Objective:To describe the epidemiological characteristics of 22 common respiratory patho-gens in patients with pneumonia in Yinzhou,Ningbo,from January 1,2015 to December 21,2024.Methods:The test data of 22 common respiratory pathogens in patients diagnosed with pneumonia or lung infection in the Yinzhou Regional Health Information Platform from January 1,2015 to December 21,2024 were collected.The positive cases,positive rates,and positive proportions were calculated.The epidemiological characteristics were described by the year,sex,age group,season,and coronavirus disease 2019(COVID-19)pandemic period.Results:A total of 77 531 pneumonia patients were included,with 492 696 respiratory pathogen tests performed.The number of respiratory pathogen tests and positive cases of pneumonia patients in Yinzhou showed an upward trend.In the study,34.63％of the pneumo-nia patients tested positive for at least one pathogen,and the pathogen non-detection rate decreased from 79.44％in 2015 to 58.38％in 2024.The overall pathogen positive rate was 9.12％,which decreased during the COVID-19 pandemic and had not returned to the historical level after the COVID-19 pande-mic.The positive rate was highest in children aged 6-17 years(13.99％),and lowest in the elderly over 60 years(4.16％).The top 3 highest number of positive cases was Mycoplasma pneumoniae,influenza A virus,and influenza B virus;the top 3 highest positive rates of pathogen tests were Mycoplasma pneu-moniae(25.26％),rhinovirus(12.02％),and Bordetella pertussis(11.66％).The pathogen spectrum proportion in men was similar to that in women,only showing a higher ratio of Mycobacterium tuberculosis and a slightly lower ratio of Mycoplasma pneumoniae(P＜0.001).Mycoplasma pneumoniae,respiratory syncytial virus,and rhinovirus infections were more common in children,while influenza virus,Mycobac-terium tuberculosis,and Streptococcus pyogenes infections were more common in adults and the elderly(P＜0.001).Influenza virus and human metapneumovirus infections were more common in winter,rhi-novirus and Bordetella pertussis infections were more common in spring,and Mycoplasma pneumoniae in-fections were relatively more common in fall(P＜0.001).After the COVID-19 pandemic,the propor-tions of rhinovirus,respiratory syncytial virus,and human metapneumovirus infections in the pneumonia patients increased signi-ficantly,reaching 7.53％,4.26％,and 2.25％,respectively,while the propor-tions of influenza B virus and Mycobacterium tuberculosis infections decreased to 4.14％and 2.80％,re-spectively(P＜0.001).Conclusion:In the past decade,the scale of respiratory pathogen infection in the pneumonia population in Yinzhou had expanded significantly,and there were differences in distribu-tion by the year,gender,age group,and season.The respiratory pathogen spectrum in pneumonia pa-tients after the COVID-19 pandemic had a trend of diversification.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Objective To develop and validate a prediction model to identify high-risk individuals who are at-risk to develop acute liver injury(ALI)within 180 days in new statin users,and to support early clinical intervention.Methods Data were sourced from the Yinzhou Regional Health Information Platform,covering statin initiators aged 18 years and older from January 1,2010,to October 31,2021.The dataset was divided into a derivation cohort and a temporal validation cohort based on the time of statin initiation.Predictors were selected using LASSO regression,and the model was constructed using the extreme gradient boosting(XGBoost)algorithm combined with cost-sensitive learning.Model performance was evaluated using Brier scores,Harrell's C-index,and calibration curves.Results A total of 126,440 statin initiators were included,with 90,542 in the derivation cohort and 35,898 in the validation cohort.Within 180 days of initial statin use,412(0.33％)patients developed ALI,including 305(0.34％)in the derivation cohort and 107(0.30％)in the validation cohort.The final model incorporated 16 predictors,which included demographic characteristics,lifestyle factors,family history,medical history,statin use,and concomitant medication use.The model demonstrated excellent overall performance[Brier score=0.0043,95％CI(0.0038,0.0049)],discrimination[Harrell's C-index=0.761,95％CI(0.725,0.794)],and calibration in internal validation.In temporal validation,the model also performed well[Brier score=0.0044,95％CI(0.0036,0.0052),Harrell's C-index=0.703,95％CI(0.614,0.781)].Conclusion This study develope and validate a prediction model for ALI in statin users,providing clinicians with a reliable tool for individualized risk assessment.This model can help achieve risk stratification and reduce the occurrence of ALI.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To investigate the prevalence of sarcopenia and potential influencing factors in middle-aged and elderly populations in Zhejiang Province.Methods:Data were obtained from Zhejiang Provincial Household Economic Status Survey, a cross-sectional survey was condcuted in middle-aged and olde adults selected through multi-stage sampling in three cities in Zhejiang (Huzhou, Jiaxing and Shaoxing) in July 2023. A total of 3 019 study participants, average age 62.3 years old, 53.5% men, were included according to the inclusion and exclusion criteria. Sarcopenia screening was conducted by using the questionnaire with five sarcopenia related-items. Univariable and multivariable logistic regression analysis was used to identify the factors associated with sarcopenia.Results:The prevalence of sarcopenia in the middle-aged and old study participants was 4.47%. Significant differences were observed between the participants with or without sarcopenia in terms of age, educational level, BMI, alcohol consumption status, diet habit, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, adulthood community socioeconomic status, muscle strength, walking assistance, ability to stand from seat, ability to climb stairs, and fall frequency ( P<0.05). Multivariable logistic regression analysis revealed that old age (≥75 years: OR=2.82, 95% CI: 1.60-4.97), low body weight ( OR=1.96, 95% CI: 1.06-3.62), unhealthy diet habit ( OR=1.57, 95% CI: 1.01-2.46), physical inactivity ( OR=5.80, 95% CI: 3.09-10.88), poor or very poor sleep quality ( OR=1.65, 95% CI:1.23-2.41), number of chronic diseases (1 chronic disease: OR=1.84, 95% CI: 1.08-3.14; 2 chronic diseases: OR=3.22, 95% CI: 1.81-5.71; 3 or more chronic diseases: OR=3.74, 95% CI: 2.11-6.65), poor childhood socioeconomic status ( OR=2.98, 95% CI: 1.23-7.20), and poor adulthood community socioeconomic status ( OR=3.87, 95% CI: 1.63-9.17) were significant risk factors for sarcopenia. Conclusions:The prevalence of sarcopenia was relatively low in middle-aged and old population in Zhejiang. Age, BMI, unhealthy diet, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, and adulthood community socioeconomic status were identified as significant influencing factors.

Background and Aims:Papillary thyroid carcinoma(PTC)is the most common endocrine malignancy in China,with cervical lymph node metastasis being a frequent and critical clinical feature that directly affects patient prognosis and recurrence risk.In recent years,with the rapid increase in the prevalence of overweight and obesity in China,the role of body mass index(BMI)in various tumors has attracted growing attention.This study aimed to investigate the association between overweight and cervical LNM in PTC,analyze sex-specific differences and influencing factors,and provide evidence for precise clinical management.Methods:A retrospective analysis was conducted on the clinicopathologic data of 1 445 patients with classical PTC treated at Xiangya Hospital of Central South University between August 2021 and June 2022.Patients were divided into groups based on the presence or absence of lymph node metastasis.Restricted cubic spline analysis explored the nonlinear relationship between BMI and lymph node metastasis risk.Univariate and multivariate Logistic regression analyses were applied to identify independent risk factors.Furthermore,sex-stratified analysis was performed among overweight patients(BMI≥24 kg/m2)to determine sex-specific risk factors for lymph node metastasis.Results:Among all patients,716(49.6%)had lymph node metastasis.Univariate analysis showed that BMI,sex,age,tumor diameter,multifocality,and extrathyroidal extension were significantly associated with cervical lymph node metastasis in PTC patients(all P<0.05).A nonlinear positive correlation was observed between BMI and lymph node metastasis risk,which was more pronounced in male patients.Additionally,BMI was positively correlated with triglyceride levels and negatively correlated with high-density lipoprotein cholesterol.Sex-stratified analysis revealed that in overweight male patients,younger age(OR=0.954),larger tumor diameter(OR=1.085),and multifocality(OR=2.776)were independent risk factors for LNM;in overweight female patients,younger age(OR=0.943)and larger tumor diameter(OR=1.074)were the main influencing factors.Conclusion:Overweight is closely associated with cervical lymph node metastasis in PTC,and the high-risk factors for LNM differ between male and female overweight patients.Young age,larger tumor size,and multifocality in overweight males,and young age and larger tumors in overweight females indicate a higher risk of metastasis.It is recommended that high-risk populations receive enhanced preoperative evaluation and individualized lymph node dissection strategies to achieve precise treatment and improved risk control.

Objective:To comprehensively evaluate the application of Common Data Model (CDM) of Observational Medical Outcomes Partnership (OMOP) in China, and provide reference for the implementation of data standardization and evidence sharing in China.Methods:PubMed, Embase, Web of Science, CNKI, VIP, WanFang and SinoMed databases were used for literature retrieval to collect the research papers of OMOP CDM application for data standardization in China until March 15, 2023. The information about institutions, types and numbers of patients were extracted.Results:A total of 14 research papers, including 9 in English and 5 in Chinese, were selected. The research papers published since 2018 were collected, which focused on patients with hypertension, diabetes, and depression. A total of 12 institutions or platforms transformed data into OMOP CDM. Jiangsu Provincial People's Hospital was the first one to apply the CDM and demonstrated its feasibility in China. Additionally, the regional information system in Yinzhou District of Ningbo, Zhejiang Province, standardized the multi-dimensional data of patients with diabetes and hypertension. Based on this platform, a series of prediction models for complications in patients with diabetes were constructed. Another major database in Beijing Anding Hospital applied OMOP CDM to analyze the characteristics of patients with late-life depression and dementia.Conclusions:This study analyzed the application of OMOP CDM in China. Through in-depth analysis of specific cases, the study provided guidance for the future cross-regional evidence sharing and collaboration.

Objective:To investigate the prevalence of sarcopenia and potential influencing factors in middle-aged and elderly populations in Zhejiang Province.Methods:Data were obtained from Zhejiang Provincial Household Economic Status Survey, a cross-sectional survey was condcuted in middle-aged and olde adults selected through multi-stage sampling in three cities in Zhejiang (Huzhou, Jiaxing and Shaoxing) in July 2023. A total of 3 019 study participants, average age 62.3 years old, 53.5% men, were included according to the inclusion and exclusion criteria. Sarcopenia screening was conducted by using the questionnaire with five sarcopenia related-items. Univariable and multivariable logistic regression analysis was used to identify the factors associated with sarcopenia.Results:The prevalence of sarcopenia in the middle-aged and old study participants was 4.47%. Significant differences were observed between the participants with or without sarcopenia in terms of age, educational level, BMI, alcohol consumption status, diet habit, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, adulthood community socioeconomic status, muscle strength, walking assistance, ability to stand from seat, ability to climb stairs, and fall frequency ( P<0.05). Multivariable logistic regression analysis revealed that old age (≥75 years: OR=2.82, 95% CI: 1.60-4.97), low body weight ( OR=1.96, 95% CI: 1.06-3.62), unhealthy diet habit ( OR=1.57, 95% CI: 1.01-2.46), physical inactivity ( OR=5.80, 95% CI: 3.09-10.88), poor or very poor sleep quality ( OR=1.65, 95% CI:1.23-2.41), number of chronic diseases (1 chronic disease: OR=1.84, 95% CI: 1.08-3.14; 2 chronic diseases: OR=3.22, 95% CI: 1.81-5.71; 3 or more chronic diseases: OR=3.74, 95% CI: 2.11-6.65), poor childhood socioeconomic status ( OR=2.98, 95% CI: 1.23-7.20), and poor adulthood community socioeconomic status ( OR=3.87, 95% CI: 1.63-9.17) were significant risk factors for sarcopenia. Conclusions:The prevalence of sarcopenia was relatively low in middle-aged and old population in Zhejiang. Age, BMI, unhealthy diet, physical activity level, sleep quality, number of chronic diseases, childhood socioeconomic status, and adulthood community socioeconomic status were identified as significant influencing factors.

Rhabdomyolysis is a clinical syndrome characterized by damage to the integrity of skel-etal muscle cell membranes,massive release of intracellular contents(such as myoglobin,creatine ki-nase,and electrolytes)into the bloodstream,thereby triggering systemic pathophysiological responses.Its most common and severe complication is acute kidney injury,primarily resulting from the combined effects of myoglobin deposition in renal tubules and renal ischemia.Due to lack of specific early clini-cal manifestations,patients are often misdiagnosed or experience delayed treatment,which can lead to exacerbation of the condition and even life-threatening consequences.This article reports a case of a patient who presented with unexplained rhabdomyolysis at onset and subsequently developed acute kid-ney injury during the course of the disease.A systematic analysis and discussion of the potential etiolo-gy are conducted based on the patient's clinical features and laboratory test results.