BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed t...BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.展开更多
This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfectio...This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.展开更多
Objective:To study the risk prediction model for multiple bronchoalveolar lavage in children with mycoplasma pneumoniae pneumonia(MPP).Methods:151 pediatric patients with MPP admitted in our hospital from July to Dece...Objective:To study the risk prediction model for multiple bronchoalveolar lavage in children with mycoplasma pneumoniae pneumonia(MPP).Methods:151 pediatric patients with MPP admitted in our hospital from July to December 2023 were selected,the incidence rate of multiple bronchoalveolar lavage was recorded.A logistic multivariate regression model was employed to analyze relevant factors and construct a risk prediction model for multiple bronchoalveolar lavage in children with MPP.Results:Among 151 children with MPP,64 cases underwent multiple bronchoalveolar lavage,accounting for 42.38%.The Logistic multivariate model analysis revealed that the pleural effusion,sepsis,and abnormally elevated serum levels of LDH and D-D were independent influence factors for multiple bronchoalveolar lavage in children with MPP(p<0.05),based on this,a Nomogram prediction model can be established.The ROC analysis results showed that the AUC of the model to judge the multiple bronchoalveolar lavage in MPP patients was 0.828(SE=0.035,95%CI=0.760-0.896,p<0.001),the sensitivity was 0.813 and the specificity was 0.759.Conclusion:The multiple bronchoscopic bronchoalveolar lavage in MPP patients are associated with the levels of LDH and D-D,as well as the presence of pleural effusion and sepsis complications,the risk prediction model established,which based on this has high accuracy.展开更多
Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays...Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.展开更多
BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages ...BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.展开更多
Objective:To analyze the independent risk factors and establish a risk prediction model by investigating the readmission of elderly patients with coronary heart disease(CHD)within 1 year after discharge.Methods:A tota...Objective:To analyze the independent risk factors and establish a risk prediction model by investigating the readmission of elderly patients with coronary heart disease(CHD)within 1 year after discharge.Methods:A total of 480 CHD patients,who were hospitalized in the Affiliated Hospital of Hebei University from October 2019 to December 2020,were included in this study.A general data scale,mental health status scale,the Clinical Frailty Scale,Pittsburgh Sleep Quality Index,as well as the Family Adaptability and Cohesion Evaluation Scale were used to collect data.According to the number of readmissions due to CHD within 1 year after discharge,the patients were divided into two groups:the readmission group(n=212)and the no readmission group(n=268).General data,laboratory examination indicators,frailty,mental health status,sleep status,as well as family intimacy and adaptability were compared between the two groups.Logistic regression was used to analyze the independent risk factors for the readmission of these patients,and R software was used to construct a line diagram model for predicting readmission of elderly patients with CHD.Results:Five factors including body mass index(OR=1.045),low density lipoprotein(OR=1.123),frailty(OR=1.946),mental health(OR=1.099),as well as family intimacy and adaptability(OR=0.928)were included to construct the risk prediction model for the readmission of elderly patients with CHD within 1 year after discharge.The ROC curve showed that the area under the curve for predicting readmission of elderly patients with CHD was 0.816;Hosmer-Lemeshow goodness of fit test showed X2=1.456 and P=0.989;the maximum Youden index corresponding to the predicted value of risk was 0.526.The results showed that the model could accurately predict the risk of readmission in elderly patients with CHD within 1 year after discharge.Conclusion:This study constructed a line diagram model based on five independent risk factors of the readmission of elderly patients with CHD:body mass index,low density lipoprotein,frailty,mental health status,as well as family intimacy and adaptability.This model has good discrimination,accuracy,and predictive efficiency,providing reference for the early prevention and intervention of readmission in elderly patients with CHD recurrence.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagn...BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.展开更多
This paper discusses the risk factors related to gallbladder disease in Shanghai,improves the accuracy of risk prediction,and provides a theoretical basis for scientific diagnosis and universality of gallbladder disea...This paper discusses the risk factors related to gallbladder disease in Shanghai,improves the accuracy of risk prediction,and provides a theoretical basis for scientific diagnosis and universality of gallbladder disease.We selected 3462 data of middle-aged and elderly health check-up patients in a general hospital in Shanghai,and divided into gallbladder disease group according to color doppler ultrasound diagnosis results.Single-factor analysis screened out 8 important risk factors,which were used as an analysis variable of multi-layer perceptron neural network and binary logistic regression to construct the prediction model of gallbladder disease.The prediction accuracy of the multi-layer perceptron neural network risk prediction model is 76%.The area under the receiver operating characteristic curve(AUC)is 0.82,the maximum Youden index is 0.44,the sensitivity is 79.51,and the specificity is 64.23.The prediction accuracy of the multi-layer perceptron neural network model is better than that of the binary logistic regression prediction model.The overall prediction accuracy of the binary logistic regression prediction model is 75.60%,the AUC is 0.81,the maximum Youden index is 0.42,the sensitivity is 74.48,and the specificity is 57.60.In the objective risk prediction of gallbladder disease in middle-aged and elderly people in Shanghai,the risk prediction model based on the multi-layer perceptron neural network has a better prediction performance than the binary logistic regression model,which provides a theoretical basis for preventive treatment and intervention.展开更多
BACKGROUND Arthritis is a prevalent and debilitating condition that affects a significant proportion of middle-aged and older adults worldwide.Characterized by chronic pain,inflammation,and joint dysfunction,arthritis...BACKGROUND Arthritis is a prevalent and debilitating condition that affects a significant proportion of middle-aged and older adults worldwide.Characterized by chronic pain,inflammation,and joint dysfunction,arthritis can severely impact physical function,quality of life,and mental health.The overall burden of arthritis is further compounded in this population due to its frequent association with depression.As the global population both the prevalence and severity of arthritis are anticipated to increase.AIM To investigate depressive symptoms in the middle-aged and elderly arthritic population in China,a risk prediction model was constructed,and its effectiveness was validated.METHODS Using the China Health and Retirement Longitudinal Study 2018 data on middleaged and elderly arthritic individuals,the population was randomly divided into a training set(n=4349)and a validation set(n=1862)at a 7:3 ratio.Based on 10-fold cross-validation,least absolute shrinkage and selection regression was used to screen the model for the best predictor variables.Logistic regression was used to construct the nomogram model.Subject receiver operating characteristic and calibration curves were used to determine model differentiation and accuracy.Decision curve analysis was used to assess the net clinical benefit.RESULTS The prevalence of depressive symptoms in the middle-aged and elderly arthritis population in China was 47.1%,multifactorial logistic regression analyses revealed that gender,age,number of chronic diseases,number of pain sites,nighttime sleep time,education,audiological status,health status,and place of residence were all predictors of depressive symptoms.The area under the curve values for the training and validation sets were 0.740(95%confidence interval:0.726-0.755)and 0.731(95%confidence interval:0.709-0.754),respectively,indicating good model differentiation.The calibration curves demonstrated good prediction accuracy,and the decision curve analysis curves demonstrated good clinical utility.CONCLUSION The risk prediction model developed in this study has strong predictive performance and is useful for screening and assessing depression symptoms in middle-aged and elderly arthritis patients.展开更多
Introduction:Prediabetes is a well-established risk factor for type 2 diabetes development.While existing risk prediction models primarily focus on the general adult population,those specifically designed for older ad...Introduction:Prediabetes is a well-established risk factor for type 2 diabetes development.While existing risk prediction models primarily focus on the general adult population,those specifically designed for older adults remain scarce.This study aimed to develop and validate a 3-year risk prediction model for type 2 diabetes specifically for older adults with prediabetes.Methods:Data were derived from the Health Aging Evaluation Longitudinal Survey(HAELS),which included 1,127 older adults with prediabetes defined by FPG between 5.6–7.0 mmol/L at baseline(2019).Incident diabetes was defined as FPG≥7.0 mmol/L at 3-year follow-up(2022).A multivariable Cox proportional hazards regression model with stepwise variable selection was used to identify predictors and construct the prediction model.Bootstrapping was employed for internal validation.Model performance was evaluated using area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA).A nomogram was constructed to visualize individualized risk.Results:Seven predictors were included in the final model:age,physical activity,waist circumference,baseline FPG,triglycerides,systolic blood pressure,and dessert intake.The model demonstrated good discrimination(AUC=0.752 in the training dataset,0.793 in the validation dataset)and satisfactory calibration.DCA indicated a net clinical benefit within a threshold probability range of 0.02–0.50.Conclusion:This model,demonstrating good predictive performance and clinical utility,provides a simple and practical tool to estimate the 3-year diabetes risk in older adults with prediabetes.展开更多
Introduction:Non-suicidal self-injury(NSSI)has become increasingly prevalent among adolescents,representing a significant public health concern with profound impacts on both physical and mental wellbeing.This study ai...Introduction:Non-suicidal self-injury(NSSI)has become increasingly prevalent among adolescents,representing a significant public health concern with profound impacts on both physical and mental wellbeing.This study aims to determine the prevalence of NSSI among adolescents in Jiangsu Province and develop a prediction model to facilitate early identification and intervention.Methods:This study is based on the“School-based Evaluation and Response to Child Health(SEARCH)”project.A cross-sectional survey was conducted among students from 11 schools in Jiangsu Province,China in 2023.Following data cleaning,11,427 students were included in the analysis.Machine learning methods were employed to establish a risk prediction model for NSSI among adolescents.Results:The prevalence of NSSI among adolescents aged 10–18 was 12.72%.Eight key predictors of NSSI were identified:insomnia,emotional symptoms,cohesion of family environment,history of drinking alcohol,gender,conflict of family environment,conduct problems,and academic level.The XGBoost model demonstrated an area under the curve(AUC)of 0.800[95%confidence interval(CI):0.776,0.823]and an accuracy(ACC)of 0.886 in the testing set.Conclusions:This study underscores the importance of personalized prevention strategies for NSSI and highlights the necessity of implementing comprehensive behavioral interventions,including mental health support,sleep quality enhancement,and cultivation of supportive family environments.展开更多
BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate ...BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.展开更多
BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with ment...BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.展开更多
Background and Objectives:It has been found that ICU patients may encounter various complications during enteral nutrition(EN).Of these,feeding intolerance(FI)is a common issue that often necessitates the reduction or...Background and Objectives:It has been found that ICU patients may encounter various complications during enteral nutrition(EN).Of these,feeding intolerance(FI)is a common issue that often necessitates the reduction or cessation of EN.This study aims to evaluate risk prediction models for feeding intolerance(FI)in critically ill patients receiving EN by searching major public databases.Methods and Study Design:We searched for rele vant studies in Embase,PubMed,Web of Science,Chinese Biomedical Database(CBM),China National Knowledge Infrastructure(CNKI),Wanfang Data,and cqvip.com up until January 2024.Two researchers inde pendently conducted the screening and data extraction processes,and the quality of the literature was assessed us ing bias risk assessment tools.Results:A total of 13 references were included,and the subjects included patients with sepsis,pancreatitis or cerebral apoplexy;the incidence of FI was 35.2%-49.3%.The studies discussed the predictive performance of various models,with 11 studies reporting on their accuracy and calibration.The mod els demonstrated the area under the curve(AUC)of the receiver operating characteristic(ROC)curve or the con cordance index(C-index)between 0.70 and 0.91,sensitivity from 0.81 to 0.93,and specificity from 0.68 to 0.83.Conclusions:There is a critical need for risk prediction models for FI in critically ill patients on EN that are both internally and externally validated and exhibit high performance.展开更多
Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological featur...Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.展开更多
Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This w...Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020.The demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital admission.In-hospital survival or death was documented.Clinical characteristics associated with death were evaluated.Results Among 67 children,51 survived,and 16 died.The most common symptom was digestive disorder(67.2%).Based on the Bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction<50%,pulmonary edema,ventricular tachycardia,lactic acid value>4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation cohort.Model calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with AMC.The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.展开更多
This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk facto...This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.展开更多
BACKGROUND As a minimally invasive technique,endoscopic submucosal dissection(ESD)is widely used in treating early colorectal cancer(ECRC)and precancerous lesions(PCLs).However,a common postoperative complication-dela...BACKGROUND As a minimally invasive technique,endoscopic submucosal dissection(ESD)is widely used in treating early colorectal cancer(ECRC)and precancerous lesions(PCLs).However,a common postoperative complication-delayed postoperative bleeding(DPOB)-can significantly hinder patient recovery.AIM To build and validate a predictive model for assessing post-ESD DPOB risk in ECRC and PCL patients,utilizing logistic regression methodology.METHODS A retrospective review was conducted on ECRC/PCL 302 patients who received ESD at our hospital between July 2021 and July 2024.The cohort was stratified based on the incidence of DPOB following ESD,forming DPOB and non-DPOB groups.Through allocation,they were further allocated into model and validation cohorts.Clinical variables from both cohorts were collated and subjected to univariate analysis to determine potential factors associated with post-ESD DPOB.Subsequently,we constructed a predictive model for DPOB risk employing logistic regression analysis.Model performance assessment used receiver operating characteristic curves in both the training and validation cohorts,with internal validation accomplished via 10-fold cross-validation.RESULTS The occurrence rate of DPOB was 9.93%.Univariate analysis revealed that the number of lesions,lesion size,lesion location,degree of submucosal fibrosis,and intraoperative bleeding were significantly associated with DPOB.Binary logistic regression analysis identified the number of lesions,lesion size,lesion location,and degree of submucosal fibrosis as independent DPOB determinants.A nomogram that was developed to quantify the DPOB risk exhibited that an increment in the total score corresponded to an increased risk.The model achieved area under the curve values of 0.831 and 0.821 in the model and validation groups,respectively,with P values of 0.853 and 0.203 in the Hosmer-Lemeshow test.The model demonstrated robust discriminative performance,with an average area under the curve of 0.795(95%confidence interval:0.702-0.887)in 10-fold cross-validation.CONCLUSION Collectively,the presence of multiple lesions,lesion size of≥3 cm,lesion localization in the rectum,and severe fibrosis are significant independent predictors of DPOB in patients undergoing surgery for ECRC or PCLs.The proposed risk prediction model,which integrates these factors,demonstrates excellent predictive accuracy and clinical utility,thereby providing a valuable tool for risk stratification and postoperative management in this patient population.展开更多
BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited rese...BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.展开更多
Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulf...Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.展开更多
基金Supported by the Research Fund of Qiannan Medical College for Nationalities,No.Qnyz202222.
文摘BACKGROUND Colorectal cancer(CRC)is one of the most prevalent and lethal malignant tumors worldwide.Currently,surgical intervention was the primary treatment modality for CRC.However,increasing studies have revealed that CRC patients may experience postoperative cognitive dysfunction(POCD).AIM To establish a risk prediction model for POCD in CRC patients and investigate the preventive value of dexmedetomidine(DEX).METHODS A retrospective analysis was conducted on clinical data from 140 CRC patients who underwent surgery at the People’s Hospital of Qian Nan from February 2020 to May 2024.Patients were allocated into a modeling group(n=98)and a validation group(n=42)in a 7:3 ratio.General clinical data were collected.Additionally,in the modeling group,patients who received DEX preoperatively were incorporated into the observation group(n=54),while those who did not were placed in the control group(n=44).The incidence of POCD was recorded for both cohorts.Data analysis was performed using statistical product and service solutions 20.0,with t-tests orχ^(2) tests employed for group comparisons based on the data type.Least absolute shrinkage and selection operator regression was applied to identify influencing factors and reduce the impact of multicollinear predictors among variables.Multivariate analysis was carried out using Logistic regression.Based on the identified risk factors,a risk prediction model for POCD in CRC patients was developed,and the predictive value of these risk factors was evaluated.RESULTS Significant differences were observed between the cognitive dysfunction group and the non-cognitive dysfunction group in diabetes status,alcohol consumption,years of education,anesthesia duration,intraoperative blood loss,intraoperative hypoxemia,use of DEX during surgery,intraoperative use of vasoactive drugs,surgical time,systemic inflammatory response syndrome(SIRS)score(P<0.05).Multivariate Logistic regression analysis identified that diabetes[odds ratio(OR)=4.679,95%confidence interval(CI)=1.382-15.833],alcohol consumption(OR=5.058,95%CI:1.255-20.380),intraoperative hypoxemia(OR=4.697,95%CI:1.380-15.991),no use of DEX during surgery(OR=3.931,95%CI:1.383-11.175),surgery duration≥90 minutes(OR=4.894,95%CI:1.377-17.394),and a SIRS score≥3(OR=4.133,95%CI:1.323-12.907)were independent risk factors for POCD in CRC patients(P<0.05).A risk prediction model for POCD was constructed using diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score as factors.A receiver operator characteristic curve analysis of these factors revealed the model’s predictive sensitivity(88.56%),specificity(70.64%),and area under the curve(AUC)(AUC=0.852,95%CI:0.773-0.919).The model was validated using 42 CRC patients who met the inclusion criteria,demonstrating sensitivity(80.77%),specificity(81.25%),and accuracy(80.95%),and AUC(0.805)in diagnosing cognitive impairment,with a 95%CI:0.635-0.896.CONCLUSION Logistic regression analysis identified that diabetes,alcohol consumption,intraoperative hypoxemia,non-use of DEX during surgery,surgery duration,and SIRS score vigorously influenced the occurrence of POCD.The risk prediction model based on these factors demonstrated good predictive performance for POCD in CRC individuals.This study offers valuable insights for clinical practice and contributes to the prevention and management of POCD under CRC circumstances.
文摘This editorial critically evaluated the recent study by Wang et al,which systematically investigated the efficacy of perioperative disinfection and isolation measures(including preoperative povidone-iodine disinfection,intraoperative sterile barrier techniques,and postoperative intensive care)in reducing infection rates.The study further incorporated the surgical site infection risk prediction model(constructed via the least absolute shrinkage and selection operator al-gorithm,integrating patients'baseline characteristics,surgical indicators,and regional antibiotic-resistant bacterial data),and proposed a dynamic prevention and control system termed“disinfection protocols-predictive models–real-time monitoring”.The article highlighted that preoperative risk stratification,intraoperative personalized antibiotic selection,and postoperative multidimensional monitoring(encompassing inflammatory biomarkers,imaging,and microbiological testing)enabled the precise identification of high-risk patients and optimized intervention thresholds.Future research is deemed necessary to validate the synergistic effects of disinfection protocols and predictive models through large-scale multicenter studies,combined with advanced intraoperative rapid microbial detection technologies.This approach aims to establish standardized infection control protocols tailored for precision medicine and regional adaptability.Future research should prioritize validating the synergistic effects of disinfection protocols and predictive models via multi-center studies,while incorporating advanced rapid intraoperative microbial detection technologies to develop standardized infection prevention and control procedures.Such efforts will enhance the implementation of precise and regionally adaptive infection control strategies.
文摘Objective:To study the risk prediction model for multiple bronchoalveolar lavage in children with mycoplasma pneumoniae pneumonia(MPP).Methods:151 pediatric patients with MPP admitted in our hospital from July to December 2023 were selected,the incidence rate of multiple bronchoalveolar lavage was recorded.A logistic multivariate regression model was employed to analyze relevant factors and construct a risk prediction model for multiple bronchoalveolar lavage in children with MPP.Results:Among 151 children with MPP,64 cases underwent multiple bronchoalveolar lavage,accounting for 42.38%.The Logistic multivariate model analysis revealed that the pleural effusion,sepsis,and abnormally elevated serum levels of LDH and D-D were independent influence factors for multiple bronchoalveolar lavage in children with MPP(p<0.05),based on this,a Nomogram prediction model can be established.The ROC analysis results showed that the AUC of the model to judge the multiple bronchoalveolar lavage in MPP patients was 0.828(SE=0.035,95%CI=0.760-0.896,p<0.001),the sensitivity was 0.813 and the specificity was 0.759.Conclusion:The multiple bronchoscopic bronchoalveolar lavage in MPP patients are associated with the levels of LDH and D-D,as well as the presence of pleural effusion and sepsis complications,the risk prediction model established,which based on this has high accuracy.
基金supported by funds from the National Key Basic Research Program "973 project" (2015CB554000)the State Key Project Specialized for Infectious Diseases of China (No.2008ZX10002-015 and 2012ZX10002008-002)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No.81421001)
文摘Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.
文摘BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.
文摘Objective:To analyze the independent risk factors and establish a risk prediction model by investigating the readmission of elderly patients with coronary heart disease(CHD)within 1 year after discharge.Methods:A total of 480 CHD patients,who were hospitalized in the Affiliated Hospital of Hebei University from October 2019 to December 2020,were included in this study.A general data scale,mental health status scale,the Clinical Frailty Scale,Pittsburgh Sleep Quality Index,as well as the Family Adaptability and Cohesion Evaluation Scale were used to collect data.According to the number of readmissions due to CHD within 1 year after discharge,the patients were divided into two groups:the readmission group(n=212)and the no readmission group(n=268).General data,laboratory examination indicators,frailty,mental health status,sleep status,as well as family intimacy and adaptability were compared between the two groups.Logistic regression was used to analyze the independent risk factors for the readmission of these patients,and R software was used to construct a line diagram model for predicting readmission of elderly patients with CHD.Results:Five factors including body mass index(OR=1.045),low density lipoprotein(OR=1.123),frailty(OR=1.946),mental health(OR=1.099),as well as family intimacy and adaptability(OR=0.928)were included to construct the risk prediction model for the readmission of elderly patients with CHD within 1 year after discharge.The ROC curve showed that the area under the curve for predicting readmission of elderly patients with CHD was 0.816;Hosmer-Lemeshow goodness of fit test showed X2=1.456 and P=0.989;the maximum Youden index corresponding to the predicted value of risk was 0.526.The results showed that the model could accurately predict the risk of readmission in elderly patients with CHD within 1 year after discharge.Conclusion:This study constructed a line diagram model based on five independent risk factors of the readmission of elderly patients with CHD:body mass index,low density lipoprotein,frailty,mental health status,as well as family intimacy and adaptability.This model has good discrimination,accuracy,and predictive efficiency,providing reference for the early prevention and intervention of readmission in elderly patients with CHD recurrence.
文摘BACKGROUND Hepatocellular carcinoma(HCC)is difficult to diagnose with poor therapeutic effect,high recurrence rate and has a low survival rate.The survival of patients with HCC is closely related to the stage of diagnosis.At present,no specific serolo-gical indicator or method to predict HCC,early diagnosis of HCC remains a challenge,especially in China,where the situation is more severe.AIM To identify risk factors associated with HCC and establish a risk prediction model based on clinical characteristics and liver-related indicators.METHODS The clinical data of patients in the Affiliated Hospital of North Sichuan Medical College from 2016 to 2020 were collected,using a retrospective study method.The results of needle biopsy or surgical pathology were used as the grouping criteria for the experimental group and the control group in this study.Based on the time of admission,the cases were divided into training cohort(n=1739)and validation cohort(n=467).Using HCC as a dependent variable,the research indicators were incorporated into logistic univariate and multivariate analysis.An HCC risk prediction model,which was called NSMC-HCC model,was then established in training cohort and verified in validation cohort.RESULTS Logistic univariate analysis showed that,gender,age,alpha-fetoprotein,and protein induced by vitamin K absence or antagonist-II,gamma-glutamyl transferase,aspartate aminotransferase and hepatitis B surface antigen were risk factors for HCC,alanine aminotransferase,total bilirubin and total bile acid were protective factors for HCC.When the cut-off value of the NSMC-HCC model joint prediction was 0.22,the area under receiver operating characteristic curve(AUC)of NSMC-HCC model in HCC diagnosis was 0.960,with sensitivity 94.40%and specificity 95.35%in training cohort,and AUC was 0.966,with sensitivity 90.00%and specificity 94.20%in validation cohort.In early-stage HCC diagnosis,the AUC of NSMC-HCC model was 0.946,with sensitivity 85.93%and specificity 93.62%in training cohort,and AUC was 0.947,with sensitivity 89.10%and specificity 98.49%in validation cohort.CONCLUSION The newly NSMC-HCC model was an effective risk prediction model in HCC and early-stage HCC diagnosis.
基金the Hospital Management Construction Project Foundation of China Hospital Development Institute,Shanghai Jiao Tong University(No.CHDI-2019-B-14)。
文摘This paper discusses the risk factors related to gallbladder disease in Shanghai,improves the accuracy of risk prediction,and provides a theoretical basis for scientific diagnosis and universality of gallbladder disease.We selected 3462 data of middle-aged and elderly health check-up patients in a general hospital in Shanghai,and divided into gallbladder disease group according to color doppler ultrasound diagnosis results.Single-factor analysis screened out 8 important risk factors,which were used as an analysis variable of multi-layer perceptron neural network and binary logistic regression to construct the prediction model of gallbladder disease.The prediction accuracy of the multi-layer perceptron neural network risk prediction model is 76%.The area under the receiver operating characteristic curve(AUC)is 0.82,the maximum Youden index is 0.44,the sensitivity is 79.51,and the specificity is 64.23.The prediction accuracy of the multi-layer perceptron neural network model is better than that of the binary logistic regression prediction model.The overall prediction accuracy of the binary logistic regression prediction model is 75.60%,the AUC is 0.81,the maximum Youden index is 0.42,the sensitivity is 74.48,and the specificity is 57.60.In the objective risk prediction of gallbladder disease in middle-aged and elderly people in Shanghai,the risk prediction model based on the multi-layer perceptron neural network has a better prediction performance than the binary logistic regression model,which provides a theoretical basis for preventive treatment and intervention.
基金Supported by the Changning District Health Committee Excellent Innovation Talent Training Project,No.RCJD2022S01.
文摘BACKGROUND Arthritis is a prevalent and debilitating condition that affects a significant proportion of middle-aged and older adults worldwide.Characterized by chronic pain,inflammation,and joint dysfunction,arthritis can severely impact physical function,quality of life,and mental health.The overall burden of arthritis is further compounded in this population due to its frequent association with depression.As the global population both the prevalence and severity of arthritis are anticipated to increase.AIM To investigate depressive symptoms in the middle-aged and elderly arthritic population in China,a risk prediction model was constructed,and its effectiveness was validated.METHODS Using the China Health and Retirement Longitudinal Study 2018 data on middleaged and elderly arthritic individuals,the population was randomly divided into a training set(n=4349)and a validation set(n=1862)at a 7:3 ratio.Based on 10-fold cross-validation,least absolute shrinkage and selection regression was used to screen the model for the best predictor variables.Logistic regression was used to construct the nomogram model.Subject receiver operating characteristic and calibration curves were used to determine model differentiation and accuracy.Decision curve analysis was used to assess the net clinical benefit.RESULTS The prevalence of depressive symptoms in the middle-aged and elderly arthritis population in China was 47.1%,multifactorial logistic regression analyses revealed that gender,age,number of chronic diseases,number of pain sites,nighttime sleep time,education,audiological status,health status,and place of residence were all predictors of depressive symptoms.The area under the curve values for the training and validation sets were 0.740(95%confidence interval:0.726-0.755)and 0.731(95%confidence interval:0.709-0.754),respectively,indicating good model differentiation.The calibration curves demonstrated good prediction accuracy,and the decision curve analysis curves demonstrated good clinical utility.CONCLUSION The risk prediction model developed in this study has strong predictive performance and is useful for screening and assessing depression symptoms in middle-aged and elderly arthritis patients.
文摘Introduction:Prediabetes is a well-established risk factor for type 2 diabetes development.While existing risk prediction models primarily focus on the general adult population,those specifically designed for older adults remain scarce.This study aimed to develop and validate a 3-year risk prediction model for type 2 diabetes specifically for older adults with prediabetes.Methods:Data were derived from the Health Aging Evaluation Longitudinal Survey(HAELS),which included 1,127 older adults with prediabetes defined by FPG between 5.6–7.0 mmol/L at baseline(2019).Incident diabetes was defined as FPG≥7.0 mmol/L at 3-year follow-up(2022).A multivariable Cox proportional hazards regression model with stepwise variable selection was used to identify predictors and construct the prediction model.Bootstrapping was employed for internal validation.Model performance was evaluated using area under the receiver operating characteristic curve(AUC),calibration plots,and decision curve analysis(DCA).A nomogram was constructed to visualize individualized risk.Results:Seven predictors were included in the final model:age,physical activity,waist circumference,baseline FPG,triglycerides,systolic blood pressure,and dessert intake.The model demonstrated good discrimination(AUC=0.752 in the training dataset,0.793 in the validation dataset)and satisfactory calibration.DCA indicated a net clinical benefit within a threshold probability range of 0.02–0.50.Conclusion:This model,demonstrating good predictive performance and clinical utility,provides a simple and practical tool to estimate the 3-year diabetes risk in older adults with prediabetes.
基金Supported by the Jiangsu Provincial Key Research and Development Program(BE2021617).
文摘Introduction:Non-suicidal self-injury(NSSI)has become increasingly prevalent among adolescents,representing a significant public health concern with profound impacts on both physical and mental wellbeing.This study aims to determine the prevalence of NSSI among adolescents in Jiangsu Province and develop a prediction model to facilitate early identification and intervention.Methods:This study is based on the“School-based Evaluation and Response to Child Health(SEARCH)”project.A cross-sectional survey was conducted among students from 11 schools in Jiangsu Province,China in 2023.Following data cleaning,11,427 students were included in the analysis.Machine learning methods were employed to establish a risk prediction model for NSSI among adolescents.Results:The prevalence of NSSI among adolescents aged 10–18 was 12.72%.Eight key predictors of NSSI were identified:insomnia,emotional symptoms,cohesion of family environment,history of drinking alcohol,gender,conflict of family environment,conduct problems,and academic level.The XGBoost model demonstrated an area under the curve(AUC)of 0.800[95%confidence interval(CI):0.776,0.823]and an accuracy(ACC)of 0.886 in the testing set.Conclusions:This study underscores the importance of personalized prevention strategies for NSSI and highlights the necessity of implementing comprehensive behavioral interventions,including mental health support,sleep quality enhancement,and cultivation of supportive family environments.
基金Supported by Zhangjiajie"Xiao He(Young Talent)"Project,No.2024XHRC03Jishou University School-Level Research Project.
文摘BACKGROUND Aortic adverse remodeling remains a critical complication following thoracic endovascular aortic repair(TEVAR)for Stanford type B aortic dissection(TBAD),significantly impacting long-term survival.Accurate risk prediction is essential for optimized clinical management.AIM To develop and validate a logistic regression-based risk prediction model for aortic adverse remodeling following TEVAR in patients with TBAD.METHODS This retrospective observational cohort study analyzed 140 TBAD patients undergoing TEVAR at a tertiary center(2019–2024).Based on European guidelines,patients were categorized into adverse remodeling(aortic growth rate>2.9 mm/year,n=45)and favorable remodeling groups(n=95).Comprehensive variables(clinical/imaging/surgical)were analyzed using multivariable logistic regression to develop a predictive model.Model performance was assessed via receiver operating characteristic-area under the curve(AUC)and Hosmer-Lemeshow tests.RESULTS Multivariable analysis identified several strong independent predictors of negative aortic remodeling.Larger false lumen diameter at the primary entry tear[odds ratio(OR):1.561,95%CI:1.197–2.035;P=0.001]and patency of the false lumen(OR:5.639,95%CI:4.372-8.181;P=0.004)were significant risk factors.False lumen involvement extending to the thoracoabdominal aorta was identified as the strongest predictor,significantly increasing the risk of adverse remodeling(OR:11.751,95%CI:9.841-15.612;P=0.001).Conversely,false lumen involvement confined to the thoracic aorta demonstrated a significant protective effect(OR:0.925,95%CI:0.614–0.831;P=0.015).The prediction model exhibited excellent discrimination(AUC=0.968)and calibration(Hosmer-Lemeshow P=0.824).CONCLUSION This validated risk prediction model identifies aortic adverse remodeling with high accuracy using routinely available clinical parameters.False lumen involvement thoracoabdominal aorta is the strongest predictor(11.751-fold increased risk).The tool enables preoperative risk stratification to guide tailored TEVAR strategies and improve long-term outcomes.
基金Supported by the 2024 Yiwu City Research Plan Project,No.24-3-102.
文摘BACKGROUND Rabies is a zoonotic viral disease affecting the central nervous system,caused by the rabies virus,with a case-fatality rate of 100%once symptoms appear.AIM To analyze high-risk factors associated with mental disorders induced by rabies vaccination and to construct a risk prediction model to inform strategies for improving patients’mental health.METHODS Patients who received rabies vaccinations at the Department of Infusion Yiwu Central Hospital between August 2024 and July 2025 were included,totaling 384 cases.Data were collected from medical records and included demographic characteristics(age,gender,occupation),lifestyle habits,and details regarding vaccine type,dosage,and injection site.The incidence of psychiatric disorders following vaccination was assessed using standardized anxiety and depression rating scales.Patients were categorized into two groups based on the presence or absence of anxiety and depression symptoms:The psychiatric disorder group and the non-psychiatric disorder group.Differences between the two groups were compared,and high-risk factors were identified using multivariate logistic regression analysis.A predictive model was then developed based on these factors to evaluate its predictive performance.RESULTS Among the 384 patients who received rabies vaccinations,36 cases(9.38%)were diagnosed with anxiety,52 cases(13.54%)with depression,and 88 cases(22.92%)with either condition.Logistic regression analysis identified the following signi ficant risk factors for psychiatric disorders:Education level of primary school or below,exposure site at the head and neck,exposure classified as grade III,family status of divorced/widowed/unmarried/living alone,number of wounds greater than one,and low awareness of rabies prevention and control(P<0.05).The risk prediction model demonstrated good performance,with an area under the receiver operating characteristic curve of 0.859,a specificity of 74.42%,and a sensitivity of 93.02%.CONCLUSION In real-world settings,psychiatric disorders following rabies vaccination are relatively common and are associated with factors such as lower education level,higher exposure severity,vulnerable family status,and limited awareness of rabies prevention and control.The developed risk prediction model may aid in early identification of high-risk individuals and support timely clinical intervention.
基金supported by the Research and Development Program of Chuanbei Medical College(CBY23-QNA17)Nan chong City,Sichuan Province.
文摘Background and Objectives:It has been found that ICU patients may encounter various complications during enteral nutrition(EN).Of these,feeding intolerance(FI)is a common issue that often necessitates the reduction or cessation of EN.This study aims to evaluate risk prediction models for feeding intolerance(FI)in critically ill patients receiving EN by searching major public databases.Methods and Study Design:We searched for rele vant studies in Embase,PubMed,Web of Science,Chinese Biomedical Database(CBM),China National Knowledge Infrastructure(CNKI),Wanfang Data,and cqvip.com up until January 2024.Two researchers inde pendently conducted the screening and data extraction processes,and the quality of the literature was assessed us ing bias risk assessment tools.Results:A total of 13 references were included,and the subjects included patients with sepsis,pancreatitis or cerebral apoplexy;the incidence of FI was 35.2%-49.3%.The studies discussed the predictive performance of various models,with 11 studies reporting on their accuracy and calibration.The mod els demonstrated the area under the curve(AUC)of the receiver operating characteristic(ROC)curve or the con cordance index(C-index)between 0.70 and 0.91,sensitivity from 0.81 to 0.93,and specificity from 0.68 to 0.83.Conclusions:There is a critical need for risk prediction models for FI in critically ill patients on EN that are both internally and externally validated and exhibit high performance.
基金supported by grants from the Youth Cultivation Fund of Beijing Medical Ward Foundation(No.20180502)Beijing Medical Ward Foundation(No.YXJL-2020-0941-0736)。
文摘Background:Breast cancer with low-positive human epidermal growth factor receptor 2(HER2)expression has triggered further refinement of evaluation criteria for HER2 expression.We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors.Methods:Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association,from January 2015 to December 2016 were collected.The prognostic factors of these patients were analyzed.Results:Twenty-nine hospitals provided valid cases.From 2015 to 2016,a total of 25,096 cases of early-stage breast cancer were treated,7642(30.5%)of which had low-positive HER2 expression and were included in the study.After ineligible cases were excluded,6486 patients were included in the study.The median follow-up time was 57 months(4-76 months).The disease-free survival rate was 92.1%at 5 years,and the overall survival rate was 97.4%at 5 years.At the follow-up,506(7.8%)cases of metastasis and 167(2.6%)deaths were noted.Multivariate Cox regression analysis showed that tumor stage,lymphvascular invasion,and the Ki67 index were related to recurrence and metastasis(P<0.05).The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815(95%confidence interval:0.750-0.880).Conclusions:Early-stage breast cancer patients with low-positive HER2 expression account for 30.5%of all patients.Tumor stage,lymphvascular invasion,and the Ki67 index are factors affecting prognosis.The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years.Trial registration:ChiCTR.org.cn,ChiCTR2100046766.
基金Shanghai Top Priority Clinical Medical Center Project(No.2017ZZ01008-001).
文摘Background Acute myocarditis(AMC)can cause poor outcomes or even death in children.We aimed to identify AMC risk factors and create a mortality prediction model for AMC in children at hospital admission.Methods This was a single-center retrospective cohort study of AMC children hospitalized between January 2016 and January 2020.The demographics,clinical examinations,types of AMC,and laboratory results were collected at hospital admission.In-hospital survival or death was documented.Clinical characteristics associated with death were evaluated.Results Among 67 children,51 survived,and 16 died.The most common symptom was digestive disorder(67.2%).Based on the Bayesian model averaging and Hosmer–Lemeshow test,we created a final best mortality prediction model(acute myocarditis death risk score,AMCDRS)that included ten variables(male sex,fever,congestive heart failure,left-ventricular ejection fraction<50%,pulmonary edema,ventricular tachycardia,lactic acid value>4,fulminant myocarditis,abnormal creatine kinase-MB,and hypotension).Despite differences in the characteristics of the validation cohort,the model discrimination was only marginally lower,with an AUC of 0.781(95%confidence interval=0.675–0.852)compared with the derivation cohort.Model calibration likewise indicated acceptable fit(Hosmer‒Lemeshow goodness-of-fit,P¼=0.10).Conclusions Multiple factors were associated with increased mortality in children with AMC.The prediction model AMCDRS might be used at hospital admission to accurately identify AMC in children who are at an increased risk of death.
文摘This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.
文摘BACKGROUND As a minimally invasive technique,endoscopic submucosal dissection(ESD)is widely used in treating early colorectal cancer(ECRC)and precancerous lesions(PCLs).However,a common postoperative complication-delayed postoperative bleeding(DPOB)-can significantly hinder patient recovery.AIM To build and validate a predictive model for assessing post-ESD DPOB risk in ECRC and PCL patients,utilizing logistic regression methodology.METHODS A retrospective review was conducted on ECRC/PCL 302 patients who received ESD at our hospital between July 2021 and July 2024.The cohort was stratified based on the incidence of DPOB following ESD,forming DPOB and non-DPOB groups.Through allocation,they were further allocated into model and validation cohorts.Clinical variables from both cohorts were collated and subjected to univariate analysis to determine potential factors associated with post-ESD DPOB.Subsequently,we constructed a predictive model for DPOB risk employing logistic regression analysis.Model performance assessment used receiver operating characteristic curves in both the training and validation cohorts,with internal validation accomplished via 10-fold cross-validation.RESULTS The occurrence rate of DPOB was 9.93%.Univariate analysis revealed that the number of lesions,lesion size,lesion location,degree of submucosal fibrosis,and intraoperative bleeding were significantly associated with DPOB.Binary logistic regression analysis identified the number of lesions,lesion size,lesion location,and degree of submucosal fibrosis as independent DPOB determinants.A nomogram that was developed to quantify the DPOB risk exhibited that an increment in the total score corresponded to an increased risk.The model achieved area under the curve values of 0.831 and 0.821 in the model and validation groups,respectively,with P values of 0.853 and 0.203 in the Hosmer-Lemeshow test.The model demonstrated robust discriminative performance,with an average area under the curve of 0.795(95%confidence interval:0.702-0.887)in 10-fold cross-validation.CONCLUSION Collectively,the presence of multiple lesions,lesion size of≥3 cm,lesion localization in the rectum,and severe fibrosis are significant independent predictors of DPOB in patients undergoing surgery for ECRC or PCLs.The proposed risk prediction model,which integrates these factors,demonstrates excellent predictive accuracy and clinical utility,thereby providing a valuable tool for risk stratification and postoperative management in this patient population.
基金Supported by Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Special Project,No.YN2023WSSQ01State Key Laboratory of Traditional Chinese Medicine Syndrome.
文摘BACKGROUND Research has shown that several factors can influence postoperative abnormal liver function;however,most studies on this issue have focused specifically on hepatic and cardiac surgeries,leaving limited research on contributing factors in other types of surgeries.AIM To identify the risk factors for early postoperative abnormal liver function in multiple surgery types and construct a risk prediction model.METHODS This retrospective cohort study involved 3720 surgical patients from 5 surgical departments at Guangdong Provincial Hospital of Traditional Chinese Medicine.Patients were divided into abnormal(n=108)and normal(n=3612)groups based on liver function post-surgery.Univariate analysis and LASSO regression screened variables,followed by logistic regression to identify risk factors.A prediction model was constructed based on the variables selected via logistic re-gression.The goodness-of-fit of the model was evaluated using the Hosm-er–Lemeshow test,while discriminatory ability was measured by the area under the receiver operating characteristic curve.Calibration curves were plotted to visualize the consistency between predicted probabilities and observed outcomes.RESULTS The key factors contributing to abnormal liver function after surgery include elevated aspartate aminotransferase and alanine aminotransferase levels and reduced platelet counts pre-surgery,as well as the sevoflurane use during the procedure,among others.CONCLUSION The above factors collectively represent notable risk factors for postoperative liver function injury,and the prediction model developed based on these factors demonstrates strong predictive efficacy.
文摘Background The vasovagal reflex syndrome （VVRS） is common in the patiems undergoing percutaneous coronary intervemion （PCI） However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI （78.0% males, mean age 60 years） in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC） analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation （all P 〈 0.001）. The rate of VVRS [95% confidence interval （CI）] in patients receiving PCI was 4.5% （4.1%-5.6%）. Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending （LAD）, and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS （all P 〈 0.001）. We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI （c-statistic 0.76, 95% CI： 0.72-0.79, P 〈 0.001）. There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute （AUC： 0.84, 95% CI： 0.81-0.87, P 〈 0.001）. Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD.
