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
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 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
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
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 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
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
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 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
Kidney failure is particularly common in the United States,where it affects over 700,000 individuals.It is typically treated through repeated sessions of hemodialysis to filter and clean the blood.Hemodialysis require...Kidney failure is particularly common in the United States,where it affects over 700,000 individuals.It is typically treated through repeated sessions of hemodialysis to filter and clean the blood.Hemodialysis requires vascular access,in about 70%of cases through an arteriovenous fistula(AVF)surgically created by connecting an artery and vein.AVF take 6 weeks or more to mature.Mature fistulae often require intervention,most often percutaneous transluminal angioplasty(PTA),also known as fistulaplasty,to maintain the patency of the fistula.PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF,and many patients undergo the procedure multiple times.Although PTA is important for AVF maturation and maintenance,research into predictive models of AVF function following PTA has been limited.Therefore,in this paper we hypothesize that based on patient-specific information collected during PTA,a predictive model can be created to help improve treatment planning.We test a set of rich,multimodal data from 28 patients that includes medical history,AVF blood flow,and interventional angiographic imaging(specifically excluding any post-PTA measurements)and build deep hybrid neural networks.A hybrid model combining a 3D convolutional neural network with a multilayer perceptron to classify AVF was established.We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months.The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75,and AUROC of 0.75.These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA.These initial findings suggest that the hybrid model combining clinical data,imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis.Further study based on a large cohort is needed to refine the accuracy and model efficiency.展开更多
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.
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.
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.
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 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.
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.
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 in part by NIH Grant R01HL152270a UCLA Radiology Exploratory Research Grant Award.
文摘Kidney failure is particularly common in the United States,where it affects over 700,000 individuals.It is typically treated through repeated sessions of hemodialysis to filter and clean the blood.Hemodialysis requires vascular access,in about 70%of cases through an arteriovenous fistula(AVF)surgically created by connecting an artery and vein.AVF take 6 weeks or more to mature.Mature fistulae often require intervention,most often percutaneous transluminal angioplasty(PTA),also known as fistulaplasty,to maintain the patency of the fistula.PTA is also the first-line intervention to restore blood flow and prolong the use of an AVF,and many patients undergo the procedure multiple times.Although PTA is important for AVF maturation and maintenance,research into predictive models of AVF function following PTA has been limited.Therefore,in this paper we hypothesize that based on patient-specific information collected during PTA,a predictive model can be created to help improve treatment planning.We test a set of rich,multimodal data from 28 patients that includes medical history,AVF blood flow,and interventional angiographic imaging(specifically excluding any post-PTA measurements)and build deep hybrid neural networks.A hybrid model combining a 3D convolutional neural network with a multilayer perceptron to classify AVF was established.We found using this model that we were able to identify the association between different factors and evaluate whether the PTA procedure can maintain primary patency for more than 3 months.The testing accuracy achieved was 0.75 with a weighted F1-score of 0.75,and AUROC of 0.75.These results indicate that evaluating multimodal clinical data using artificial neural networks can predict the outcome of PTA.These initial findings suggest that the hybrid model combining clinical data,imaging and hemodynamic analysis can be useful to treatment planning for hemodialysis.Further study based on a large cohort is needed to refine the accuracy and model efficiency.
