Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective To explore the value of analysis on the incidence of healthcare-associated infection(HAI)based on disease diagnosis-related grouping(DRG),case mix index(CMI),and relative weight(RW).Methods All discharged cases,DRG and HAI status in a tertiary first-class general hospital from January 1 to December 31,2023 were analyzed retrospectively.Incidences of HAI in different departments were adjusted and compared by CMI.Incidences of HAI in different DRG groups were adjusted by RW.Results Among the 47 695 cases included in the analysis,757 were HAI cases,including 225 DRG groups.The department of critical care medicine had the highest incidence of HAI(11.98％).After CMI adjustment,departments with higher incidence of HAI were main-ly the department of respiratory and critical care medicine(3.96％),department of critical care medicine(3.04％),and department of neurology(2.85％),et al.DRG groups with the top five high incidence of HAI were AH11(tracheotomy and with ventilator support ≥96 hours or extracorporeal membrane oxygenation[ECMO],accompa-nied by major complications and comorbidity[MCC],50.00％),BC29(ventricular shunt and revision surgery,31.43％),BB21(craniotomy other than trauma,accompanied by MCC,27.56％),BB11(craniotomy of brain trauma,accompanied by MCC,26.32％),and GB1A(major surgery of esophagus,stomach,and duodenum,accompanied by major or moderate complications and comorbidity,16.00％).After RW adjustment,the DRG groups with the top five high incidence of HAI were ES21(respiratory system infection/inflammation,accompanied by MCC,5.89％),BR21(cerebral ischemic disease,accompanied by MCC,5.17％),FR11(heart failure,shock,accompanied by MCC,4.80％),BC29(4.57％)and AH11(3.57％).Conclusion Analyzing the incidence of HAI based on CMI and RW can help to identify key departments and disease groups for infection prevention and control,and provide reference for precise prevention and control of HAI in the new era.

Objective:To establish and validate a lipid parameter-based prognostic model for predicting recurrence free survival (RFS) in pancreatic cancer patients receiving postoperative adjuvant chemotherapy.Methods:A retrospective analysis was conducted on the clinical and pathological data of 155 patients who underwent pancreatic cancer resection followed by adjuvant chemotherapy at Affiliated Hospital of Qingdao University between January 2019 and December 2022. The patients were randomly divided into a training set ( n=108) and a validation set ( n=47) in a 7∶3 ratio. X-tile software was used to determine cutoff values for lipid parameters. Univariate and multivariate Cox regression analyses were performed to construct a model predicting RFS, which was then visualized using a nomogram. The model's predictive performance, accuracy and stability, and clinical application value were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. Individual risk scores for recurrence were calculated based on the nomogram model, and X-tile software was employed to identify optimal cutoff values for risk stratification, which was used to divide patients into low-risk and high-risk groups. Survival differences between two groups were analyzed using survival curves. Results:Among lipid parameters, patients with higher apolipoprotein A1 level had obviously longer RFS than those with low apolipoprotein A1 level (10.17 months vs 8.92 months, HR=0.397, 95% CI 0.237~0.664); patients with high total cholesterol level had obviously shorter RFS than those with low total cholesterol level (8.33 months vs 16.27months, HR=3.382, 95% CI 1.901~5.824) ; patients with high low-density lipoprotein level had obviously shorter RFS than those with low low-density lipoprotein level (8.53 months vs 11.43 months, HR=1.617, 95% CI 1.013~2.582) ; patients with high lipoprotein(a) had shorter RFS than those with low lipoprotein(a) (8.53 months vs 14.43 months, HR=2.640, 95% CI 1.514-4.604) ; and all the differences were statistical significant (all P value <0.05). Univariate Cox regression analysis identified advanced T stage, advanced N stage, high total cholesterol level, high low-density lipoprotein level, low apolipoprotein A1 level, high apolipoprotein B level, and high lipoprotein(a) level as risk factors for RFS. Multivariate Cox regression analysis revealed that tumors located in the pancreatic body or tail ( HR=0.63, 95% CI 0.36-0.86, P=0.042), advanced T stage ( HR=4.85, 95% CI 1.47-16.04, P=0.010), advanced N stage ( HR=0.48, 95% CI 0.26-0.87, P=0.015), elevated total cholesterol levels ( HR=3.61, 95% CI 1.46-8.91, P=0.005), high density lipoprotein levels ( HR=0.48, 95% CI 0.26-0.87, P=0.015), and elevated lipoprotein(a) levels ( HR=3.17, 95% CI 1.61-6.24, P<0.001) were independent risk factors for RFS. The nomogram model incorporating these six factors above demonstrated an AUC of 0.78 (95% CI 0.70-0.87) in the training set and 0.75 (95% CI 0.59-0.91) in the validation set. Calibration curves indicated a high degree of agreement between predicted and observed outcomes. DCA suggested that the model provides substantial clinical benefit. Kaplan-Meier survival curve analysis showed that patients in the high-recurrence risk group from training set and validation set both had significantly shorter RFS compared to those in the low-recurrence risk group (6.93 months vs 12.13 months, HR=4.024, 95% CI 2.594-6.243; 6.85 months vs 11.93 months, HR=2.314, 95% CI 1.227-4.362); and all the differences were statistical significant (all P value <0.05). Conclusions:The nomogram model based on lipid parameters can effectively predict recurrence free survival in patients undergoing adjuvant chemotherapy after pancreatic cancer surgery.

The aim of this paper is to present the basic principles of the mesh basket-like six-electrode renal denervation (RDN) system, research progress in anti-hypertensive therapy and modulation of autonomic nerves for the treatment of diseases. It discusses the advances in system design, operational optimization and expansion of clinical applications, as well as the challenges faced. The future prospects of personalized autonomic nerves modulation for the treatment of diseases are also discussed.

Objective:To explore the correlation and predictive value of the blood urea nitrogen to serum albumin ratio (BAR) in the development of sepsis among patients with acute-on-chronic liver failure (ACLF).Methods:A total of 410 patients diagnosed with ACLF who were admitted to Wuxi Fifth People′s Hospital between January 1st, 2020 and December 31st, 2024 were enrolled in this study. Demographic information, laboratory test indicators, and other clinical data were retrospectively analyzed. Participants were stratified into two groups using a 6∶4 allocation ratio, comprising a training set of 246 patients and a validation set of 164 patients, the clinical data of two groups were compared. Logistic regression was employed to evalute the influencing factors of sepsis during hospitalization in ACLF patients. Additionally, the predictive value of different factors for sepsis occurrence was evaluated using receiver-operating characteristic curve analysis. DeLong test was used to compare the area under the curve.Results:The comparison of baseline data between the training set and the validation set revealed no statistically significant differences (all P>0.05). A total of 197 sepsis cases were observed during the study period. Multivariate logistic regression analysis revealed that both BAR and the sequential organ failure assessment (SOFA) score were independent influencing factors for sepsis development in ACLF patients (odds ratio ( OR)=1.274, 95% confidence interval (95% CI) 1.075 to 1.510, P=0.005; OR=1.142, 95% CI 1.038 to 1.256, P=0.006). In the training set, the area under the curve (AUC) of BAR for predicting sepsis in ACLF patients was 0.802, which was superior to that of the SOFA score (AUC=0.706) ( Z=2.16, P=0.031). The validation set showed the predictive ability of BAR with an AUC of 0.726, which was superior to the SOFA score′s performance (AUC=0.606) ( Z=2.28, P=0.023). Conclusions:BAR could independently predict sepsis development in ACLF patients with significant prognostic value. BAR could be used as a clinically useful biomarker for sepsis risk stratification.

Objective:To establish and validate a lipid parameter-based prognostic model for predicting recurrence free survival (RFS) in pancreatic cancer patients receiving postoperative adjuvant chemotherapy.Methods:A retrospective analysis was conducted on the clinical and pathological data of 155 patients who underwent pancreatic cancer resection followed by adjuvant chemotherapy at Affiliated Hospital of Qingdao University between January 2019 and December 2022. The patients were randomly divided into a training set ( n=108) and a validation set ( n=47) in a 7∶3 ratio. X-tile software was used to determine cutoff values for lipid parameters. Univariate and multivariate Cox regression analyses were performed to construct a model predicting RFS, which was then visualized using a nomogram. The model's predictive performance, accuracy and stability, and clinical application value were evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), respectively. Individual risk scores for recurrence were calculated based on the nomogram model, and X-tile software was employed to identify optimal cutoff values for risk stratification, which was used to divide patients into low-risk and high-risk groups. Survival differences between two groups were analyzed using survival curves. Results:Among lipid parameters, patients with higher apolipoprotein A1 level had obviously longer RFS than those with low apolipoprotein A1 level (10.17 months vs 8.92 months, HR=0.397, 95% CI 0.237~0.664); patients with high total cholesterol level had obviously shorter RFS than those with low total cholesterol level (8.33 months vs 16.27months, HR=3.382, 95% CI 1.901~5.824) ; patients with high low-density lipoprotein level had obviously shorter RFS than those with low low-density lipoprotein level (8.53 months vs 11.43 months, HR=1.617, 95% CI 1.013~2.582) ; patients with high lipoprotein(a) had shorter RFS than those with low lipoprotein(a) (8.53 months vs 14.43 months, HR=2.640, 95% CI 1.514-4.604) ; and all the differences were statistical significant (all P value <0.05). Univariate Cox regression analysis identified advanced T stage, advanced N stage, high total cholesterol level, high low-density lipoprotein level, low apolipoprotein A1 level, high apolipoprotein B level, and high lipoprotein(a) level as risk factors for RFS. Multivariate Cox regression analysis revealed that tumors located in the pancreatic body or tail ( HR=0.63, 95% CI 0.36-0.86, P=0.042), advanced T stage ( HR=4.85, 95% CI 1.47-16.04, P=0.010), advanced N stage ( HR=0.48, 95% CI 0.26-0.87, P=0.015), elevated total cholesterol levels ( HR=3.61, 95% CI 1.46-8.91, P=0.005), high density lipoprotein levels ( HR=0.48, 95% CI 0.26-0.87, P=0.015), and elevated lipoprotein(a) levels ( HR=3.17, 95% CI 1.61-6.24, P<0.001) were independent risk factors for RFS. The nomogram model incorporating these six factors above demonstrated an AUC of 0.78 (95% CI 0.70-0.87) in the training set and 0.75 (95% CI 0.59-0.91) in the validation set. Calibration curves indicated a high degree of agreement between predicted and observed outcomes. DCA suggested that the model provides substantial clinical benefit. Kaplan-Meier survival curve analysis showed that patients in the high-recurrence risk group from training set and validation set both had significantly shorter RFS compared to those in the low-recurrence risk group (6.93 months vs 12.13 months, HR=4.024, 95% CI 2.594-6.243; 6.85 months vs 11.93 months, HR=2.314, 95% CI 1.227-4.362); and all the differences were statistical significant (all P value <0.05). Conclusions:The nomogram model based on lipid parameters can effectively predict recurrence free survival in patients undergoing adjuvant chemotherapy after pancreatic cancer surgery.

Objective:To explore the correlation and predictive value of the blood urea nitrogen to serum albumin ratio (BAR) in the development of sepsis among patients with acute-on-chronic liver failure (ACLF).Methods:A total of 410 patients diagnosed with ACLF who were admitted to Wuxi Fifth People′s Hospital between January 1st, 2020 and December 31st, 2024 were enrolled in this study. Demographic information, laboratory test indicators, and other clinical data were retrospectively analyzed. Participants were stratified into two groups using a 6∶4 allocation ratio, comprising a training set of 246 patients and a validation set of 164 patients, the clinical data of two groups were compared. Logistic regression was employed to evalute the influencing factors of sepsis during hospitalization in ACLF patients. Additionally, the predictive value of different factors for sepsis occurrence was evaluated using receiver-operating characteristic curve analysis. DeLong test was used to compare the area under the curve.Results:The comparison of baseline data between the training set and the validation set revealed no statistically significant differences (all P>0.05). A total of 197 sepsis cases were observed during the study period. Multivariate logistic regression analysis revealed that both BAR and the sequential organ failure assessment (SOFA) score were independent influencing factors for sepsis development in ACLF patients (odds ratio ( OR)=1.274, 95% confidence interval (95% CI) 1.075 to 1.510, P=0.005; OR=1.142, 95% CI 1.038 to 1.256, P=0.006). In the training set, the area under the curve (AUC) of BAR for predicting sepsis in ACLF patients was 0.802, which was superior to that of the SOFA score (AUC=0.706) ( Z=2.16, P=0.031). The validation set showed the predictive ability of BAR with an AUC of 0.726, which was superior to the SOFA score′s performance (AUC=0.606) ( Z=2.28, P=0.023). Conclusions:BAR could independently predict sepsis development in ACLF patients with significant prognostic value. BAR could be used as a clinically useful biomarker for sepsis risk stratification.

The STAR tool was used to evaluate and analyze the science, transparency, and applicability of Chinese pathology guidelines and consensus published in medical journals in 2022. There were a total of 18 pathology guidelines and consensuses published in 2022, including 1 guideline and 17 consensuses. The results showed that the guideline score was 21.83 points, lower than the overall guideline average (43.4 points). Consensus ratings scored an average of 27.87 points, on par with the overall consensus level (28.3 points). Areas that scored above the overall level were "conflict of interest" and "working groups", while areas that scored below the overall level were "proposals", "funding", "evidence", "consensus approaches" and "accessibility". To sum up, the formulation of pathology guidelines and consensuses in 2022 is not standardized, and the evidence retrieval process, evidence evaluation methods and grading criteria for recommendations on clinical issues are not provided in the formulation process; the process and method for reaching consensus are not provided, the plan is lacking, and registration is not carried out. It is therefore suggested that guidelines/consensus makers in the field of pathology should attach importance to evidence-based medical evidence, strictly follow guideline formulation methods and processes, further improve the scientific, applicable and transparent guidelines/consensuses in the field, and better provide support for clinicians and patients.

Objectives:To assess the prognostic impact of the neoadjuvant rectal (NAR) score following neoadjuvant short-course radiotherapy and consolidation chemotherapy in locally advanced rectal cancer (LARC), as well as its value in guiding decisions for adjuvant chemotherapy.Methods:Between August 2015 and August 2018, patients were eligible from the STELLAR phase III trial (NCT02533271) who received short-course radiotherapy plus consolidation chemotherapy and for whom the NAR score could be calculated. Based on the NAR score, patients were categorized into low (＜8), intermediate (8-16), and high (＞16) groups. The Kaplan-Meier method, log rank tests, and multivariate Cox proportional hazard regression models were used to evaluate the impact of the NAR score on disease-free survival (DFS).Results:Out of the 232 patients, 24.1%, 48.7%, and 27.2% had low (56 cases), intermediate (113 cases), and high NAR scores (63 cases), respectively. The median follow-up period was 37 months, with 3-year DFS rates of 87.3%, 68.3%, and 53.4% ( P＜0.001) for the low, intermediate, and high NAR score groups. Multivariate analysis demonstrated that the NAR score (intermediate NAR score: HR, 3.10, 95% CI, 1.30-7.37, P=0.011; high NAR scores: HR=5.44, 95% CI, 2.26-13.09, P＜0.001), resection status ( HR, 3.00, 95% CI, 1.64-5.52, P＜0.001), and adjuvant chemotherapy ( HR, 3.25, 95% CI, 2.01-5.27, P＜0.001) were independent prognostic factors for DFS. In patients with R0 resection, the 3-year DFS rates were 97.8% and 78.0% for those with low and intermediate NAR scores who received adjuvant chemotherapy, significantly higher than the 43.2% and 50.6% for those who did not ( P＜0.001, P=0.002). There was no significant difference in the 3-year DFS rate (54.2% vs 53.3%, P=0.214) among high NAR score patients, regardless of adjuvant chemotherapy. Conclusions:The NAR score is a robust prognostic indicator in LARC following neoadjuvant short-course radiotherapy and consolidation chemotherapy, with potential implications for subsequent decisions regarding adjuvant chemotherapy. These findings warrant further validation in studies with larger sample sizes.

Objectives:To assess the prognostic impact of the neoadjuvant rectal (NAR) score following neoadjuvant short-course radiotherapy and consolidation chemotherapy in locally advanced rectal cancer (LARC), as well as its value in guiding decisions for adjuvant chemotherapy.Methods:Between August 2015 and August 2018, patients were eligible from the STELLAR phase III trial (NCT02533271) who received short-course radiotherapy plus consolidation chemotherapy and for whom the NAR score could be calculated. Based on the NAR score, patients were categorized into low (＜8), intermediate (8-16), and high (＞16) groups. The Kaplan-Meier method, log rank tests, and multivariate Cox proportional hazard regression models were used to evaluate the impact of the NAR score on disease-free survival (DFS).Results:Out of the 232 patients, 24.1%, 48.7%, and 27.2% had low (56 cases), intermediate (113 cases), and high NAR scores (63 cases), respectively. The median follow-up period was 37 months, with 3-year DFS rates of 87.3%, 68.3%, and 53.4% ( P＜0.001) for the low, intermediate, and high NAR score groups. Multivariate analysis demonstrated that the NAR score (intermediate NAR score: HR, 3.10, 95% CI, 1.30-7.37, P=0.011; high NAR scores: HR=5.44, 95% CI, 2.26-13.09, P＜0.001), resection status ( HR, 3.00, 95% CI, 1.64-5.52, P＜0.001), and adjuvant chemotherapy ( HR, 3.25, 95% CI, 2.01-5.27, P＜0.001) were independent prognostic factors for DFS. In patients with R0 resection, the 3-year DFS rates were 97.8% and 78.0% for those with low and intermediate NAR scores who received adjuvant chemotherapy, significantly higher than the 43.2% and 50.6% for those who did not ( P＜0.001, P=0.002). There was no significant difference in the 3-year DFS rate (54.2% vs 53.3%, P=0.214) among high NAR score patients, regardless of adjuvant chemotherapy. Conclusions:The NAR score is a robust prognostic indicator in LARC following neoadjuvant short-course radiotherapy and consolidation chemotherapy, with potential implications for subsequent decisions regarding adjuvant chemotherapy. These findings warrant further validation in studies with larger sample sizes.