were investigated by face-to-face and telephone follow-up before surgery, 1-2 days after surgery, on the day of discharge and 2 weeks after discharge. The investigation tool was the revised Chinese version of MD Anderson Symptom Inventory lung cancer specific module. Results A total of 192 patients with lung cancer were included in this study, including 59 males and 133 females, with an average age of (55.68±11.01) years. There were two symptom clusters (respiratory-gastrointestinal and emotional/psychological-disturbed sleep symptom clusters) before surgery, three symptom clusters (respiratory, gastrointestinal, and emotional/psychological-disturbed sleep symptom clusters) 1-2 days after surgery, three symptom clusters (pain-fatigue-emotional/psychological, respiratory, and gastrointestinal symptom clusters) on the day of discharge, and two symptom clusters (pain-fatigue-respiratory and respiratory symptom clusters) 2 weeks after discharge. The composition of symptoms was different in each time point during perioperative period. ConclusionThere are four symptom clusters in patients with lung cancer during perioperative period, which are pain-fatigue-disturbed sleep symptoms, gastrointestinal symptoms, respiratory symptoms and emotional/psychological symptoms. The symptom clusters of lung cancer patients at different time points are relatively stable, but the symptoms within the symptom clusters show dynamic changes. Medical staff should attach great importance to and continuously monitor the dynamic changes of perioperative symptom groups of lung cancer patients, do relevant education and nursing in advance, and timely adjust the management plan according to the symptom group evaluation results.
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.
Patients with inflammatory bowel disease (IBD) often face the complex challenge of multiple coexisting symptoms, and systematic assessment and management of symptom clusters are key to improving prognosis. This article provides a systematic review and analysis of domestic and international literature on symptom clusters in patients with IBD, briefly introduces the clinical characteristics of these symptom clusters, and summarizes the types, applicability, reliability, validity, and current application status of related assessment tools. At present, assessment tools for symptom clusters in patients with IBD still require optimization in terms of standardization and individualization. In the future, more precise symptom cluster management systems should be developed to provide a basis for research and clinical management of symptom clusters in these patients, thereby enhancing the precision of symptom management.
Objective To explore the incidence and severity of diverse clinical symptoms in patients with severe acute pancreatitis (SAP), construct a symptom correlation network model, accurately identify core symptoms within the network, and classify symptom clusters. MethodsA convenience sampling method was adopted. A total of 211 patients with SAP admitted to the First Affiliated Hospital with Nanjing Medical University (Jiangsu Provincial People’s Hospital) from January 2024 to December 2025 were enrolled. The memorial symptom assessment scale was used for evaluation to investigate the composition of clinical symptoms in SAP patients. Exploratory factor analysis was performed to extract symptom clusters. R 4.4.2 software was applied to construct symptom clusters network. Centrality indicators including strength, betweenness and closeness were analyzed to identify core symptoms and core symptom clusters. ResultsA total of three symptom clusters were extracted in this study, including the gastrointestinal symptom cluster (abdominal pain, abdominal distension, dyspnea, dry mouth, nausea, vomiting, diarrhea, constipation, dizziness), the fatigue symptom cluster (lack of energy, difficulty concentrating, drowsiness), and the psychological symptom cluster (anxiety, sleep disturbance, irritability, sadness, distress). Among them, anxiety [95.7% (202/211)], difficulty concentrating [94.8% (200/211)], lack of energy [92.4% (195/211)], and abdominal pain [90.0% (190/211)] were identified as core symptoms. Symptoms with high strength values included anxiety (rs=2.2), difficulty concentrating (rs=1.2), and dyspnea (rs=1.0); symptoms with high closeness centrality values included drowsiness (rc=1.8), difficulty concentrating (rc=1.7), and anxiety (rc=1.1); symptoms with high betweenness centrality values included difficulty concentrating (rb=2.3), anxiety (rb=2.0), and drowsiness (rb=1.5). ConclusionsAnxiety, difficulty concentrating, lack of energy, and abdominal pain were identified as the core symptoms within the symptom clusters of SAP patients, with the fatigue symptom cluster being the core symptom cluster. It is recommended that medical staff pay close attention to the manifestations of core symptoms in SAP patients during treatment and formulate targeted symptom management plans.
ObjectivePatients with lung cancer often experience interrelated symptoms such as fatigue, pain, and sleep disturbances during chemotherapy, forming a core symptom cluster that significantly impacts daily functioning, treatment experience, and quality of life. Currently, evidence on non-pharmacological management of this core symptom cluster is scattered, and there is a lack of standardized, patient- and caregiver-friendly guidance tools that are easy to understand and implement. This patient guideline aims to provide decision support for self-management of the core symptom cluster in patients undergoing chemotherapy for lung cancer. MethodsGuided by evidence-based methodology, qualitative interviews and on-site questionnaire surveys were conducted to identify the health-related needs of stakeholders. Domestic and international guideline websites, relevant professional association websites, and comprehensive databases were systematically searched according to the inclusion and exclusion criteria. The included literature was evaluated for quality, and recommendations were extracted, integrated, and graded to form a summary of the best evidence. Based on the identified health issues and evidence summary, preliminary recommendations for the patient guideline were developed. The recommendations were then revised through two rounds of Delphi expert consultation, and consensus was reached on the strength of recommendations, forming the final version of the patient guideline. ResultsThe patient guideline covered four major sections: screening and assessment of core symptom clusters, daily management, intervention measures, and timing of referral. The core content included nine aspects: screening of core symptom clusters, assessment, lifestyle habits, dietary interventions, sleep environment, exercise interventions, psychological interventions, Traditional Chinese Medicine interventions, and referral indications. In total, the guidelines addressed 16 health issues and provided 25 recommendations. ConclusionThe development process of this patient guideline is rigorous and fully considered the needs of patients. Expert review concludes that the recommendations are highly feasible and appropriate.
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.