Objective:To identify the influencing factors for acute kidney injury (AKI) in patients undergoing interventional embolization of intracranial aneurysms under general anesthesia and develop a predictive model.Methods:In this retrospective study, the clinical data from patients who underwent elective interventional embolization of intracranial aneurysms at Hospital Affiliated to Nanjing Medical University from January 2019 to December 2023 were collected. All the patients were divided into AKI group and non-AKI group based on whether AKI occurred postoperatively. The general data, preoperative laboratory parameters, and intraoperative parameters of the patients were obtained through the electronic medical record system and operation-anesthesia management system. The variables screened using LASSO regression were included in the multivariate logistic regression analysis to identify the risk factors for AKI following embolization of cerebrovascular aneurysms. Based on the results, a predictive model was established and evaluated.Results:A total of 428 patients were collected, with 73 in AKI group and 355 in non-AKI group. LASSO regression for variable selection and multivariate logistic regression analysis showed that serum creatinine concentration, duration of surgery and age were independent risk factors, and the glomerular filtration rate and hemoglobin concentration were protective factors for AKI after interventional embolization of intracranial aneurysms ( P<0.05). The area under the receiver operating characteristic curve of the prediction model was 0.860 (95% confidence interval 0.815 to 0.905), with a sensitivity of 0.880 and a specificity of 0.718. The calibration curve showed a mean absolute error of 0.014. The Hosmer-Lemeshow goodness-of-fit test indicated that there was no statistically significant difference between the predicted and observed values of the model ( χ2=3.29, P=0.915). The clinical decision curve demonstrated that the net benefit rate for patients was higher when the threshold probability ranged from 0 to 0.83. Conclusions:The glomerular filtration rate, serum creatinine concentration, hemoglobin concentration, duration of surgery and age are influencing factors for AKI following interventional embolization of intracranial aneurysms, and the prediction model established based on these factors demonstrates good predictive performance.

In recent years,the microorganisms residues in endoscopes have frequently resulted in cross transmis-sion or even the outbreak of hospital-associated infections.It is of great importance to carry out standardized bio-logical surveillance of endoscopes,find out the high-risk links of cleaning and disinfection,and take targeted inter-vention measures to reduce the incidence of endoscopy-related infection.Based on the related guidelines in China and abroad as well as the latest clinical practice researches,the biological surveillance sampling and culture for en-doscopes were summarized in the article so as to enhance the surveillance quality,ensure the reprocessing effect and guarantee the endoscopy-related quality and safety.

OBJECTIVE To explore the distribution and drug resistance of the pathogens causing the abdominal(pelvic)soft tissue infections in secondary or above medical institutions of Suzhou so as to provide bases for pre-vention and control of the infections.METHODS The surveillance data of abdominal(pelvic)soft tissue infections that were reported regularly from 58 member institutions of Suzhou from Jan.2020 to Dec.2023 were collected from the regional nosocomial infection surveillance platform by Suzhou nosocomial infection management and qual-ity control center.Totally 26 tertiary hospitals and 32 secondary hospitals were involved.RESULTS Most of the 1178 strains of pathogens were isolated from the tertiary hospitals,the proportion of gram-negative bacteria was the highest;Escherichia coli,Klebsiella pneumoniae and Enterococcus faecium ranked the top 3 species.The constituent ratio of carbapenem-resistant Klebsiella pneumoniae(CRKP)strains the was highest among the mul-tidrug-resistant organisms.The K.pneumoniae and CRKP strains were sensitive to tigecycline;the E.coli strains were highly sensitive to carbapenems,minocycline and piperacillin-tazobactam;Stenotrophomonas maltophilia strains were highly resistant to most of the antibiotics;Enterobacter cloacae strains were highly resistant to ampi-cillin-sulbactam but were highly sensitive to carbapenems;the drug resistance rate of the A.baumannii strains to tigecycline was less than 5％;the drug resistance rate of Pseudomonas aeruginosa strains to ticarcillin-clavulanic acid was highest.CONCLUSIONS The abdominal(pelvic)soft tissue infection is always mixed infections.The pathogens show severe drug resistance.It is necessary to strengthen the surveillance of etiological spectrum and drug resistance and conduct targeted guidance for clinical practice of diagnosis and treatment.

OBJECTIVE To investigate the characteristics of multidrug-resistant organisms(MDROs)infections in the patients of intensive care units(ICUs)so as to provide supportive data for precise prevention and control of in-fections.METHODS From Jan.2016 to Dec.2023,the case-time incidence of hospital-associated MDROs infec-tions,constituent ratios,infection sites and characteristics of drug resistance were observed and compared among the patients from 5 different ICUs of the First Affiliated Hospital of Suzhou University by means of real-time sur-veillance system of hospital-associated infection.RESULTS Among the ICU patients,there were 1322 case-times of hospital-associated infections with MDROs,and the case-time incidence rate was 4.67％,which varied in the ICUs.The patients with lower respiratory tract infections accounted for 66.51％,the patients with bloodstream infections 14.53％,the patients with urinary tract infections 10.78％.The distribution of infection sites,distribu-tion of pathogens and etiological spectrum of infection sites varied according to the ICUs.In general,the percenta-ges of carbapenem-resistant Klebsiella pneumoniae(CRKP)and carbapenem-resistant Acinetobacter baumannii(CRAB)infections were highest,which were 35.63％(471/1322)and 38.28％(506/1322),respectively.The drug resistance rates of the CRKP strains to polymyxin B and ceftazidime-avibactam were low,and the drug re-sistance rate to tigecycline was 38.44％.The drug resistance rates of the CRAB strains to polymyxin B and tigecy-cline were 10.00％and 21.64％,respectively.The carbapenem-resistant Pseudomonas aeruginosa(CRPA)strains showed high sensitivity to most of the antibiotics.The drug resistance rates of the methicillin-resistant Staphylococcus aureus(MRSA)strains to tetracycline,levofloxacin,gentamicin,moxifloxacin and ciprofloxacin were high.CONCLUSION It is necessary for the hospital to reasonably use antibiotics based on the result of sur-veillance of MDROs in the ICUs and take targeted prevention measures for the infections.

To enhance the emergency response capabilities of medical institutions and community public health,and strengthen the efficiency in handling major sudden infectious disease events.METHODS Relying on community grid management and under the integration of medical treatment and disease prevention,a multi-level medical service system was established,funding guarantees were implemented,and software and hardware supports were strengthened by emphasizing infection control training and introducing modern technological means like artifi-cial intelligence.In addition,stratified screening for infectious diseases among different populations was carried out in an orderly manner.RESULTS The grid management model for infectious diseases based on the integration of medical treatment and disease prevention in Suzhou was preliminarily formed and applied to the screening and treatment of COVID-19 and hepatitis C.The epidemic prevention teams,jointly established by medical institutions and community-based medical and preventive organizations,were critical for emergency response to major sud-den infectious diseases and became a key component of epidemic prevention and control.CONCLUSION The grid management strategy for infectious diseases based on the integration of medical treatment and disease preven-tion in Suzhou demonstrates certain reference value in enhancing emergency response capabilities and efficiency for major sudden infectious diseases,as well as in the field of disease prevention and control.

OBJECTIVE To investigate the current status and risk factors of carbapenem-resistant gram-negative or-ganisms(CRO)infections in neurosurgical ICU patients,and to construct and validate their prediction models.METHODS Clinical data and active screening microbiological results of 113 patients admitted to the Neurosurgical Intensive Care Unit(ICU)of The First Affiliated Hospital of Soochow University between Jul.2023 and Jan.2024 were retrospectively collected,and the patients were divided into a CRO-infected group(n=28)and a non-CRO-infected group(n=85).Predictive variables were screened using LASSO regression and logistic regression.Risk prediction models were constructed using random forest(RF)and logistic regression,the performance of the mod-el was evaluated by analyzing the area under the receiver operating characteristic(ROC)curve(AUC),calibration curves,and decision curves,and internal validation was performed using the bootstrap resampling method.RESULTS Among 113 neurosurgical ICU patients,28 cases developed CRO infections,with an infection rate of 24.78％.The highest infection rate was observed in lower respiratory tract infection,with 17 cases(15.04％).The predominant CRO pathogens were carbapenem-resistant Klebsiella pneumoniae(CRKP)and carbapenem-re-sistant Acinetobacter baumannii(CRAB),accounting for 50.00％and 42.86％of cases respectively.The AUC values for the RF prediction model and nomogram prediction modeling groups were 0.881 and 0.787 respectively,with Brier scores of 0.114 and 0.146,and threshold probabilities of net benefit ranging from 10％to 97％and 12％to 62％respectively.The RF prediction model exhibited superior discrimination,calibration,and clinical u-tility.The RF prediction model demonstrated that days of combined use of meropenem and vancomycin,GCS score,intestinal colonization,and hospitalization history were important predictors for CRO infections.CONCLUSION The prediction model for CRO infections in neurosurgical ICU patients established based on random forest algorithm has good predictive performance,and can be intervened with preventive and control measures for important predictive factors.

Objective To explore the distribution of healthcare-associated infection(HAI)and direct economic burden in neurosurgical patients based on disease diagnosis-related grouping(DRG),providing data support for in-fection prevention and control.Methods Clinical data of neurosurgical patients in a hospital from January to Decem-ber 2023 were retrospectively investigated,the average length of hospital stay and average hospitalization expense of HAI and non-HAI groups of the subgroups of DRG were analyzed.Results A total of 102 cases of HAI occurred among 2 180 neurosurgical patients,with HAI incidence being 4.68％.The main infection sites were lower respira-tory tract and organ space,accounting for 53.92％and 25.49％respectively.HAI patients distributed in 16 DRG subgroups,out of which AH 19 subgroup(invasive ventilator support≥96 hours or extracorporeal membrane oxy-genation[ECMO]or total artificial heart transplantation)had the highest incidence(58.82％),followed by BC19 subgroup(intracranial vascular surgery accompanied with hemorrhage diagnosis)(17.65％)and BB2A subgroup(craniotomy other than trauma,with severe or general complications and comorbidities)(12.81％).There was no statistically significant difference in resource consumption between HAI group and control group of AH19 group(all P＞0.05).HAI in BB2A group increased the average length of hospital stay and average hospitalization expense by 5.00 days and 34 600 Yuan,respectively.HAI in BC19 group increased the average length of hospital stay and ave-rage hospitalization expense by 8.50 days and 42 800 Yuan,respectively.Lower respiratory tract infection had a significant impact on resource consumption,while organ space infection only increased length of hospital stay of pa-tients.Conclusion Analysis of incidence of HAI and resource consumption of major infection sites based on DRG can clarify the focus of infection prevention and control,formulate targeted intervention measures,control medical expense and improve the quality of medical services.

The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.

Objective To explore the epidemiological characteristics and differences in antimicrobial resistance be-tween catheter-associated urinary tract infection(CAUTI)and non-CAUTI of healthcare-associated infection(HAI),and provide scientific basis for precise clinical prevention and control.Methods Based on the regional HAI surveillance platform in Suzhou City,urinary tract infection(UTI)surveillance data reported by 61 member units from January 2020 to December 2024 were analyzed retrospectively.Pathogen distribution,detection rate of multi-drug-resistant organisms(MDROs),and antimicrobial resistance spectrum characteristics of patients in the CAUTI group and non-CAUTI group were compared.Results The incidence of CAUTI in patients in CAUTI group was 0.99‰,the incidence of healthcare-associated UTI in patients in non-CAUTI group was 0.14％.There was statis-tically significant difference in the distribution of UTI pathogens between the two groups(P＜0.05).The patho-gens of the CAUTI group were mainly Gram-negative bacteria(56.1％),with high proportions of Escherichia coli(19.6％)and Klebsiella pneumoniae(15.0％).In the non-CAUTI group,the proportion of Gram-negative bacteria was higher(64.7％).Antimicrobial susceptibility testing results showed that the resistance rates of Escherichia co-li to tobramycin,cephalosporins,and carbapenems in the CAUTI group were all higher than those in the non-CAU-TI group(all P＜0.05).Except for tigecycline,the resistance rates of Klebsiella pneumoniae to other antimicrobial agents in the CAUTI group were all significantly different from the non-CAUTI group(all P＜0.05).The resis-tance rates of Acinetobacterbaumannii to ticarcillin/clavulanic acid,quinolones,most cephalosporins,carbapenems,and aminoglycosides in the CAUTI group were higher than those of the non-CAUTI group(all P＜0.05).The de-tection rates of MDROs were higher in the CAUTI group,especially that of carbapenem-resistant Klebsiella pneu-moniae,accounting for 57.8％.Conclusion There are significant differences in pathogen distribution and antimi-crobial resistance of UTI between the CAUTI group and the non-CAUTI group.It is necessary to establish a re-gional antimicrobial resistance surveillance system for pathogens in UTI,and provide basis for the rational use of an-timicrobial agents in clinical practice.

Objective:To explore the associated factors for membranous nephropathy (MN) patients with IgM deposition, and to construct a prediction model.Methods:This study was a retrospective cohort study. Patients diagnosed with MN with IgM deposition by renal biopsy in the Fourth Hospital of Hebei Medical University from February 2017 to December 2023 were retrospectively included. Clinical and pathological data were collected. The study population was randomized into a training set and a validation set at a 7:3 ratio. The endpoint event was defined as the remission of MN, and the patients were divided into remission group and non-remission group to compare the clinical and pathological examination results. Least absolute shrinkage and selection operator regression analysis and Cox regression analysis were used to explore the associated factors of poor prognosis of MN patients with IgM deposition. Internal validation was conducted using the validation set data. The clinical efficacy of the predictive model was evaluated by calculating the area under the receiver operating characteristic (ROC) curve and generating calibration curves. The total nomogram score for each patient was calculated based on the training set data, and the predictive performance was assessed by plotting the ROC curve. Patients were then stratified into low-risk and high-risk groups according to the optimal cut-off value derived from the ROC analysis of the total nomogram score. Kaplan-Meier survival analysis was performed to compare the remission rate between the two groups. Model performance was evaluated using the validation set.Results:A total of 200 MN patients with IgM deposition were included, and 49.0% of them achieved clinical remission. In the training set, statistically significant differences were observed in 24-hour urine protein quantification ( Z=-2.638, P=0.008), renal arteriolar wall thickening ( χ2=6.891, P=0.009), the proportion of patients receiving immunosuppressive therapy ( χ2=21.381, P<0.001), and the proportion of patients treated with corticosteroids combined with cyclophosphamide ( χ2=10.107, P=0.001). Through least absolute shrinkage and selection operator regression and Cox regression, 2 factors associated with clinical remission in MN patients with IgM deposition were simultaneously identified from 16 potential associated factors, including the use of immunosuppressants ( HR=3.823, 95% CI 2.055-7.113, P<0.001), and renal arteriolar wall thickening ( HR=0.428, 95% CI 0.221-0.831, P=0.012). Incorporating the clinical measurement of phospholipase A2 receptor (PLA2R) antibodies, a predictive model was established. The performance of the model was evaluated using the training dataset, yielding an area under the ROC curve of 0.731 (95% CI 0.648-0.814), with a sensitivity of 88.7% and a specificity of 55.1%. The optimal cut-off value was a total nomogram score of 41.7 points. The Kaplan-Meier survival analysis showed that the remission rate was significantly higher in the low-risk group than that of the high-risk group (Log-rank test, χ2=33.525, P<0.001). Model validation was performed using the validation dataset, which showed an AUC of 0.715 (95% CI 0.591-0.839), sensitivity of 70.4%, and specificity of 63.6%. Similarly, the Kaplan-Meier survival analysis demonstrated a significantly higher remission rate in the low-risk group than in the high-risk group (Log-rank test, χ2=8.467, P=0.004). Conclusion:A nomogram predictive model for remission of MN patients with IgM deposition, based on serum PLA2R antibody levels, the use of immunosuppressive therapy, and renal arteriolar wall thickening is developed. The model demonstrates a moderate clinical applicability.