BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artifi...BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.展开更多
Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including...Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.展开更多
Objective:Immune checkpoint inhibitor-related pneumonitis(ICIP)is a common and potentially lifethreatening adverse event with non-specific symptoms.It is of significance to identify high-risk population of ICIP.Howeve...Objective:Immune checkpoint inhibitor-related pneumonitis(ICIP)is a common and potentially lifethreatening adverse event with non-specific symptoms.It is of significance to identify high-risk population of ICIP.However,existing prediction models for ICIP are often limited by their reliance on clinically inaccessible variables and homogeneous methodologies,hindering their clinical utility.This study aimed to develop a clinical riskprediction model for ICIP in patients with gastrointestinal(GI)cancer based on four machine learning(ML)methods.Methods:We conducted a retrospective analysis of data from GI cancer patients who received immune checkpoint inhibitors(ICIs)between 2018 and 2022 in Beijing Cancer Hospital.For each patient,36 clinical indicators associated with pneumonia risk were gathered.The dataset was split into training and testing sets in a ratio of 7:3.Variable selection was first performed using Least Absolute Shrinkage and Selection Operator(LASSO)regression.Subsequently,four ML algorithms:logistic regression(LR),random forest(RF),Support vector machine(SVM),and Adaptive Boosting(AdaBoost),were employed to develop and validate ICIP prediction models.The models'performance was assessed using sensitivity,specificity,precision,F1-score,and the area under the receiver operating characteristic curve(AUC)value.The optimal cutoff point for the best model was determined and a web-based tool was developed based on it.Results:We collected medical data from 1,101 GI cancer patients.Ten predictive variables were identified as significant:gender,age,treatment line,smoking index,drinking history,lung metastasis,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,hemoglobin,and albumin.After constructing and comparing four ML models,the RF model demonstrated best performance with an AUC of 0.899.The web-based tool for ICIP risk prediction is available at https://healthy.aistarfish.com/business/pneumonia-prediction/#/home.Conclusions:We analyzed 36 clinical predictors of ICIP in 1,101 patients treated with ICIs,and 10 variables were included.The smoking index,albumin and hemoglobin emerged as novel predictors specific to GI cancers.Among the models constructed using four ML methods,the RF model showed the best performance.Additionally,a web-based tool was developed to facilitate the early clinical identification of populations at high risk of ICIP.Future directions include external validation of the model to enhance clinical usability.展开更多
Objective: To establish breastfeeding clinical holistic nursing model used in clinical nursing and teaching, it is possible to improve nurses and nursing students to master knowledge of breastfeeding, analysis, judgme...Objective: To establish breastfeeding clinical holistic nursing model used in clinical nursing and teaching, it is possible to improve nurses and nursing students to master knowledge of breastfeeding, analysis, judgment, decision-making and the ability to care-related issues, promoting pure improve breastfeeding rates. Method: 1) The North American Nursing Diagnosis Nursing Association (NANDA), Nursing Outcomes Classification (NOC) and Nursing Classification (NIC) link (NNN link) clinical clues reasoning to determine the contents of the questionnaire were used. Then, the Delphi method to care was used. 2) The survey questionnaire was designed. The content included the clinical holistic nursing care model in breastfeeding and the model’s use situation. 3) Questionnaire survey: in the national midwifery care industries, volunteers were collected who were willing to use the breastfeeding clinical holistic nursing care model in the nursing work. We issued 98 questionnaires and took back 76 valid questionnaires. Results: The construction of the breastfeeding clinical holistic nursing model includes 9 nursing diagnoses which contain the breastfeeding effective, the lack of knowledge, the decision conflicts, the risk of breast tenderness, the risk of cracked nipple, the invalid hazard occurring in breastfeeding, the risk of caregiver role strain, the risk of breast feeding jaundice and the ineffective community coping, 18 nursing outcomes and 64 nursing measures. In the composition of breastfeeding after clinical holistic nursing model in clinical practice, 100% of obstetric nurses think that the breastfeeding nursing model can promote the nursing staff to care patients according to the nursing process, can help nurses to analyze, evaluate, make decision, and care about breastfeeding related problems, as well as can promote the mother’s milk feeding rate. Conclusion: The establishment of the breastfeeding nursing model provides a learning material for obstetric breast-feeding. Moreover, nursing diagnosis, nursing outcomes and nursing measures correspond according to the form of chart, which are easy to use and find. The breastfeeding clinical holistic nursing model is practicing in clinical nursing, which can help nursing staff to improve the ability of nursing personnel according to evidence-based nursing patients, to improve the ability of nursing personnel analysis, evaluation, decision-making and nursing in breastfeeding problems and to promote the rate of breastfeeding.展开更多
Background:The high recurrence rate of hepatocellular carcinoma(HCC)following curative resection affects patient survival.The present study combined critical clinicopathological features and molecular markers to devel...Background:The high recurrence rate of hepatocellular carcinoma(HCC)following curative resection affects patient survival.The present study combined critical clinicopathological features and molecular markers to develop machine learning models to predict the risk of recurrence and mortality.We aimed to individualize risk stratification,post-surgical management strategies,and ultimately improve long-term prognosis for HCC patients with curative resections.Methods:A total of 815 HCC patients undergoing surgical resection were divided randomly into a training cohort(n=652)and a validation cohort(n=163).To build a high-accuracy recurrent/death classifier using clinicopathological characteristics and molecular biomarkers,four different machine learning models,including the Cox proportional risk model,generalized linear model,extreme gradient boosting(XGBoost)model,and random survival forest(RSF)model,were developed and comprehensively compared.The outcomes were recurrence-free survival(RFS)and overall survival(OS).Results:Factors including diabetes,albumin,tumor numbers,HCC diameter,portal vein tumor thrombus,blood loss,mismatch repair protein 2(MSH2),and epithelial membrane antigen were significantly associated with RFS,while albumin,HCC diameter,MSH2,and Barcelona Clinic Liver Cancer(BCLC)stage were significantly associated with OS.The RSF model not only grouped HCC patients into high-and lowprobability recurrence groups with significant differences in 5-year recurrence probability rate(training cohort:87.3%vs.51.5%,P<0.0001;validation cohort:75.9%vs.64.8%,P<0.0001),but also grouped HCC patients into high-and low-probability death groups with significant differences in 5-year death probability rate(training cohort:56.0%vs.15.3%,P<0.0001;validation cohort:50.0%vs.23.1%,P<0.0001).Conclusions:The RSF model accurately stratified HCC patient into high-and low-risk recurrence or death groups,which guides the surgeons to plan adjuvant therapy after surgery.展开更多
This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the au...This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the authors found that the tumor location correlated with patient prognosis following surgery.Patients with tumors situated nearer to the stomach’s proximal end were associated with shorter survival periods and poorer outcomes.Notably,gender-based differences in tumor markers,particularly carbohydrate antigen 72-4,further highlight the need for sex-specific influence on the tumor location.Despite increasing recognition of tumor location as a prognostic factor,its role remains unclear in clinical prediction models for various cancers.This letter highlights the potential of incorporating tumor location into artificial intelligence-based prognostic tools to enhance prognostic models.It also outlines a stepwise framework for developing these models,from retrospective training to prospective multicenter validation and clinical implementation.In addition,it addresses the technical,ethical,and interoperability challenges critical to successful real-world prognosis.展开更多
BACKGROUND Chemotherapy is an essential treatment for colorectal cancer(CRC)patients after surgery,but many patients do not benefit from chemotherapy because tumour heterogeneity results in varied responses.AIM To stu...BACKGROUND Chemotherapy is an essential treatment for colorectal cancer(CRC)patients after surgery,but many patients do not benefit from chemotherapy because tumour heterogeneity results in varied responses.AIM To study the effectiveness of in vitro chemosensitivity tests adenosine tripho-sphate-based tumour chemotherapy sensitivity test(ATP-TCA)for tailoring po-stoperative chemotherapy regimens for patients with CRC.METHODS Between January 2015 to December 2021,a total of 1549 CRC patients underwent surgery and in vitro chemosensitivity testing using ATP-TCA.A subset of 405 patients who met the survival assessment criteria were followed to collect data on overall survival(OS)and disease-free survival(DFS).Cox regression analysis revealed independent prognostic factors that affect OS and DFS for those re-ceiving oxaliplatin(L-OPH)and fluoropyrimidine-based regimens,aiding in the development of clinical predictive models.The relationships between the ATP-TCA results and clinical outcomes were analysed using the Kaplan-Meier method.RESULTS Tumour heterogeneity and resistance to multiple drugs were observed in 1549 patients.The sensitivity to 5-fluorouracil(5-FU)combined with L-OPH was tested among 1474 of these patients,yielding a sensitivity rate of 11.9%.ATP-TCA results were identified as an independent prognostic factor for DFS[P=0.002,hazard ratio(95%confidence interval):4.98(1.81-13.72)]in patients with resectable CRC.Compared with drug-resistant patients,sensitive CRC patients treated with 5-FU and L-OPH had significantly prolonged DFS(P=0.027).Further Kaplan-Meier analysis indicated that ATP-TCA sensitivity was significantly associated with improved OS(P=0.048)and DFS(P=0.003)in patients with stage III CRC.CONCLUSION The response of CRC patients to the combination regimen of 5-FU and L-OPH is heterogeneous.This study confirmed that the ATP-TCA is a valuable tool for predicting clinical outcomes,such as DFS,in patients with resectable CRC receiving chemotherapy.Although further validation with multicentre data is still necessary,these findings support that the ATP-TCA may function as a guiding tool for personalized chemotherapy administration,thereby optimizing treatment opportunities for patients.展开更多
BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the r...BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.展开更多
BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a...BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.展开更多
Replacing urethral tissue with functional scaffolds has been one of the challenging problems in the field of urethra reconstruction or repair over the last several decades. Various scaffold materials have been used in...Replacing urethral tissue with functional scaffolds has been one of the challenging problems in the field of urethra reconstruction or repair over the last several decades. Various scaffold materials have been used in animal studies, but clinical studies on use of scaffolds for urethral repair are scarce. The aim of this study was to review recent animal and clinical studies on the use of different scaffolds for urethral repair, and to evaluate these scaffolds based on the evidence from these studies. Pub Med and OVID databases were searched to identify relevant studies, in conjunction with further manual search. Studies that met the inclusion criteria were systematically evaluated. Of 555 identified studies, 38 were included for analysis. It was found that in both animal and clinical studies, scaffolds seeded with cells were used for repair of large segmental defects of the urethra, such as in tubular urethroplasty. When the defect area was small, cell-free scaffolds were more likely to be applied. A lot of pre-clinical and limited clinical evidence showed that natural or artificial materials could be used as scaffolds for urethral repair. Urinary tissue engineering is still in the immature stage, and the safety, efficacy, cost-effectiveness of the scaffolds are needed for further study.展开更多
In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding ma...In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.展开更多
BACKGROUND In recent years,endoscopic resection(ER)has been employed for the excision of submucosal tumors(SMTs).Nonetheless,ER in the duodenum is linked to ele-vated risks of both immediate and delayed hemorrhagic co...BACKGROUND In recent years,endoscopic resection(ER)has been employed for the excision of submucosal tumors(SMTs).Nonetheless,ER in the duodenum is linked to ele-vated risks of both immediate and delayed hemorrhagic complications and perforations.Satisfactory suturing is crucial for reducing the occurrence of complications.AIM To establish a clinical score model for supporting suture decision-making of duodenal SMTs.METHODS This study included 137 individuals diagnosed with duodenal SMTs who under-went ER.Participants were evenly divided into two groups:A training cohort(TC)comprising 95 cases and an internal validation cohort(VC)with 42 cases.Subsequently,a scoring system was formulated utilizing multivariate logistic regression analysis within the TC,which was then subjected to evaluation in the VC.RESULTS The clinical scoring system incorporated two key factors:Extraluminal growth,which was assigned 2 points,and endoscopic full-thickness resection,which was given 3 points.This model demonstrated strong predictive accuracy,as evidenced by the area under the receiver operating characteristic curve of 0.900(95%confidence interval:0.823-0.976).Additionally,the model’s goodness-of-fit was validated by the Hosmer-Lemeshow test(P=0.404).The probability of purse-string suturing in low(score 0-2)and high(score>3)categories were 3.0%and 64.3%in the TC,and 6.1%and 88.9%in the VC,respectively.CONCLUSION This scoring system may function as a beneficial instrumentality for medical practitioners,facilitating the decision-making process concerning suture techniques in the context of duodenal SMTs.展开更多
Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This re...Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This review systematically examines the global landscape of traditional medicine-modern medicine integration by analyzing policy developments,regulatory frameworks,and clinical implementation models across various regions,including Asia,Africa,Europe,and the USA.The scope of the review encompasses five key domains:(1)global policy initiatives,(2)regulatory and institutional frameworks,(3)clinical integration models,(4)impacts and outcomes of integrative practices,and(5)challenges and barriers to implementation.Based on peer-reviewed literature and official health policy documents published between 2000 and 2025,the present review investigates how countries have operationalized clinical integration models combining traditional and complementary medicine.Although interest in traditional and complementary medicine has grown worldwide,persistent challenges,such as limited scientific validation,lack of standardization,and professional resistance,continue to hinder progress.This review concludes that successful and sustainable integration requires evidence-based clinical approaches,inclusive regulatory reforms,and coordinated policy strategies.Countries such as China,India,and Brazil have made significant advances,offering valuable models for future implementation worldwide.展开更多
AIM:To develop a nomogram to predict the risk of visual impairment(VI)in patients with chronic kidney disease(CKD).METHODS:Totally 897 patients with CKD were selected from the National Health and Nutrition Examination...AIM:To develop a nomogram to predict the risk of visual impairment(VI)in patients with chronic kidney disease(CKD).METHODS:Totally 897 patients with CKD were selected from the National Health and Nutrition Examination Survey(NHANES).The training and validation sets were divided in a 7:3 ratio.Multivariate logistic regression and bidirectional stepwise regression was used to select the factor of developing nomogram.The performance of the nomogram was evaluated by receiver operator characteristic curve,calibration curve and decision curve analysis(DCA).RESULTS:Age,diastolic blood pressure,glucose,serum creatinine,income at or above poverty,and history of smoking were included in the nomogram.And the area under the receiver operating characteristic curve of the training and validation sets were 0.684 and 0.640,respectively.The fit of the model was demonstrated the calibration curve,and DCA showed the value of clinical application.CONCLUSION:The nomogram may help to screening the probability of VI in patients with CKD.Larger samples are needed to validate and improve the model to increase its efficacy.展开更多
Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and perf...Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and performance of available clinical prediction models(CPMs).Methods:A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed.Both the quality and performance of the CPMs were assessed.Results:Fifty-two CPMs were identified,of which 31 were HBV-ACLF specific.The modeling data were mostly derived from retrospective(83.87%)and single-center(96.77%)cohorts,with sample sizes ranging from 46 to 1,202.Three-month mortality was the most common endpoint.The Asian Pacific Association for the Study of the Liver consensus(51.92%)and Chinese Medical Association liver failure guidelines(40.38%)were commonly used for HBV-ACLF diagnosis.Serum bilirubin(67.74%),the international normalized ratio(54.84%),and hepatic encephalopathy(51.61%)were the most frequent variables used in models.Model discrimination was commonly evaluated(88.46%),but model calibration was seldom performed.The model for end-stage liver disease score was the most widely used(84.62%);however,varying performance was reported among the studies.Conclusions:Substantial limitations lie in the quality of HBV-ACLF-specific CPMs.Disease severity of study populations may impact model performance.The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.展开更多
BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the...BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.展开更多
Background:The yin deficiency type of perimenopausal syndrome(PMS)as a common category of PMS based on the theory of traditional Chinese medicine(TCM)has a high prevalence with severe symptoms and long course of disea...Background:The yin deficiency type of perimenopausal syndrome(PMS)as a common category of PMS based on the theory of traditional Chinese medicine(TCM)has a high prevalence with severe symptoms and long course of disease.Therefore,it is necessary to construct a prediction model to assist in diagnosis.Objective:This study aimed to investigate the independent predictors of the yin deficiency type of PMS and to develop a clinical prediction model of this disease.Methods:PMS patients who attended the Third Affiliated Hospital of Zhejiang Chinese Medical University between February 2020 and August 2023 were selected and divided chronologically into training and validation groups.Logistic regression analysis was applied in the training group to clarify the independent predictors of the yin deficiency type of PMS,and a nomogram was plotted.Internal and external validations were performed in the training and validation groups to evaluate the model’s accuracy,goodness of fit,and clinical adaptability.Results:Hot flashes and sweating(≥10 episodes/day),palpitations,emotional fluctuations,and abnormal sexual activity were independent predictors of the yin deficiency type of PMS(P>0.05).Based on the clinical prediction model constructed,the area under the receiver operating characteristic curve(AUR OC)in the training group was 0.989(95%CI 0.980–0.998),and the AUR OC in the validation group was 0.971(95%CI 0.940–0.999).This demonstrates that the model has superior prediction performance.The Hosmer-Lemeshow test was used to evaluate the model’s goodness of fit with P=0.596 for the training group and P=0.883 for the validation group,indicating a good fit.The decision curve analysis(DCA)curve and clinical impact curve(CIC)indicated good clinical adaptability.Conclusion:The model can accurately predict the occurrence of the yin deficiency type of PMS,which may help clinicians identify such patients at an early stage.展开更多
Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for ...Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.展开更多
Amyotrophic lateral sclerosis（ALS） is the most common degenerative disease of the motor neuron system. Over the last years, a growing interest was aimed to discovery new innovative and safer therapeutic approaches i...Amyotrophic lateral sclerosis（ALS） is the most common degenerative disease of the motor neuron system. Over the last years, a growing interest was aimed to discovery new innovative and safer therapeutic approaches in the ALS treatment. In this context, the bioactive compounds of Cannabis sativa have shown antioxidant, anti-inflammatory and neuroprotective effects in preclinical models of central nervous system disease. However, most of the studies proving the ability of cannabinoids in delay disease progression and prolong survival in ALS were performed in animal model, whereas the few clinical trials that investigated cannabinoids-based medicines were focused only on the alleviation of ALS-related symptoms, not on the control of disease progression. The aim of this report was to provide a short but important overview of evidences that are useful to better characterize the efficacy as well as the molecular pathways modulated by cannabinoids.展开更多
Objective To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone (rhGH) treatment in children with short stature.Methods A retrospective analysi...Objective To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone (rhGH) treatment in children with short stature.Methods A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1,2020,and January 1,2024.展开更多
文摘BACKGROUND Bleeding is one of the major complications after endoscopic submucosal dissection(ESD)in early gastric cancer(EGC)patients.There are limited studies on estimating the bleeding risk after ESD using an artificial intelligence system.AIM To derivate and verify the performance of the deep learning model and the clinical model for predicting bleeding risk after ESD in EGC patients.METHODS Patients with EGC who underwent ESD between January 2010 and June 2020 at the Samsung Medical Center were enrolled,and post-ESD bleeding(PEB)was investigated retrospectively.We split the entire cohort into a development set(80%)and a validation set(20%).The deep learning and clinical model were built on the development set and tested in the validation set.The performance of the deep learning model and the clinical model were compared using the area under the curve and the stratification of bleeding risk after ESD.RESULTS A total of 5629 patients were included,and PEB occurred in 325 patients.The area under the curve for predicting PEB was 0.71(95%confidence interval:0.63-0.78)in the deep learning model and 0.70(95%confidence interval:0.62-0.77)in the clinical model,without significant difference(P=0.730).The patients expected to the low-(<5%),intermediate-(≥5%,<9%),and high-risk(≥9%)categories were observed with actual bleeding rate of 2.2%,3.9%,and 11.6%,respectively,in the deep learning model;4.0%,8.8%,and 18.2%,respectively,in the clinical model.CONCLUSION A deep learning model can predict and stratify the bleeding risk after ESD in patients with EGC.
基金Supported by the Science Planning Project of Liaoning Province,No.2019JH2/10300031-05the National Natural Science Foundation of China,No.12171074.
文摘Artificial intelligence(AI)is rapidly transforming the landscape of hepatology by enabling automated data interpretation,early disease detection,and individualized treatment strategies.Chronic liver diseases,including non-alcoholic fatty liver disease,cirrhosis,and hepatocellular carcinoma,often progress silently and pose diagnostic challenges due to reliance on invasive biopsies and operatordependent imaging.This review explores the integration of AI across key domains such as big data analytics,deep learning-based image analysis,histopathological interpretation,biomarker discovery,and clinical prediction modeling.AI algorithms have demonstrated high accuracy in liver fibrosis staging,hepatocellular carcinoma detection,and non-alcoholic fatty liver disease risk stratification,while also enhancing survival prediction and treatment response assessment.For instance,convolutional neural networks trained on portal venous-phase computed tomography have achieved area under the curves up to 0.92 for significant fibrosis(F2-F4)and 0.89 for advanced fibrosis,with magnetic resonance imaging-based models reporting comparable performance.Advanced methodologies such as federated learning preserve patient privacy during cross-center model training,and explainable AI techniques promote transparency and clinician trust.Despite these advancements,clinical adoption remains limited by challenges including data heterogeneity,algorithmic bias,regulatory uncertainty,and lack of real-time integration into electronic health records.Looking forward,the convergence of multi-omics,imaging,and clinical data through interpretable and validated AI frameworks holds great promise for precision liver care.Continued efforts in model standardization,ethical oversight,and clinician-centered deployment will be essential to realize the full potential of AI in hepatopathy diagnosis and treatment.
基金supported by Beijing Cancer hospital(No.KC2408)。
文摘Objective:Immune checkpoint inhibitor-related pneumonitis(ICIP)is a common and potentially lifethreatening adverse event with non-specific symptoms.It is of significance to identify high-risk population of ICIP.However,existing prediction models for ICIP are often limited by their reliance on clinically inaccessible variables and homogeneous methodologies,hindering their clinical utility.This study aimed to develop a clinical riskprediction model for ICIP in patients with gastrointestinal(GI)cancer based on four machine learning(ML)methods.Methods:We conducted a retrospective analysis of data from GI cancer patients who received immune checkpoint inhibitors(ICIs)between 2018 and 2022 in Beijing Cancer Hospital.For each patient,36 clinical indicators associated with pneumonia risk were gathered.The dataset was split into training and testing sets in a ratio of 7:3.Variable selection was first performed using Least Absolute Shrinkage and Selection Operator(LASSO)regression.Subsequently,four ML algorithms:logistic regression(LR),random forest(RF),Support vector machine(SVM),and Adaptive Boosting(AdaBoost),were employed to develop and validate ICIP prediction models.The models'performance was assessed using sensitivity,specificity,precision,F1-score,and the area under the receiver operating characteristic curve(AUC)value.The optimal cutoff point for the best model was determined and a web-based tool was developed based on it.Results:We collected medical data from 1,101 GI cancer patients.Ten predictive variables were identified as significant:gender,age,treatment line,smoking index,drinking history,lung metastasis,neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio,hemoglobin,and albumin.After constructing and comparing four ML models,the RF model demonstrated best performance with an AUC of 0.899.The web-based tool for ICIP risk prediction is available at https://healthy.aistarfish.com/business/pneumonia-prediction/#/home.Conclusions:We analyzed 36 clinical predictors of ICIP in 1,101 patients treated with ICIs,and 10 variables were included.The smoking index,albumin and hemoglobin emerged as novel predictors specific to GI cancers.Among the models constructed using four ML methods,the RF model showed the best performance.Additionally,a web-based tool was developed to facilitate the early clinical identification of populations at high risk of ICIP.Future directions include external validation of the model to enhance clinical usability.
文摘Objective: To establish breastfeeding clinical holistic nursing model used in clinical nursing and teaching, it is possible to improve nurses and nursing students to master knowledge of breastfeeding, analysis, judgment, decision-making and the ability to care-related issues, promoting pure improve breastfeeding rates. Method: 1) The North American Nursing Diagnosis Nursing Association (NANDA), Nursing Outcomes Classification (NOC) and Nursing Classification (NIC) link (NNN link) clinical clues reasoning to determine the contents of the questionnaire were used. Then, the Delphi method to care was used. 2) The survey questionnaire was designed. The content included the clinical holistic nursing care model in breastfeeding and the model’s use situation. 3) Questionnaire survey: in the national midwifery care industries, volunteers were collected who were willing to use the breastfeeding clinical holistic nursing care model in the nursing work. We issued 98 questionnaires and took back 76 valid questionnaires. Results: The construction of the breastfeeding clinical holistic nursing model includes 9 nursing diagnoses which contain the breastfeeding effective, the lack of knowledge, the decision conflicts, the risk of breast tenderness, the risk of cracked nipple, the invalid hazard occurring in breastfeeding, the risk of caregiver role strain, the risk of breast feeding jaundice and the ineffective community coping, 18 nursing outcomes and 64 nursing measures. In the composition of breastfeeding after clinical holistic nursing model in clinical practice, 100% of obstetric nurses think that the breastfeeding nursing model can promote the nursing staff to care patients according to the nursing process, can help nurses to analyze, evaluate, make decision, and care about breastfeeding related problems, as well as can promote the mother’s milk feeding rate. Conclusion: The establishment of the breastfeeding nursing model provides a learning material for obstetric breast-feeding. Moreover, nursing diagnosis, nursing outcomes and nursing measures correspond according to the form of chart, which are easy to use and find. The breastfeeding clinical holistic nursing model is practicing in clinical nursing, which can help nursing staff to improve the ability of nursing personnel according to evidence-based nursing patients, to improve the ability of nursing personnel analysis, evaluation, decision-making and nursing in breastfeeding problems and to promote the rate of breastfeeding.
基金partially supported by grants from Key R&D Program of Zhejiang Province(2021C03G2013079)the General Scientific Research Project of Zhejiang Provincial Department of Education(Y202146219)the Postgraduate Education Research Project of Zhejiang University(20220326)。
文摘Background:The high recurrence rate of hepatocellular carcinoma(HCC)following curative resection affects patient survival.The present study combined critical clinicopathological features and molecular markers to develop machine learning models to predict the risk of recurrence and mortality.We aimed to individualize risk stratification,post-surgical management strategies,and ultimately improve long-term prognosis for HCC patients with curative resections.Methods:A total of 815 HCC patients undergoing surgical resection were divided randomly into a training cohort(n=652)and a validation cohort(n=163).To build a high-accuracy recurrent/death classifier using clinicopathological characteristics and molecular biomarkers,four different machine learning models,including the Cox proportional risk model,generalized linear model,extreme gradient boosting(XGBoost)model,and random survival forest(RSF)model,were developed and comprehensively compared.The outcomes were recurrence-free survival(RFS)and overall survival(OS).Results:Factors including diabetes,albumin,tumor numbers,HCC diameter,portal vein tumor thrombus,blood loss,mismatch repair protein 2(MSH2),and epithelial membrane antigen were significantly associated with RFS,while albumin,HCC diameter,MSH2,and Barcelona Clinic Liver Cancer(BCLC)stage were significantly associated with OS.The RSF model not only grouped HCC patients into high-and lowprobability recurrence groups with significant differences in 5-year recurrence probability rate(training cohort:87.3%vs.51.5%,P<0.0001;validation cohort:75.9%vs.64.8%,P<0.0001),but also grouped HCC patients into high-and low-probability death groups with significant differences in 5-year death probability rate(training cohort:56.0%vs.15.3%,P<0.0001;validation cohort:50.0%vs.23.1%,P<0.0001).Conclusions:The RSF model accurately stratified HCC patient into high-and low-risk recurrence or death groups,which guides the surgeons to plan adjuvant therapy after surgery.
基金Supported by Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality,No.23ZR1458300Key Discipline Project of Shanghai Municipal Health System,No.2024ZDXK0004+1 种基金Doctoral Innovation Talent Base Project for Diagnosis and Treatment of Chronic Liver Diseases,No.RCJD2021B02Pujiang Project of Shanghai Magnolia Talent Plan,No.24PJD098.
文摘This letter is a commentary on the findings of Huang et al,who emphasize the prognostic value of tumor location in gastric cancer.Analyzing data from 3287 patients using Kaplan-Meier and multivariate Cox models,the authors found that the tumor location correlated with patient prognosis following surgery.Patients with tumors situated nearer to the stomach’s proximal end were associated with shorter survival periods and poorer outcomes.Notably,gender-based differences in tumor markers,particularly carbohydrate antigen 72-4,further highlight the need for sex-specific influence on the tumor location.Despite increasing recognition of tumor location as a prognostic factor,its role remains unclear in clinical prediction models for various cancers.This letter highlights the potential of incorporating tumor location into artificial intelligence-based prognostic tools to enhance prognostic models.It also outlines a stepwise framework for developing these models,from retrospective training to prospective multicenter validation and clinical implementation.In addition,it addresses the technical,ethical,and interoperability challenges critical to successful real-world prognosis.
基金Supported by the National Natural Science Foundation of China,No.U24A20765 and No.T2321005Jiangsu Provincial Science and Technology Plan Special Fund,No.BM2023003+5 种基金Jiangsu Provincial Medical Key Discipline,No.ZDXK202247the Priority Academic Program Development of the Jiangsu Higher Education InstitutesJiangsu Engineering Research Center on Drug Evaluation and Translation of Organoids/Organ Chip(2024)the Science and Technology Plan of Suzhou,No.SKYD2023183the Research Project Established by Chinese Pharmaceutical Association Hospital Pharmacy Department,No.CPA-Z05-ZC-2023002Gusu Health Talent Research Project,No.GSWS2022015.
文摘BACKGROUND Chemotherapy is an essential treatment for colorectal cancer(CRC)patients after surgery,but many patients do not benefit from chemotherapy because tumour heterogeneity results in varied responses.AIM To study the effectiveness of in vitro chemosensitivity tests adenosine tripho-sphate-based tumour chemotherapy sensitivity test(ATP-TCA)for tailoring po-stoperative chemotherapy regimens for patients with CRC.METHODS Between January 2015 to December 2021,a total of 1549 CRC patients underwent surgery and in vitro chemosensitivity testing using ATP-TCA.A subset of 405 patients who met the survival assessment criteria were followed to collect data on overall survival(OS)and disease-free survival(DFS).Cox regression analysis revealed independent prognostic factors that affect OS and DFS for those re-ceiving oxaliplatin(L-OPH)and fluoropyrimidine-based regimens,aiding in the development of clinical predictive models.The relationships between the ATP-TCA results and clinical outcomes were analysed using the Kaplan-Meier method.RESULTS Tumour heterogeneity and resistance to multiple drugs were observed in 1549 patients.The sensitivity to 5-fluorouracil(5-FU)combined with L-OPH was tested among 1474 of these patients,yielding a sensitivity rate of 11.9%.ATP-TCA results were identified as an independent prognostic factor for DFS[P=0.002,hazard ratio(95%confidence interval):4.98(1.81-13.72)]in patients with resectable CRC.Compared with drug-resistant patients,sensitive CRC patients treated with 5-FU and L-OPH had significantly prolonged DFS(P=0.027).Further Kaplan-Meier analysis indicated that ATP-TCA sensitivity was significantly associated with improved OS(P=0.048)and DFS(P=0.003)in patients with stage III CRC.CONCLUSION The response of CRC patients to the combination regimen of 5-FU and L-OPH is heterogeneous.This study confirmed that the ATP-TCA is a valuable tool for predicting clinical outcomes,such as DFS,in patients with resectable CRC receiving chemotherapy.Although further validation with multicentre data is still necessary,these findings support that the ATP-TCA may function as a guiding tool for personalized chemotherapy administration,thereby optimizing treatment opportunities for patients.
文摘BACKGROUND Enteral nutrition(EN)is essential for critically ill patients.However,some patients will have enteral feeding intolerance(EFI)in the process of EN.AIM To develop a clinical prediction model to predict the risk of EFI in patients receiving EN in the intensive care unit.METHODS A prospective cohort study was performed.The enrolled patients’basic information,medical status,nutritional support,and gastrointestinal(GI)symptoms were recorded.The baseline data and influencing factors were compared.Logistic regression analysis was used to establish the model,and the bootstrap resampling method was used to conduct internal validation.RESULTS The sample cohort included 203 patients,and 37.93%of the patients were diagnosed with EFI.After the final regression analysis,age,GI disease,early feeding,mechanical ventilation before EN started,and abnormal serum sodium were identified.In the internal validation,500 bootstrap resample samples were performed,and the area under the curve was 0.70(95%CI:0.63-0.77).CONCLUSION This clinical prediction model can be applied to predict the risk of EFI.
文摘BACKGROUND Choledocholithiasis is a common clinical bile duct disease,laparoscopic choledocholithotomy is the main clinical treatment method for choledocho-lithiasis.However,the recurrence of postoperative stones is a big challenge for patients and doctors.AIM To explore the related risk factors of gallstone recurrence after laparoscopic choledocholithotomy,establish and evaluate a clinical prediction model.METHODS A total of 254 patients who underwent laparoscopic choledocholithotomy in the First Affiliated Hospital of Ningbo University from December 2017 to December 2020 were selected as the research subjects.Clinical data of the patients were collected,and the recurrence of gallstones was recorded based on the postope-rative follow-up.The results were analyzed and a clinical prediction model was established.RESULTS Postoperative stone recurrence rate was 10.23%(26 patients).Multivariate Logistic regression analysis showed that cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube were risk factors associated with postoperative recurrence(P<0.05).The clinical prediction model was ln(p/1-p)=-6.853+1.347×cholangitis+1.535×choledochal diameter+2.176×stone diameter+1.784×stone number+2.242×lithotripsy+0.021×preoperative total bilirubin+2.185×T tube.CONCLUSION Cholangitis,the diameter of the common bile duct,the diameter of the stone,number of stones,lithotripsy,preoperative total bilirubin,and T tube are the associated risk factors for postoperative recurrence of gallstone.The prediction model in this study has a good prediction effect,which has a certain reference value for recurrence of gallstone after laparoscopic choledocholi-thotomy.
文摘Replacing urethral tissue with functional scaffolds has been one of the challenging problems in the field of urethra reconstruction or repair over the last several decades. Various scaffold materials have been used in animal studies, but clinical studies on use of scaffolds for urethral repair are scarce. The aim of this study was to review recent animal and clinical studies on the use of different scaffolds for urethral repair, and to evaluate these scaffolds based on the evidence from these studies. Pub Med and OVID databases were searched to identify relevant studies, in conjunction with further manual search. Studies that met the inclusion criteria were systematically evaluated. Of 555 identified studies, 38 were included for analysis. It was found that in both animal and clinical studies, scaffolds seeded with cells were used for repair of large segmental defects of the urethra, such as in tubular urethroplasty. When the defect area was small, cell-free scaffolds were more likely to be applied. A lot of pre-clinical and limited clinical evidence showed that natural or artificial materials could be used as scaffolds for urethral repair. Urinary tissue engineering is still in the immature stage, and the safety, efficacy, cost-effectiveness of the scaffolds are needed for further study.
基金Supported by the National Natural Science Foundation of China,No.82100599 and No.81960112the Jiangxi Provincial Department of Science and Technology,No.20242BAB26122+1 种基金the Science and Technology Plan of Jiangxi Provincial Administration of Traditional Chinese Medicine,No.2023Z021the Project of Jiangxi Provincial Academic and Technical Leaders Training Program for Major Disciplines,No.20243BCE51001.
文摘In this letter,we explore into the potential role of the recent study by Zeng et al.Rectal neuroendocrine tumours(rNETs)are rare,originate from peptidergic neurons and neuroendocrine cells,and express corresponding markers.Although most rNETs patients have a favourable prognosis,the median survival period significantly decreases when high-risk factors,such as larger tumours,poorer differentiation,and lymph node metastasis exist,are present.Clinical prediction models play a vital role in guiding diagnosis and prognosis in health care,but their complex calculation formulae limit clinical use.Moreover,the prognostic models that have been developed for rNETs to date still have several limitations,such as insufficient sample sizes and the lack of external validation.A high-quality prognostic model for rNETs would guide treatment and follow-up,enabling the precise formulation of individual patient treatment and follow-up plans.The future development of models for rNETs should involve closer collab-oration with statistical experts,which would allow the construction of clinical prediction models to be standardized and robust,accurate,and highly general-izable prediction models to be created,ultimately achieving the goal of precision medicine.
基金Supported by National Natural Science Foundation of China,No.82170555Shanghai Academic/Technology Research Leader,No.22XD1422400+2 种基金Shanghai“Rising Stars of Medical Talent”Youth Development Program,No.20224Z0005the 74th General Support of China Postdoctoral Science Foundation,No.2023M740675Outstanding Resident Clinical Postdoctoral Program of Zhongshan Hospital Affiliated to Fudan University.
文摘BACKGROUND In recent years,endoscopic resection(ER)has been employed for the excision of submucosal tumors(SMTs).Nonetheless,ER in the duodenum is linked to ele-vated risks of both immediate and delayed hemorrhagic complications and perforations.Satisfactory suturing is crucial for reducing the occurrence of complications.AIM To establish a clinical score model for supporting suture decision-making of duodenal SMTs.METHODS This study included 137 individuals diagnosed with duodenal SMTs who under-went ER.Participants were evenly divided into two groups:A training cohort(TC)comprising 95 cases and an internal validation cohort(VC)with 42 cases.Subsequently,a scoring system was formulated utilizing multivariate logistic regression analysis within the TC,which was then subjected to evaluation in the VC.RESULTS The clinical scoring system incorporated two key factors:Extraluminal growth,which was assigned 2 points,and endoscopic full-thickness resection,which was given 3 points.This model demonstrated strong predictive accuracy,as evidenced by the area under the receiver operating characteristic curve of 0.900(95%confidence interval:0.823-0.976).Additionally,the model’s goodness-of-fit was validated by the Hosmer-Lemeshow test(P=0.404).The probability of purse-string suturing in low(score 0-2)and high(score>3)categories were 3.0%and 64.3%in the TC,and 6.1%and 88.9%in the VC,respectively.CONCLUSION This scoring system may function as a beneficial instrumentality for medical practitioners,facilitating the decision-making process concerning suture techniques in the context of duodenal SMTs.
文摘Globally,the integration of traditional medicine and modern medicine has been recognized as a global health priority aimed at improving healthcare accessibility,cultural relevance,and therapeutic effectiveness.This review systematically examines the global landscape of traditional medicine-modern medicine integration by analyzing policy developments,regulatory frameworks,and clinical implementation models across various regions,including Asia,Africa,Europe,and the USA.The scope of the review encompasses five key domains:(1)global policy initiatives,(2)regulatory and institutional frameworks,(3)clinical integration models,(4)impacts and outcomes of integrative practices,and(5)challenges and barriers to implementation.Based on peer-reviewed literature and official health policy documents published between 2000 and 2025,the present review investigates how countries have operationalized clinical integration models combining traditional and complementary medicine.Although interest in traditional and complementary medicine has grown worldwide,persistent challenges,such as limited scientific validation,lack of standardization,and professional resistance,continue to hinder progress.This review concludes that successful and sustainable integration requires evidence-based clinical approaches,inclusive regulatory reforms,and coordinated policy strategies.Countries such as China,India,and Brazil have made significant advances,offering valuable models for future implementation worldwide.
基金Supported by Key Research and Development Program of Hubei Province(No.2022BCA011).
文摘AIM:To develop a nomogram to predict the risk of visual impairment(VI)in patients with chronic kidney disease(CKD).METHODS:Totally 897 patients with CKD were selected from the National Health and Nutrition Examination Survey(NHANES).The training and validation sets were divided in a 7:3 ratio.Multivariate logistic regression and bidirectional stepwise regression was used to select the factor of developing nomogram.The performance of the nomogram was evaluated by receiver operator characteristic curve,calibration curve and decision curve analysis(DCA).RESULTS:Age,diastolic blood pressure,glucose,serum creatinine,income at or above poverty,and history of smoking were included in the nomogram.And the area under the receiver operating characteristic curve of the training and validation sets were 0.684 and 0.640,respectively.The fit of the model was demonstrated the calibration curve,and DCA showed the value of clinical application.CONCLUSION:The nomogram may help to screening the probability of VI in patients with CKD.Larger samples are needed to validate and improve the model to increase its efficacy.
基金the Chinese National Natural Science Foundation(Nos.81670567 and 81870425)the Fundamental Research Funds for the Central Universities.
文摘Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and performance of available clinical prediction models(CPMs).Methods:A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed.Both the quality and performance of the CPMs were assessed.Results:Fifty-two CPMs were identified,of which 31 were HBV-ACLF specific.The modeling data were mostly derived from retrospective(83.87%)and single-center(96.77%)cohorts,with sample sizes ranging from 46 to 1,202.Three-month mortality was the most common endpoint.The Asian Pacific Association for the Study of the Liver consensus(51.92%)and Chinese Medical Association liver failure guidelines(40.38%)were commonly used for HBV-ACLF diagnosis.Serum bilirubin(67.74%),the international normalized ratio(54.84%),and hepatic encephalopathy(51.61%)were the most frequent variables used in models.Model discrimination was commonly evaluated(88.46%),but model calibration was seldom performed.The model for end-stage liver disease score was the most widely used(84.62%);however,varying performance was reported among the studies.Conclusions:Substantial limitations lie in the quality of HBV-ACLF-specific CPMs.Disease severity of study populations may impact model performance.The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.
基金Supported by National Natural Science Foundation of China,No.81900641the Research Funding of Peking University,BMU2021MX020 and BMU2022MX008。
文摘BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.
基金supported by Zhejiang Province traditional Chinese medicine modernization project.(No.2022ZX011).
文摘Background:The yin deficiency type of perimenopausal syndrome(PMS)as a common category of PMS based on the theory of traditional Chinese medicine(TCM)has a high prevalence with severe symptoms and long course of disease.Therefore,it is necessary to construct a prediction model to assist in diagnosis.Objective:This study aimed to investigate the independent predictors of the yin deficiency type of PMS and to develop a clinical prediction model of this disease.Methods:PMS patients who attended the Third Affiliated Hospital of Zhejiang Chinese Medical University between February 2020 and August 2023 were selected and divided chronologically into training and validation groups.Logistic regression analysis was applied in the training group to clarify the independent predictors of the yin deficiency type of PMS,and a nomogram was plotted.Internal and external validations were performed in the training and validation groups to evaluate the model’s accuracy,goodness of fit,and clinical adaptability.Results:Hot flashes and sweating(≥10 episodes/day),palpitations,emotional fluctuations,and abnormal sexual activity were independent predictors of the yin deficiency type of PMS(P>0.05).Based on the clinical prediction model constructed,the area under the receiver operating characteristic curve(AUR OC)in the training group was 0.989(95%CI 0.980–0.998),and the AUR OC in the validation group was 0.971(95%CI 0.940–0.999).This demonstrates that the model has superior prediction performance.The Hosmer-Lemeshow test was used to evaluate the model’s goodness of fit with P=0.596 for the training group and P=0.883 for the validation group,indicating a good fit.The decision curve analysis(DCA)curve and clinical impact curve(CIC)indicated good clinical adaptability.Conclusion:The model can accurately predict the occurrence of the yin deficiency type of PMS,which may help clinicians identify such patients at an early stage.
基金supported by the National Natural Science Fund(no.82072200,82200169).
文摘Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.
文摘Amyotrophic lateral sclerosis（ALS） is the most common degenerative disease of the motor neuron system. Over the last years, a growing interest was aimed to discovery new innovative and safer therapeutic approaches in the ALS treatment. In this context, the bioactive compounds of Cannabis sativa have shown antioxidant, anti-inflammatory and neuroprotective effects in preclinical models of central nervous system disease. However, most of the studies proving the ability of cannabinoids in delay disease progression and prolong survival in ALS were performed in animal model, whereas the few clinical trials that investigated cannabinoids-based medicines were focused only on the alleviation of ALS-related symptoms, not on the control of disease progression. The aim of this report was to provide a short but important overview of evidences that are useful to better characterize the efficacy as well as the molecular pathways modulated by cannabinoids.
文摘Objective To develop and validate clinical predictive models for identifying poor short-term response to recombinant human growth hormone (rhGH) treatment in children with short stature.Methods A retrospective analysis was conducted on 118 children diagnosed with growth hormone deficiency or idiopathic short stature who were treated at the First Affiliated Hospital of Zhengzhou University and two other hospitals between January 1,2020,and January 1,2024.
