Background Bufei Huoxue Capsule (BHC) has gradually been used in clinical practice to treat patients with pneumoconiosis in recent years. However, the comprehensive evaluation of its efficacy and safety is lacking. Objective To systematically assess the therapeutic potential of BHC for pneumoconiosis. Methods By searching 9 databases, e.g. China National Knowledge Infrastructure, Wanfang Database, VIP Chinese Science and Technology Journals Database, SinoMed, Yiigle, PubMed, Embase, Cochrane Library, and Web of Science, randomized controlled trials (RCTs) related to the use of BHC for the treatment of pneumoconiosis were identified. The search covered the period from the inception of these databases to August 31, 2025. This systematic review conformed to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) 2020. The included studies underwent quality appraisal by the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE). Subgroup analysis and sensitivity analysis were applied to assess the robustness of results and explore the potential heterogeneity among the studies. Results A total of 19 RCTs were included and all were reported in Chinese. The therapy containing BHC showed a superior impact on forced expiratory volume in one second (FEV₁) (95%CI: 0.38, 0.66; I²=69%, P<0.05), FEV₁% (95%CI: 3.37, 5.72; I²=12%, P<0.05), forced vital capacity (FVC) (95%CI: 0.34, 0.45; I²=0%, P<0.05), FEV₁/FVC% (95%CI: 6.29, 10.35; I²=21%, P<0.05), 6-minute walking distance (6MWD) score (95%CI: 16.32, 27.64; I²=22%, P<0.05), St. George's Respiratory Questionnaire (SGRQ) score (95%CI: −1.47, −1.05; I²=13%, P<0.05), and total effective rate (95%CI: 2.71, 6.78; I²=0%, P<0.05) in patients with pneumoconiosis. In addition, BHC had less adverse reactions reported (95%CI: 0.69, 1.74; I²=0%, P=0.70). Conclusions Combined BHC therapy can significantly improve the lung function and the quality of life in patients with pneumoconiosis, with a good safety profile. However, high-quality RCTs with multicenter, large-sample, double-blind, and standardized protocols still need to be conducted in the future to provide more reliable evidence.

Objective:To construct and analyze the visual nomogram predictive model for the prognosis of elderly advanced lung adenocarcinoma patients after surgery based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods:SEER*Stat8.4.0.1 software was used to screen out the data from 17 register in SEER database between 2000 and 2019, and finally 4 453 lung adenocarcinoma patients aged ≥ 65 years who underwent surgical treatment and were diagnosed as stage Ⅲ and Ⅳ according to the 7th edition of the American Joint Committee on Cancer (AJCC) staging criteria were enrolled. The data were randomly divided into the training set (3 117 cases) and the validation set (1 336 cases) in a 7:3 ratio; the epidemilogical data and clinicopathological characteristics of the two groups were compared. LASSO regression was used for data dimensionality reduction to select the best predictors from the prognostic factors of patients. Cox proportional risk model was used to perform univariate and multivariate analyses of the screened variables, and based on R software rms package and the prognostic independent risk factors, the nomogram was constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) rates of the patients. The validation set was validated by using Bootstrap method with 1 000 equal repeated samples with playback, and the accuracy of the nomogram model was verified by using the C-index, receiving operating characteristic (ROC) curves and calibration curves.Results:There were no statistically significant differences in age, gender, race, tumor location, Grade grading, surgery methods, the number of lymph node dissection, radiotherapy, tumor diameter, tumor metastasis, marriage, living condition, TNM staging, radiochemotherapy of training set and validation set (all P > 0.05). In training set, 18 variables were included into LASSO regression analysis and were performed with dimensionality reduction; ultimately, 11 optimal predictive variables were selected, including age ≥ 85 years ( HR = 2.34, 95% CI: 1.803-3.037, P < 0.01), male ( HR = 1.326, 95% CI: 1.228-1.432, P < 0.01), Grade grading Ⅲ-Ⅳ ( HR = 1.333, 95% CI: 0.844-2.105, P < 0.01), undissected lymph nodes ( HR = 2.261, 95% CI: 2.023-2.527, P < 0.01), tumor diameter ≥3.7 cm ( HR = 1.445, 95% CI: 1.333-1.566, P < 0.01), bone metastasis ( HR = 1.535, 95% CI: 1.294-1.819, P < 0.01), brain metastasis ( HR = 1.308, 95% CI: 1.117-1.532, P < 0.01), lung metastasis ( HR = 1.229, 95% CI: 1.056-1.431, P = 0.01), living in rural areas ( HR = 1.215, 95% CI: 1.084-1.363, P < 0.01), TNM staging Ⅳ ( HR = 1.155, 95% CI: 1.044-1.278, P = 0.01), postoperative radiotherapy ( HR = 1.148, 95% CI: 1.054-1.250, P < 0.01); lung adenocarcinoma patients with the above 11 factors had worse prognosis. Based on the variables, the nomogram predictive model was constructed to predict 1-, 3-, and 5-year CSS rates of elderly advanced lung adenocarcinoma patients. Bootstrap method was used for repeated sampling for 1 000 times to verify the modeling effect of nomogram. In the model group, C-index was 0.654 (95% CI: 0.641-0.668), 0.666 (95% CI: 0.646-0.685), respectively in the training set and the validation set. The nomogram was drawn to predict ROC curves of 1-, 3-, and 5-year CSS rates for elderly advanced lung adenocarcinoma patients after operation in the training set and validation set; the area under the curve (AUC) of 1-year, 3-year, and 5-year CSS rates was 0.730 (95% CI: 0.708-0.754) and 0.689 (95% CI: 0.672-0.710), 0.687 (95% CI: 0.668-0.711) and 0.731 (95% CI: 0.697-0.765), 0.712 (95% CI:0.684-0.740) and 0.714 (95% CI: 0.683-0.745), respectively in the training and validation sets. The calibration curve showed a high consistency between the predicted probability of the model and the actual probability. Conclusions:The nomogram model constructed by optimal predictive variables for predicting the prognosis of elderly advanced lung adenocarcinoma patients after surgery may be a convenient tool for survival prediction of these patients.