Objective To analyze the status quo of exercise compliance in patients with chronic heart failure and its influencing factors.Methods Using convenient sampling method,425 patients with chronic heart failure in General Hospital of Ningxia Medical University from August 2022 to February 2023 were se-lected as the study objects.General data questionnaire,exercise compliance scale,exercise self-efficacy scale,heart-activity fear tampa scale,Pittsburgh sleep quality index,perceptive social support scale and psychological experience scale were used to investigate and analyze the results.Results 415 effective questionnaires were collected,with a recovery rate of 97.65%.The total score of exercise compliance in patients with chronic heart failure was 88.94±17.12.The results of multiple linear regression analysis showed that BMI,hospitalization times and fear of cardiac activity negatively affected patients'exercise compliance,while education level,exer-cise self-efficacy,perceptive social support and retirement positively affected patients'exercise compliance(P＜0.05).Conclusion The overall level of exercise compliance of patients with chronic heart failure is poor,and timely intervention measures should be taken.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective To develop and implement a drug treatment pathway for the initial treatment of diffuse large B-cell lymphoma(DLBCL)and to provide a foundation for refined medication use and cost control management under the Diagnosis Related Groups(DRG)payment system.Methods Clinical pharmacists collaborated to develop a drug treatment pathway for the initial treatment of DLBCL,utilizing evidence-based medicine and evidence-based pharmacy principles.The PDCA(Plan-Do-Check-Act)cycle method was employed for administrative intervention.The hematology department served as a pilot unit to assess the impact on economic indicators,including inpatient costs,drug expenses,and DRG payment balance,as well as treatment efficacy and the incidence of adverse reactions.Results Compared to the control group,the RG13 intervention group exhibited a significant reduction in average total hospitalization costs and drug expenses,along with a decreased DRG payment balance deficits.All differences were statistically significant(P＜0.05).Conclusion The development and implementation of a drug treatment pathway for the initial treatment of DLBCL can effectively reduce treatment costs,prevent DRG overspending,and alleviate the economic burden on patients,while ensuring the safety and effectiveness of the treatment.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective To develop and implement a drug treatment pathway for the initial treatment of diffuse large B-cell lymphoma(DLBCL)and to provide a foundation for refined medication use and cost control management under the Diagnosis Related Groups(DRG)payment system.Methods Clinical pharmacists collaborated to develop a drug treatment pathway for the initial treatment of DLBCL,utilizing evidence-based medicine and evidence-based pharmacy principles.The PDCA(Plan-Do-Check-Act)cycle method was employed for administrative intervention.The hematology department served as a pilot unit to assess the impact on economic indicators,including inpatient costs,drug expenses,and DRG payment balance,as well as treatment efficacy and the incidence of adverse reactions.Results Compared to the control group,the RG13 intervention group exhibited a significant reduction in average total hospitalization costs and drug expenses,along with a decreased DRG payment balance deficits.All differences were statistically significant(P＜0.05).Conclusion The development and implementation of a drug treatment pathway for the initial treatment of DLBCL can effectively reduce treatment costs,prevent DRG overspending,and alleviate the economic burden on patients,while ensuring the safety and effectiveness of the treatment.

Objective:To construct a machine learning model based on localization CT-based radiomics, dosiomics and clinical features for predicting radiotherapy response in non-small cell lung cancer (NSCLC) and validate its application value.Methods:A retrospective case series study was conducted. A total of 138 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022 were selected. The efficacy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1, and the patients were stratified according to the objective remission (complete remission+partial remission). Random stratified sampling was used to divide the 138 patients into a training group (96 cases) and an internal validation group (42 cases) at a ratio of 7∶3. Additionally, 33 patients who received radiotherapy at Jining Cancer Hospital from January 2019 to December 2022 were included as the external validation group. Based on the pre-radiotherapy data of the radiotherapy planning system, PyRadiomics software package was used to extract 107 radiomics features and 107 dosiomics features for each patient. Pearson correlation analysis and LASSO regression analysis were used for dimensionality reduction screening; the final selected features were weighted and integrated to generate radiomics-dosiomics scores (RDS), which were then input into logistic regression (LR), support vector machine (SVM), extremely randomized forest (Extra Trees), K-nearest neighbor algorithm (KNN), lightweight gradient boosting machine (Light GBM), and multi-layer perceptron (MLP) machine learning algorithms to construct 6 radiomics-dosiomics models (RDM) for predicting the objective remission. RECIST 1.1 standard was used to evaluate objective remission as the gold standard, receiver operating characteristic (ROC) curve of 6 RDM for predicting objective remission was plotted, and the optimal algorithm for RDM was selected. Univariate and multivariate logistic regression were performed on demographic characteristics, hematological indicators and radiotherapy parameters of the training group to screen independent risk factors for NSCLC patients who received radiotherapy but did not achieve objective remission. These factors were input into the optimal machine learning algorithm to construct a clinical model (CM). Combined with features from RDS and CM, the clinical feature-radiomics-dosiomics combined model (CRDM) was established, and the nomogram of the model for predicting objective remission in NSCLC patients with radiotherapy was drawn. ROC curves were used to evaluate the efficacy of CM, RDM and CRDM in predicting the objective remission in NSCLC patients with radiotherapy in the training group, internal validation group and external validation group.Results:Four radiomics features (including grayscale variance, low grayscale long-range operation emphasis, low grayscale area emphasis, and small area low grayscale area emphasis, all of which were texture features) and 6 dosiomics features [including 1 first-order feature (robust mean absolute deviation), 4 texture features (grayscale non-uniformity, large area emphasis, large area high grayscale emphasis, contrast) and 1 shape feature (shortest axis length)] were selected. ROC curve analysis showed that the area under the curve (AUC) of the RDM constructed using SVM algorithm for judging the objective remission in the training group and the internal validation group was 0.907 (95% CI: 0.836-0.977) and 0.822 (95% CI: 0.685-0.959), which were higher than RDM constructed using other algorithms, and the sensitivity (96.2% and 91.7%), specificity (78.6% and 76.7%) and accuracy (83.3% and 81.0%) at the optimal cut-off values were all higher. Considering the stability and generalization ability of the model, SVM algorithm was ultimately used to construct RDM, CM and CRDM uniformly. Based on training group data, univariate and multivariate logistic regression analysis showed that elevated platelet-to-lymphocyte ratio (PLR) ( OR = 1.001, 95% CI: 1.000-1.003, P = 0.035) and increased target volume of radiotherapy plan ( OR = 1.001, 95% CI: 1.000-1.001, P = 0.008) were independent risk factors for failure to achieve objective remission. ROC curve analysis showed that in the training group and the internal validation group, the AUC of CRDM predicting objective remission were 0.914 (95% CI: 0.856-0.972) and 0.864 (95% CI: 0.754-0.974), respectively, which were better than CM [AUC were 0.735 (95% CI: 0.612-0.857) and 0.697 (95% CI: 0.507-0.888)] and RDM, respectively. In the external validation group, the AUC of CRDM, CM and RDM were 0.778 (95% CI: 0.500-1.000), 0.667 (95% CI: 0.434-0.899) and 0.741 (95% CI: 0.463-1.000), respectively. Conclusions:The CRDM constructed by combining radiomics, dosiomics and clinical features can comprehensively and accurately evaluate the radiotherapy response of NSCLC patients, and may have important clinical application value in achieving precision medicine and optimizing treatment strategies.

Objective:To observe the role of Huaiqihuang Granules (HQ) in the long-term management of bronchial asthma in young children, and the effective effect on concomitant rhinitis.Methods:A prospective real-world multicenter study was conducted in children aged 2-5 years with asthma diagnosed in the outpatient department (from April 2016 to March 2019)who received either inhaled corticosteroid (ICS)/leukotriene receptor antagonist (LTRA)(control group); inhaled ICS/LTRA plus HQ(combination group), or HQ alone(HQ group). All patients were followed up at week 4, 8, 12 after treatment. The number of days with asthma symptoms, the frequency of severe asthma attacks, the level of asthma control, and the days with rhinitis symptoms in the last 4 weeks were recorded. Differences before and after treatment, and those among groups after treatment were compared using Kruskal- Wallis H test or Wilcoxon rank-sum test. Results:A total of 2 234 eligible patients were recruited, and 2 147 cases completed followed-up visits, including 477, 1 374 and 296 cases in the control group, combination group, and HQ group, respectively. After the treatment, all 3 groups showed significant declines in the days with asthma symptoms, frequency of severe asthma attack and the days with rhinitis symptoms (all P<0.01), and the rate of well-controlled asthma increased significantly ( P<0.01). It lasted until the end of follow-up. Among groups, patients in the combination group showed significantly less days of asthma symptoms than those of the other 2 group at week 8 and 12[0(0, 0.9) d vs.0(0, 0.3) d, P<0.05; 0(0, 0.1) d vs. 0(0, 1.0) d, P<0.01]. Patients in the combination group and HQ group showed a significantly lower rate of severe asthma attacks than that of the control group at week 12 [0(0, 1), 0(0, 1), 0(0, 2), all P<0.05]. The well-controlled rate of asthma in the combination group was significantly higher than that of the control group and HQ group at week 8 and 12 (89.6% vs. 85.9% vs.82.1%, H=15.28; 90.9% vs. 84.1% vs. 81.8%, χ2=29.32, all P<0.01). Conclusions:HQ can significantly alleviate symptoms of asthma and rhinitis, severe attack of asthma, and increase the control rate of asthma when used as an additional treatment or used alone.

Objective:To analyze the clinical value of noninvasive pressure strain loop (PSL) in assessing left ventricular myocardial work in patients with essential hypertension.Methods:In this cross-sectional study, 66 patients with essential hypertension who were admitted to the Affiliated Hospital of Yangzhou University from August to December 2020 were continuously enrolled. According to left ventricular mass index (LVMI) >95 g/m 2 in women and >115 g/m 2 in men,≤95 g/m 2 in women and ≤115 g/m 2 in men, the 66 patients were divided into left ventricular hypertrophy (LVH) group (14 cases) and non-left ventricular hypertrophy (NLVH) group (52 cases). Furthermore, the NLVH group was divided into a mild group (30 cases) and a moderate/severe group (22 cases) according to the systolic blood pressure of 140~159 mmHg (1 mmHg=0.133 kPa) and ≥160 mmHg. Another 25 healthy adults who underwent physical examination during the same period were included as healthy control group. The height, weight and blood pressure were measured in all the subjects, and routine echocardiography and speckle tracking imaging analysis were performed. PSL results were obtained by combining the results of speckle tracking imaging analysis with systolic blood pressure. The differences of general clinical data, basic parameters of two-dimensional ultrasound and myocardial work parameters of PSL (global work index, global effective work, global wasted work, and global work efficiency) were compared among the groups, and the clinical value of PSL in assessing left ventricular myocardial work in patients with essential hypertension was analyzed. Results:There was no significant difference in left ventricular ejection fraction among all groups (all P>0.05). The global work index of moderate/severe NLVH group was significantly higher than that of mild NLVH group, LVH group and healthy control group [(2 630±231) vs (2 254±179), (1 847±261), (1 724±209) mmHg%]. The global effective work of moderate/severe NLVH group was significantly higher than that of LVH group and healthy control group [(2 965±261) vs (2 330±258) and (2 121±163) mmHg%] (all P<0.05). The global wasted work of LVH group was significantly higher than that of moderate/severe NLVH group, mild NLVH group and healthy control group [(248±107) vs (141±57), (116±57), (83±58) mmHg%] (all P<0.05). The global work efficiency was significantly lower than that of moderate/severe NLVH group, mild NLVH group and healthy control group (89.1%±3.9% vs 94.3%±1.9%, 95.0%±1.8%, 95.8%±2.3%) (all P<0.05). With the increase of blood pressure, the PSL decreased in the LVH group and increased in the other three groups. The bull′s eye diagram of myocardial work in the healthy control group was uniform green (normal effective work area), red began to appear in the mild NLVH group (high intensity myocardial work area), red area increased in the moderate/severe NLVH group, and blue appeared in the LVH group (ineffective work area). Conclusions:PSL has good clinical value in assessing left ventricular myocardial work in patients with primary hypertension. The parameters derived from PSL data can sensitively identify impaired systolic function in individuals with normal left ventricular ejection fraction.

Background::Erythropoietin is a glycoprotein that mainly regulates erythropoiesis. In patients with chronic renal failure with anemia, darbepoetin alfa can stimulate erythropoiesis, correct anemia, and maintain hemoglobin levels. This study was designed to demonstrate the efficacy and safety of darbepoetin alfa injections as being not inferior to epoetin alfa injections (Recombinant Human Erythropoietin injection, rHuEPO) when maintaining hemoglobin (Hb) levels within the target range (10.0-12.0 g/dL) for the treatment of renal anemia.Methods::Ninety-five patients were enrolled in this study from April 15, 2013 to April 10, 2014 at 25 sites. In this study, patients ( n = 95) aged 18-70 years were randomized into a once per week intravenous darbepoetin alfa group ( n = 56) and a twice or three times per week intravenous epoetin alfa group ( n = 39) for 28 weeks, who had anemia with hemoglobin levels between 6 g/dL and 10 g/dL due to chronic kidney disease (CKD) and were undergoing hemodialysis or hemofiltration with ESA-naive (erythropoiesis stimulating agent-naive). The primary efficacy profile was the mean Hb level (the non-inferiority margin was -1.0 g/dL, week 21-28); the secondary efficacy profiles were the Hb increase rate (week 0-4), the target Hb achievement cumulative rate and time, the change trends of the Hb levels, and the target Hb maintenance ratio. Adverse events (AEs) were observed and compared, and the efficacy and safety were analyzed between the two treatment groups. Additionally, the frequencies of dose adjustments between the darbepoetin alfa and epoetin alfa groups were compared during the treatment period. SAS? software version 9.2 was used to perform all statistical analyses. Descriptive statistics were used for all efficacy, safety, and demographic variable analyses, including for the primary efficacy indicators. Results::The mean Hb level was 11.3 g/dL in the darbepoetin alfa group and 10.7 g/dL in the epoetin alfa group, respectively; the difference of the lower limits of the 95% confidence intervals (CI) between the two groups was 0.1 g/dL (>-1.0 g/dL), and non-inferiority was proven; the Hb levels started to increase in the first four weeks at a similar increase rate; no obvious differences were observed between the groups in the target Hb achievement cumulative rates, and the Hb levels as well as the target Hb level maintenance rate changed over time. The incidence of AEs was 62.5% in the darbepoetin alfa group and 76.9% in the epoetin alfa group. All the adverse events observed in the study were those commonly associated with hemodialysis.Conclusion::Darbepoetin alfa intravenously once per week can effectively increase Hb levels and maintain the target Hb levels well, which makes it not inferior to epoetin alfa intravenously twice or three times per week. Darbepoetin alfa shows an efficacy and safety comparable to epoetin alfa for the treatment of renal anemia.