Objective To construct and validate a risk predictive model for treatment delay of patients with lung cancer. Methods A convenience sampling method was used to select 493 lung cancer patients admitted to a tertiary care hospital in Guangxi from September 2021 to June 2022. They were randomly divided into 345 cases in a modeling group and 148 cases in an internal validation group，and 47 lung cancer patients admitted to another tertiary care hospital were selected as the external validation group. The risk prediction model and nomograms model were constructed using logistic regression analysis and R software，and the area under the receiver operating characteristic（ROC） curve was used to test the prediction effect of the model. Results The established model was Y=e^z/（1+e^z），Z=9.04 × low level of disease perception + 2.01 × irregular physical examination-0.08 × perceived social support scale score + 0.23 × score of perceived barriers to healthcare-seeking decision-Chinese -0.15 × score of Health Literacy Scale for Chronic Patients. The ROC of the internal validation group was 0.849；the sensitivity was 82.39%；the specificity was 78.83%；the positive predictive was 90.61%；the negative predictive was 63.57%. The ROC of the external validation group was 0.830；the sensitivity was 97.36%；the specificity was 80.03%；the positive predictive was 94.62%；the negative predictive was 80.05%. Conclusion The low level of disease perception，lack of regular physical examination，the low level of social support，the high impaired perception of medical seeking behavior，and the low level of health literacy are the influencing factors of treatment delay of lung cancer patients，and the prediction model based on the above factors has a good degree of differentiation and calibration，which can help to identify the high-risk treatment delay lung cancer individuals. Nurses can identify high-risk groups based on this model，so as to take measures to improve their medical behavior and reduce the risk of treatment delay.

Objective To explore the current status of symptom clusters and sentinel symptoms in patients with acute exacerbation of chronic obstructive pulmonary disease（AECOPD），to analyze the correlation between the severity of sentinel symptoms and inflammatory indexes，and to provide a basis for symptom cluster interventions. Methods From October 2021 to March 2022，165 patients with AECOPD hospitalized in the Department of Respiratory and Critical Care Medicine of a tertiary care hospital in Nanning were conveniently selected. The questionnaires were administered using a revised version of the Memory Symptom Assessment Scale，and the results of inflammatory indexes were collected from the patients’ first tests after admission. Principal component analysis was used to extract symptom clusters，and the results of association analysis based on the Apriori algorithm were used to determine the sentinel symptoms within symptom clusters，and correlation analysis was used to explore the relationship between patients’ sentinel symptoms and inflammatory indexes. Results 3 symptom clusters existed in AECOPD patients，including respiratory symptom cluster，affective symptom cluster，and fatigue symptom cluster. Correlation analysis by the Apriori algorithm showed that lack of energy was the sentinel symptom of respiratory symptom cluster，feeling sad was the sentinel symptom of affective symptom cluster，and inattention was the sentinel symptom of fatigue symptom cluster. Correlation analysis showed that neutrophil-to-lymphocyte ratio and calcitoninogen were positively correlated with the severity of energy deficiency（r=0.179，P<0.05；r=0.246，P<0.01），and prealbumin was negatively correlated with it（r=-0.167，P<0.05）；monocyte count was positively correlated with the severity of feeling sad（r=0.236，P<0.01）；eosinophil count was negatively correlated with the severity of inattentiveness（r=-0.197，P<0.05）. Conclusion Sentinel symptoms exist within the symptom cluster of AECOPD patients，and their severity correlates with inflammatory indicators neutrophil-to-lymphocyte ratio，calcitoninogen，prealbumin，monocyte count，and eosinophil count. It is recommended that healthcare professionals can use sentinel symptoms as an entry point for symptom cluster management and develop individualized interventions for patients.

Objective To analyze the influencing factors of patient reported outcomes in patients with stable chronic obstructive pulmonary disease (COPD) and the correlation with inflammatory indexes,so as to provide the reference for the early nursing intervention with the focus of patients. Methods The convenience sampling method was used to select outpatients with stable COPD from the department of respiratory clinic of a tertiary hospital in Nanning from October 2020 to July 2021. Questionnaires,including a general information questionnaire and the revised version of the Chronic Obstructive Pulmonary Disease Patient Reported Outcome Scale (mCOPD-PRO),were used to collect the results of inflammatory response indicators at the time. Results A total of 204 patients with stable COPD were included,with the mCOPD-PRO score of (1.75±0.58). Multiple regression analysis showed that the factors influencing the patient reported outcomes with stable COPD were gender,the number of comorbidities,disease duration,and lung function grading (P<0.05),which explained 10.8 % of the variance. There were positive correlations between patient reported outcomes with stable COPD and white blood cell count (WBC),neutrophil count (NEU),and eosinophil count (EOS) (P<0.05). There were negative correlations between patient reported outcomes with stable COPD and total albumin (TP) (P<0.05). Conclusion The health level of patients reported outcomes in stable COPD needs to be improved. Female patients,patients with comorbidities>3,longer duration and worse lung function reported lower levels of health. Patient reported outcomes with COPD are closely related to the levels of serum WBC,NEU,EOS and TP. Medical staff should pay more attention to patients reported outcomes and inflammatory indexes in stable COPD,and take targeted intervention measures in the early stage to improve the quality of life of patients.