BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before...Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before surgery to 1 day after surgery),and to provide a basis for optimizing clinical nursing plans for elderly cataract surgery.Methods:A retrospective selection of 90 elderly patients(aged≥60 years)who underwent cataract surgery in the Ophthalmology Department of our hospital from August 2024 to December 2024 was conducted.They were divided into an observation group(n=45)and a control group(n=45)using a random number table method.The control group received routine nursing for cataract surgery,while the observation group implemented evidence-based predictive nursing intervention(including the establishment of a multidisciplinary evidence-based team,hierarchical psychological intervention,perioperative environment optimization,intraoperative personalized cooperation,and video-based health education).Psychological stress indicators[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS),General Self-Efficacy Scale(GSES)]on the 1st day before surgery and 1st day after surgery,and fluctuations of physiological indicators[Heart Rate(HR),Systolic Blood Pressure(SBP),Diastolic Blood Pressure(DBP)]on the 1st day before surgery and during surgery were compared between the two groups.Results:Before intervention,there were no statistically significant differences in SAS,SDS,GSES scores,HR,SBP,or DBP between the two groups(p>0.05);after intervention,the SAS score(33.62±5.72)and SDS score(32.14±4.86)of the observation group on the 1st day after surgery were significantly lower than those of the control group[(41.05±5.56),(43.59±4.75)],and the GSES score(31.15±3.28)was significantly higher than that of the control group(24.84±3.52)(all p<0.05);during surgery,the fluctuations of HR(74.0±6.0)beats/min,SBP(127.0±15.8)mmHg,and DBP(75.0±5.9)mmHg in the observation group were significantly smaller than those in the control group(all p<0.05).Conclusion:Evidence-based predictive nursing intervention can effectively alleviate anxiety and depression in elderly cataract patients during the perioperative period,improve self-efficacy,stabilize intraoperative physiological status,and enhance surgical cooperation,which is worthy of clinical promotion.展开更多
Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditiona...Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.展开更多
BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),w...BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gast...BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorit...Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.展开更多
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金Hospital Quality Management Research Fund Project of China Medical Quality Management Association(Project No.:YLZG202511)。
文摘Objective:To explore the impact of evidence-based predictive nursing intervention on psychological stress and physiological indicator stability of elderly cataract patients during the perioperative period(1 day before surgery to 1 day after surgery),and to provide a basis for optimizing clinical nursing plans for elderly cataract surgery.Methods:A retrospective selection of 90 elderly patients(aged≥60 years)who underwent cataract surgery in the Ophthalmology Department of our hospital from August 2024 to December 2024 was conducted.They were divided into an observation group(n=45)and a control group(n=45)using a random number table method.The control group received routine nursing for cataract surgery,while the observation group implemented evidence-based predictive nursing intervention(including the establishment of a multidisciplinary evidence-based team,hierarchical psychological intervention,perioperative environment optimization,intraoperative personalized cooperation,and video-based health education).Psychological stress indicators[Self-Rating Anxiety Scale(SAS),Self-Rating Depression Scale(SDS),General Self-Efficacy Scale(GSES)]on the 1st day before surgery and 1st day after surgery,and fluctuations of physiological indicators[Heart Rate(HR),Systolic Blood Pressure(SBP),Diastolic Blood Pressure(DBP)]on the 1st day before surgery and during surgery were compared between the two groups.Results:Before intervention,there were no statistically significant differences in SAS,SDS,GSES scores,HR,SBP,or DBP between the two groups(p>0.05);after intervention,the SAS score(33.62±5.72)and SDS score(32.14±4.86)of the observation group on the 1st day after surgery were significantly lower than those of the control group[(41.05±5.56),(43.59±4.75)],and the GSES score(31.15±3.28)was significantly higher than that of the control group(24.84±3.52)(all p<0.05);during surgery,the fluctuations of HR(74.0±6.0)beats/min,SBP(127.0±15.8)mmHg,and DBP(75.0±5.9)mmHg in the observation group were significantly smaller than those in the control group(all p<0.05).Conclusion:Evidence-based predictive nursing intervention can effectively alleviate anxiety and depression in elderly cataract patients during the perioperative period,improve self-efficacy,stabilize intraoperative physiological status,and enhance surgical cooperation,which is worthy of clinical promotion.
基金supported in part by the Natural Science Foundation of China under Grant Nos.U2468201 and 62221001ZTE Industry-University-Institute Cooperation Funds under Grant No.IA20240420002。
文摘Accurate channel state information(CSI)is crucial for 6G wireless communication systems to accommodate the growing demands of mobile broadband services.In massive multiple-input multiple-output(MIMO)systems,traditional CSI feedback approaches face challenges such as performance degradation due to feedback delay and channel aging caused by user mobility.To address these issues,we propose a novel spatio-temporal predictive network(STPNet)that jointly integrates CSI feedback and prediction modules.STPNet employs stacked Inception modules to learn the spatial correlation and temporal evolution of CSI,which captures both the local and the global spatiotemporal features.In addition,the signal-to-noise ratio(SNR)adaptive module is designed to adapt flexibly to diverse feedback channel conditions.Simulation results demonstrate that STPNet outperforms existing channel prediction methods under various channel conditions.
基金Supported by Shenzhen High-level Hospital Construction Fund.
文摘BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
文摘BACKGROUND Gastric cancer is the most common malignancy of the digestive system and surgical resection is the primary treatment.Advances in surgical technology have reduced the risk of complications after radical gastrectomy;however,post-surgical pancreatic fistula remain a serious issue.These fistulas can lead to abdominal infections,anastomotic leakage,increased costs,and pain;thus,early diagnosis and prevention are crucial for a better prognosis.Currently,C-reactive protein(CRP),procalcitonin(PCT),and total bilirubin(TBil)levels are used to predict post-operative infections and anastomotic leakage.However,their predictive value for pancreatic fistula after radical gastrectomy for gastric cancer remains unclear.The present study was conducted to determine their predictive value.AIM To determine the predictive value of CRP,PCT,and TBil levels for pancreatic fistula after gastric cancer surgery.METHODS In total,158 patients who underwent radical gastrectomy for gastric cancer at our hospital between January 2019 and January 2023 were included.The patients were assigned to a pancreatic fistula group or a non-pancreatic fistula group.Multivariate logistic analysis was conducted to assess the factors influencing development of a fistula.Receiver operating characteristic(ROC)curves were used to determine the predictive value of serum CRP,PCT,and TBil levels on day 1 postsurgery.RESULTS On day 1 post-surgery,the CRP,PCT,and TBil levels were significantly higher in the pancreatic fistula group than in the non-pancreatic fistula group(P<0.05).A higher fistula grade was associated with higher levels of the indices.Univariate analysis revealed significant differences in the presence of diabetes,hyperlipidemia,pancreatic injury,splenectomy,and the biomarker levels(P<0.05).Logistic multivariate analysis identified diabetes,hyperlipidemia,pancreatic injury,CRP level,and PCT level as independent risk factors.ROC curves yielded predictive values for CRP,PCT,and TBil levels,with the PCT level having the highest area under the curve(AUC)of 0.80[95%confidence interval(CI):0.72-0.90].Combined indicators improved the predictive value,with an AUC of 0.86(95%CI:0.78-0.93).CONCLUSION Elevated CRP,PCT,and TBil levels predict risk of pancreatic fistula post-gastrectomy for gastric cancer.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
文摘Predicting hospital readmission and length of stay(LOS)for diabetic patients is critical for improving healthcare quality,optimizing resource utilization,and reducing costs.This study leveragesmachine learning algorithms to predict 30-day readmission rates and LOS using a robust dataset comprising over 100,000 patient encounters from 130 hospitals collected over a decade.A comprehensive preprocessing pipeline,including feature selection,data transformation,and class balancing,was implemented to ensure data quality and enhance model performance.Exploratory analysis revealed key patterns,such as the influence of age and the number of diagnoses on readmission rates,guiding the development of predictive models.Rigorous validation strategies,including 5-fold cross-validation and hyperparameter tuning,were employed to ensure model reliability and generalizability.Among the models tested,the RandomForest algorithmdemonstrated superior performance,achieving 96% accuracy for predicting readmissions and 87% for LOS prediction.These results underscore the potential of predictive analytics in diabetic patient care,enabling proactive interventions,better resource allocation,and improved clinical outcomes.
