Objective To investigate the clinical characteristics of patients with chronic abacterial pros-tafitis(CAP),the CAP related factors were analyzed,and a risk prediction model for CAP were constructed and validated.Methods The clinical dataes of 252 suspected CAP patients admitted to the hospital from June 2022 to December 2024 were collected,the patients were divided into modeling set(ni=177)and validation set(n=75)by 7∶3 ratio.Based on the modeling set dataes,the Lasso was used to screen CAP related predictive factors,a logistic multiple factor model was used to analyze the independent influence factors of CAP and a risk prediction model was constructed.The validation set patient dataes were used to plot ROC and DCA and validate the predic-tion model.Results There were 86 cases of CAP in the modeling set,accounting for 48.59％;32 cases of CAP in the validation set,accounting for 42.67％.The Logistic multiple regression analysis showed that BMI,waist to hip ratio,abnormal elevation of IL-8,COX-2,and PGE2 in prostate fluid were independent influence factors of CAP(P＜0.05),a Nomogram column chart based on this was established.The ROC analysis showed that the sensitivity of the model for detecting CAP in the modeling and validation sets were 0.814 and 0.802,respectively,and the specificity were 0.673 and 0.703,respectively.The DCA analysis showed that the net benefit thresholds for model-ing and validation sets by column charts are 0.1～0.9 and 0.2～1.0,respectively.Conclusions The occurrence of CAP is related to the patient's BMI,waist to hip ratio,the levels of IL-8,COX-2,and PGE2 in prostate fluid.The predictive model established based on this is highly accurate and it can help for CAP screening.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To explore clinical characteristics of multiple sclerosis(MS)patients in Suzhou,and to analyze main factors affecting their prognosis.Methods:General data,clinical symptoms,cerebrospinal fluid and imaging examinations of 51 MS patients admitted to Department of Neurology of the Second Hospital of Soochow University from July 31,2009 to July 31,2021 were retrospectively analyzed,and main factors affecting their prognosis were discussed.Results:Average age of onset of 51 MS patients was(43.3±15.6)years old,female accounted for 56.9%,male/female=1/1.3.Adult onset MS(AOMS)accounted for 62.8%,male/female=1/1.7;late onset MS(LOMS)accounted for 37.2%,male/female=1/0.9.Relapsing remitting MS(RRMS)accounted for 76.5%,and chronic onset accounted for 60.8%.Average annual recurrence rate was 8.8%.The first symptoms were numbness and weakness of limbs.Dizziness and numbness were more common in patients without recurrence after diagnosis of MS,and limb weak-ness and numbness were more common in patients with recurrence.Among lesions of MRI,62.7%(32/51)of periventricular involve-ment,52.9%(27/51)of spinal cord involvement,51.0%(26/51)of infratentorial involvement.Proportion of subtentorial and spinal cord(cervical,thoracic)involved were significantly higher in patients with recurrent MS than without recurrence.Values of albumin,IgG,IgA and IgM in cerebrospinal fluid increased with increase of recurrence times.EDSS score of male was higher than female,and LOMS score was higher than AOMS.MS patients without relapse had a low EDSS score,and median EDSS score at current follow-up was 0(0,1.00)score.MS score with relapse was relatively high,and median EDSS score at current follow-up was 2.75(0.25,7.25)score.Conclusion:MS patients with chronic onset are more common,with a high proportion of LOMS,and proportion of males increases with increasing age of onset.High EDSS score at first onset,cervical,thoracic and subtentorial lesions,increased values of cerebrospinal fluid albumin,IgG,IgA,IgM,age at first onset(50+years old),male associate with poor MS prognosis.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective To investigate the clinical characteristics of patients with chronic abacterial pros-tafitis(CAP),the CAP related factors were analyzed,and a risk prediction model for CAP were constructed and validated.Methods The clinical dataes of 252 suspected CAP patients admitted to the hospital from June 2022 to December 2024 were collected,the patients were divided into modeling set(ni=177)and validation set(n=75)by 7∶3 ratio.Based on the modeling set dataes,the Lasso was used to screen CAP related predictive factors,a logistic multiple factor model was used to analyze the independent influence factors of CAP and a risk prediction model was constructed.The validation set patient dataes were used to plot ROC and DCA and validate the predic-tion model.Results There were 86 cases of CAP in the modeling set,accounting for 48.59％;32 cases of CAP in the validation set,accounting for 42.67％.The Logistic multiple regression analysis showed that BMI,waist to hip ratio,abnormal elevation of IL-8,COX-2,and PGE2 in prostate fluid were independent influence factors of CAP(P＜0.05),a Nomogram column chart based on this was established.The ROC analysis showed that the sensitivity of the model for detecting CAP in the modeling and validation sets were 0.814 and 0.802,respectively,and the specificity were 0.673 and 0.703,respectively.The DCA analysis showed that the net benefit thresholds for model-ing and validation sets by column charts are 0.1～0.9 and 0.2～1.0,respectively.Conclusions The occurrence of CAP is related to the patient's BMI,waist to hip ratio,the levels of IL-8,COX-2,and PGE2 in prostate fluid.The predictive model established based on this is highly accurate and it can help for CAP screening.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective:To explore clinical characteristics of multiple sclerosis(MS)patients in Suzhou,and to analyze main factors affecting their prognosis.Methods:General data,clinical symptoms,cerebrospinal fluid and imaging examinations of 51 MS patients admitted to Department of Neurology of the Second Hospital of Soochow University from July 31,2009 to July 31,2021 were retrospectively analyzed,and main factors affecting their prognosis were discussed.Results:Average age of onset of 51 MS patients was(43.3±15.6)years old,female accounted for 56.9%,male/female=1/1.3.Adult onset MS(AOMS)accounted for 62.8%,male/female=1/1.7;late onset MS(LOMS)accounted for 37.2%,male/female=1/0.9.Relapsing remitting MS(RRMS)accounted for 76.5%,and chronic onset accounted for 60.8%.Average annual recurrence rate was 8.8%.The first symptoms were numbness and weakness of limbs.Dizziness and numbness were more common in patients without recurrence after diagnosis of MS,and limb weak-ness and numbness were more common in patients with recurrence.Among lesions of MRI,62.7%(32/51)of periventricular involve-ment,52.9%(27/51)of spinal cord involvement,51.0%(26/51)of infratentorial involvement.Proportion of subtentorial and spinal cord(cervical,thoracic)involved were significantly higher in patients with recurrent MS than without recurrence.Values of albumin,IgG,IgA and IgM in cerebrospinal fluid increased with increase of recurrence times.EDSS score of male was higher than female,and LOMS score was higher than AOMS.MS patients without relapse had a low EDSS score,and median EDSS score at current follow-up was 0(0,1.00)score.MS score with relapse was relatively high,and median EDSS score at current follow-up was 2.75(0.25,7.25)score.Conclusion:MS patients with chronic onset are more common,with a high proportion of LOMS,and proportion of males increases with increasing age of onset.High EDSS score at first onset,cervical,thoracic and subtentorial lesions,increased values of cerebrospinal fluid albumin,IgG,IgA,IgM,age at first onset(50+years old),male associate with poor MS prognosis.

OBJECTIVE Healthcare-associated infection(HAI)surveillance is a crucial tool for healthcare manage-ment and public health prevention,the World Health Organization(WHO)released simplified technical guidelines of HAI surveillance to enhance the HAI surveillance in areas with limited medical resources.This study explores the applicability and implementation pathways of the WHO's simplified standards for HAI surveillance in China.METHODS This study used text analysis and qualitative interviews to compare the differences of HAI sur-veillance criteria between China and WHO.Interviews were conducted with professionals of infection prevention and control(IPC)to explore the opportunities and challenges of implementing WHO simplified standards in China.RESULTS Twenty-two IPC professionals with long-term experiences participated in the interviews.Main themes derived from the interview were:WHO simplified standards could enhance the sensitivity of HAI surveil-lance,this approach provided insights for a risk early warning surveillance and improved surveillance in primary healthcare institutions.It also increased the international comparability of Chinese HAI surveillance results.How-ever,the implementation of the WHO simplified standards required further pilot validation,higher levels of infor-matic surveillance and clinical diagnostic capabilities.CONCLUSION This study explores the feasibility and accept-ability of the WHO's simplified HAI surveillance in China,provides references for the transformation of China's HAI surveillance models and systems.

OBJECTIVE Healthcare-associated infection(HAI)surveillance is a crucial tool for healthcare manage-ment and public health prevention,the World Health Organization(WHO)released simplified technical guidelines of HAI surveillance to enhance the HAI surveillance in areas with limited medical resources.This study explores the applicability and implementation pathways of the WHO's simplified standards for HAI surveillance in China.METHODS This study used text analysis and qualitative interviews to compare the differences of HAI sur-veillance criteria between China and WHO.Interviews were conducted with professionals of infection prevention and control(IPC)to explore the opportunities and challenges of implementing WHO simplified standards in China.RESULTS Twenty-two IPC professionals with long-term experiences participated in the interviews.Main themes derived from the interview were:WHO simplified standards could enhance the sensitivity of HAI surveil-lance,this approach provided insights for a risk early warning surveillance and improved surveillance in primary healthcare institutions.It also increased the international comparability of Chinese HAI surveillance results.How-ever,the implementation of the WHO simplified standards required further pilot validation,higher levels of infor-matic surveillance and clinical diagnostic capabilities.CONCLUSION This study explores the feasibility and accept-ability of the WHO's simplified HAI surveillance in China,provides references for the transformation of China's HAI surveillance models and systems.