AIM:To define the predictive factors of severe retinopathy of prematurity(ROP)and develop a nomogram for predicting severe ROP in southeast China.METHODS:Totally 554 infants diagnosed with ROP hospitalized in the Seco...AIM:To define the predictive factors of severe retinopathy of prematurity(ROP)and develop a nomogram for predicting severe ROP in southeast China.METHODS:Totally 554 infants diagnosed with ROP hospitalized in the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University and hospitalized in Taizhou Women and Children’s Hospital were included.Clinical data and 43 candidate predictive factors of ROP infants were collected retrospectively.Logistic regression model was used to identify predictive factors of severe ROP and to propose a nomogram for individual risk prediction,which was compared with WINROP model and Digirop-Birth model.RESULTS:Infants from the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University(n=478)were randomly allocated into training(n=402)and internal validation group(n=76).Infants from Taizhou Women and Children’s Hospital were set as external validation group(n=76).Severe ROP were found in 52 of 402 infants,12 of 76 infants,and 7 of 76 infants in training group,internal validation group,and external validation group,respectively.Birth weight[odds ratio(OR),0.997;95%confidence interval(CI),0.996-0.999;P<0.001],multiple births(OR,1.885;95%CI,1.013-3.506;P=0.045),and non-invasive ventilation(OR,0.288;95%CI,0.146-0.570;P<0.001)were identified as predictive factors for the prediction of severe ROP,by univariate analysis and multivariate analysis.For predicting severe ROP based on the internal validation group,the areas under receiver operating characteristic curve(AUC)was 78.1(95%CI,64.2-92.0)for the nomogram,32.9(95%CI,15.3-50.5)for WINROP model,70.2(95%CI,55.8-84.6)for Digirop-Birth model.In external validation group,AUC of the nomogram was also higher than that of WINROP model and Digirop-Birth model(80.2 versus 51.1 and 63.4).The decision curve analysis of the nomogram demonstrated better clinical efficacy than that of WINROP model and Digirop-Birth model.The calibration curves demonstrated a good consistency between the actual severe ROP incidence and the predicted probability.CONCLUSION:Birth weight,multiple births,and noninvasive ventilation are independent predictors of severe ROP.The nomogram has a good ability to predict severe ROP and performed well on internal validation and external validation in southeast China.展开更多
BACKGROUND Neovascular glaucoma(NVG)is likely to occur after pars plana vitrectomy(PPV)for diabetic retinopathy(DR)in some patients,thus reducing the expected benefit.Understanding the risk factors for NVG occurrence ...BACKGROUND Neovascular glaucoma(NVG)is likely to occur after pars plana vitrectomy(PPV)for diabetic retinopathy(DR)in some patients,thus reducing the expected benefit.Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.AIM To develop a visual risk profile model to explore factors influencing DR after surgery.METHODS We retrospectively selected 151 patients with DR undergoing PPV.The patients were divided into the NVG(NVG occurrence)and No-NVG(No NVG occurrence)groups according to the occurrence of NVG within 6 months after surgery.Independent risk factors for postoperative NVG were screened by logistic regression.A nomogram prediction model was established using R software,and the model’s prediction accuracy was verified internally and externally,involving the receiver operator characteristic curve and correction curve.RESULTS After importing the data into a logistic regression model,we concluded that a posterior capsular defect,preoperative vascular endothelial growth factor≥302.90 pg/mL,glycosylated hemoglobin≥9.05%,aqueous fluid interleukin 6(IL-6)≥53.27 pg/mL,and aqueous fluid IL-10≥9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR(P<0.05).A nomogram model was established based on the aforementioned independent risk factors,and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model.The area under the curve(AUC),sensitivity,and specificity of the model were 0.962[95%confidence interval(95%CI):0.932-0.991],91.5%,and 82.3%,respectively.The AUC,sensitivity,and specificity of the external validation were 0.878(95%CI:0.746-0.982),66.7%,and 95.7%,respectively.CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy.This study can help formulate relevant preventive and treatment measures.展开更多
BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tum...BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tumor areas,no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.AIM To develop and evaluate nomograms for predicting the risk of DM and person-alized prognosis in patients with duodenal cancer.METHODS Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer,and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM.Two novel nomograms were established,and the results were evaluated by receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS A total of 2603 patients with duodenal cancer were included,of whom 457 cases(17.56%)had DM at the time of diagnosis.Logistic analysis revealed independent risk factors for DM in duodenal cancer patients,including gender,grade,tumor size,T stage,and N stage(P<0.05).Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM,including age,histological type,T stage,tumor grade,tumor size,bone metastasis,chemotherapy,and surgery(P<0.05).The accuracy of the nomograms was validated in the training set,validation set,and expanded testing set using ROC curves,calibration curves,and DCA curves.The results of Kaplan-Meier survival curves(P<0.001)indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.CONCLUSION The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM,potentially enhancing clinical decision-making.展开更多
BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further stu...BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.展开更多
BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for succes...BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.展开更多
BACKGROUND The efficacy of neoadjuvant chemotherapy(NAC)in advanced gastric cancer(GC)is still a controversial issue.AIM To find factors associated with chemosensitivity to NAC treatment and to provide the optimal the...BACKGROUND The efficacy of neoadjuvant chemotherapy(NAC)in advanced gastric cancer(GC)is still a controversial issue.AIM To find factors associated with chemosensitivity to NAC treatment and to provide the optimal therapeutic strategies for GC patients receiving NAC.METHODS The clinical information was collected from 230 GC patients who received NAC treatment at the Central South University Xiangya School of Medicine Affiliated Haikou Hospital from January 2016 to December 2020.Least absolute shrinkage and selection operator logistic regression analysis was used to find the possible predictors.A nomogram model was employed to predict the response to NAC.RESULTS In total 230 patients were finally included in this study,including 154 males(67.0%)and 76 females(33.0%).The mean age was(59.37±10.60)years,ranging from 24 years to 80 years.According to the tumor regression grade standard,there were 95 cases in the obvious response group(grade 0 or grade 1)and 135 cases in the poor response group(grade 2 or grade 3).The obvious response rate was 41.3%.Least absolute shrinkage and selection operator analysis showed that four risk factors significantly related to the efficacy of NAC were tumor location(P<0.001),histological differentiation(P=0.001),clinical T stage(P=0.008),and carbohydrate antigen 724(P=0.008).The C-index for the prediction nomogram was 0.806.The calibration curve revealed that the predicted value exhibited good agreement with the actual value.Decision curve analysis showed that the nomogram had a good value in clinical application.CONCLUSION A nomogram combining tumor location,histological differentiation,clinical T stage,and carbohydrate antigen 724 showed satisfactory predictive power to the response of NAC and can be used by gastrointestinal surgeons to determine the optimal treatment strategies for advanced GC patients.展开更多
BACKGROUND Gastric cancer(GC)is prevalent and aggressive,especially when patients have distant lung metastases,which often places patients into advanced stages.By identifying prognostic variables for lung metastasis i...BACKGROUND Gastric cancer(GC)is prevalent and aggressive,especially when patients have distant lung metastases,which often places patients into advanced stages.By identifying prognostic variables for lung metastasis in GC patients,it may be po-ssible to construct a good prediction model for both overall survival(OS)and the cumulative incidence prediction(CIP)plot of the tumour.AIM To investigate the predictors of GC with lung metastasis(GCLM)to produce nomograms for OS and generate CIP by using cancer-specific survival(CSS)data.METHODS Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance,epidemiology,and end results program database.The major observational endpoint was OS;hence,patients were se-parated into training and validation groups.Correlation analysis determined va-rious connections.Univariate and multivariate Cox analyses validated the independent predictive factors.Nomogram distinction and calibration were performed with the time-dependent area under the curve(AUC)and calibration curves.To evaluate the accuracy and clinical usefulness of the nomograms,decision curve analysis(DCA)was performed.The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer(AJCC)staging system by utilizing Net Reclassification Improvement(NRI)and Integrated Discrimination Improvement(IDI).Finally,the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.RESULTS For the purpose of creating the OS nomogram,a CIP plot based on CSS was generated.Cox multivariate regression analysis identified eleven significant prognostic factors(P<0.05)related to liver metastasis,bone metastasis,primary site,surgery,regional surgery,treatment sequence,chemotherapy,radiotherapy,positive lymph node count,N staging,and time from diagnosis to treatment.It was clear from the DCA(net benefit>0),time-de-pendent ROC curve(training/validation set AUC>0.7),and calibration curve(reliability slope closer to 45 degrees)results that the OS nomogram demonstrated a high level of predictive efficiency.The OS prediction model(New Model AUC=0.83)also performed much better than the old Cox-AJCC model(AUC difference between the new model and the old model greater than 0)in terms of risk stratification(P<0.0001)and verification using the IDI and NRI.CONCLUSION The OS nomogram for GCLM successfully predicts 1-and 3-year OS.Moreover,this approach can help to ap-propriately classify patients into high-risk and low-risk groups,thereby guiding treatment.展开更多
Transoral endoscopic resections in treating upper gastrointestinal submucosal lesions have the advantages of maintaining the integrity of the gastrointestinal lumen,avoiding perforation and reducing gastrointestinal f...Transoral endoscopic resections in treating upper gastrointestinal submucosal lesions have the advantages of maintaining the integrity of the gastrointestinal lumen,avoiding perforation and reducing gastrointestinal fistulae.They are becoming more widely used in clinical practice,but,they may also present a variety of complications.Gas-related complications are one of the most common,which can be left untreated if the symptoms are mild,but in severe cases,they can lead to rapid changes in the respiratory and circulatory systems in a short period,which can be life-threatening.Therefore,it is important to predict the occurrence of gas-related complications early and take preventive measures actively.Based on the authors'results in the prepublication of the article“Nomogram to predict gas-related complications during transoral endoscopic resection of upper gastrointestinal submucosal lesions,”and in conjunction with our evaluation and additions to the relevant content,radiographs may help screen patients at high risk for gas-related complications.Controlling blood glucose levels,shortening the duration of surgery,and choosing the most appropriate surgical resection may positively impact the prognosis of patients at high risk for gas-related complications during transoral endoscopic resection of upper gastrointestinal submucosal lesions.展开更多
基金Supported by Wenzhou Science and Technology Project(No.Y20190173).
文摘AIM:To define the predictive factors of severe retinopathy of prematurity(ROP)and develop a nomogram for predicting severe ROP in southeast China.METHODS:Totally 554 infants diagnosed with ROP hospitalized in the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University and hospitalized in Taizhou Women and Children’s Hospital were included.Clinical data and 43 candidate predictive factors of ROP infants were collected retrospectively.Logistic regression model was used to identify predictive factors of severe ROP and to propose a nomogram for individual risk prediction,which was compared with WINROP model and Digirop-Birth model.RESULTS:Infants from the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University(n=478)were randomly allocated into training(n=402)and internal validation group(n=76).Infants from Taizhou Women and Children’s Hospital were set as external validation group(n=76).Severe ROP were found in 52 of 402 infants,12 of 76 infants,and 7 of 76 infants in training group,internal validation group,and external validation group,respectively.Birth weight[odds ratio(OR),0.997;95%confidence interval(CI),0.996-0.999;P<0.001],multiple births(OR,1.885;95%CI,1.013-3.506;P=0.045),and non-invasive ventilation(OR,0.288;95%CI,0.146-0.570;P<0.001)were identified as predictive factors for the prediction of severe ROP,by univariate analysis and multivariate analysis.For predicting severe ROP based on the internal validation group,the areas under receiver operating characteristic curve(AUC)was 78.1(95%CI,64.2-92.0)for the nomogram,32.9(95%CI,15.3-50.5)for WINROP model,70.2(95%CI,55.8-84.6)for Digirop-Birth model.In external validation group,AUC of the nomogram was also higher than that of WINROP model and Digirop-Birth model(80.2 versus 51.1 and 63.4).The decision curve analysis of the nomogram demonstrated better clinical efficacy than that of WINROP model and Digirop-Birth model.The calibration curves demonstrated a good consistency between the actual severe ROP incidence and the predicted probability.CONCLUSION:Birth weight,multiple births,and noninvasive ventilation are independent predictors of severe ROP.The nomogram has a good ability to predict severe ROP and performed well on internal validation and external validation in southeast China.
基金Supported by the Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-037A.
文摘BACKGROUND Neovascular glaucoma(NVG)is likely to occur after pars plana vitrectomy(PPV)for diabetic retinopathy(DR)in some patients,thus reducing the expected benefit.Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.AIM To develop a visual risk profile model to explore factors influencing DR after surgery.METHODS We retrospectively selected 151 patients with DR undergoing PPV.The patients were divided into the NVG(NVG occurrence)and No-NVG(No NVG occurrence)groups according to the occurrence of NVG within 6 months after surgery.Independent risk factors for postoperative NVG were screened by logistic regression.A nomogram prediction model was established using R software,and the model’s prediction accuracy was verified internally and externally,involving the receiver operator characteristic curve and correction curve.RESULTS After importing the data into a logistic regression model,we concluded that a posterior capsular defect,preoperative vascular endothelial growth factor≥302.90 pg/mL,glycosylated hemoglobin≥9.05%,aqueous fluid interleukin 6(IL-6)≥53.27 pg/mL,and aqueous fluid IL-10≥9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR(P<0.05).A nomogram model was established based on the aforementioned independent risk factors,and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model.The area under the curve(AUC),sensitivity,and specificity of the model were 0.962[95%confidence interval(95%CI):0.932-0.991],91.5%,and 82.3%,respectively.The AUC,sensitivity,and specificity of the external validation were 0.878(95%CI:0.746-0.982),66.7%,and 95.7%,respectively.CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy.This study can help formulate relevant preventive and treatment measures.
基金Supported by State Administration of Traditional Chinese Medicine Base Construction Stomach Cancer Special Fund,No.Y2020CX57Jiangsu Provincial Graduate Research and Practical Innovation Program Project,No.SJCX23-0799.
文摘BACKGROUND Duodenal cancer is one of the most common subtypes of small intestinal cancer,and distant metastasis(DM)in this type of cancer still leads to poor prognosis.Although nomograms have recently been used in tumor areas,no studies have focused on the diagnostic and prognostic evaluation of DM in patients with primary duodenal cancer.AIM To develop and evaluate nomograms for predicting the risk of DM and person-alized prognosis in patients with duodenal cancer.METHODS Data on duodenal cancer patients diagnosed between 2010 and 2019 were extracted from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate logistic regression analyses were used to identify independent risk factors for DM in patients with duodenal cancer,and univariate and multivariate Cox proportional hazards regression analyses were used to determine independent prognostic factors in duodenal cancer patients with DM.Two novel nomograms were established,and the results were evaluated by receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).RESULTS A total of 2603 patients with duodenal cancer were included,of whom 457 cases(17.56%)had DM at the time of diagnosis.Logistic analysis revealed independent risk factors for DM in duodenal cancer patients,including gender,grade,tumor size,T stage,and N stage(P<0.05).Univariate and multivariate COX analyses further identified independent prognostic factors for duodenal cancer patients with DM,including age,histological type,T stage,tumor grade,tumor size,bone metastasis,chemotherapy,and surgery(P<0.05).The accuracy of the nomograms was validated in the training set,validation set,and expanded testing set using ROC curves,calibration curves,and DCA curves.The results of Kaplan-Meier survival curves(P<0.001)indicated that both nomograms accurately predicted the occurrence and prognosis of DM in patients with duodenal cancer.CONCLUSION The two nomograms are expected as effective tools for predicting DM risk in duodenal cancer patients and offering personalized prognosis predictions for those with DM,potentially enhancing clinical decision-making.
基金Our study has been approved by Medical Research Ethics Approval Committee(2023010122HN11C).
文摘BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.
文摘BACKGROUND Acute myocardial infarction(AMI)is a severe cardiovascular disease caused by the blockage of coronary arteries that leads to ischemic necrosis of the myocardium.Timely medical contact is critical for successful AMI treatment,and delays increase the risk of death for patients.Pre-hospital delay time(PDT)is a significant challenge for reducing treatment times,as identifying high-risk patients with AMI remains difficult.This study aims to construct a risk prediction model to identify high-risk patients and develop targeted strategies for effective and prompt care,ultimately reducing PDT and improving treatment outcomes.AIM To construct a nomogram model for forecasting pre-hospital delay(PHD)likelihood in patients with AMI and to assess the precision of the nomogram model in predicting PHD risk.METHODS A retrospective cohort design was employed to investigate predictive factors for PHD in patients with AMI diagnosed between January 2022 and September 2022.The study included 252 patients,with 180 randomly assigned to the development group and the remaining 72 to the validation group in a 7:3 ratio.Independent risk factors influencing PHD were identified in the development group,leading to the establishment of a nomogram model for predicting PHD in patients with AMI.The model's predictive performance was evaluated using the receiver operating characteristic curve in both the development and validation groups.RESULTS Independent risk factors for PHD in patients with AMI included living alone,hyperlipidemia,age,diabetes mellitus,and digestive system diseases(P<0.05).A characteristic curve analysis indicated area under the receiver operating characteristic curve values of 0.787(95%confidence interval:0.716–0.858)and 0.770(95%confidence interval:0.660-0.879)in the development and validation groups,respectively,demonstrating the model's good discriminatory ability.The Hosmer–Lemeshow goodness-of-fit test revealed no statistically significant disparity between the anticipated and observed incidence of PHD in both development and validation cohorts(P>0.05),indicating satisfactory model calibration.CONCLUSION The nomogram model,developed with independent risk factors,accurately forecasts PHD likelihood in AMI individuals,enabling efficient identification of PHD risk in these patients.
文摘目的:基于血常规和颈动脉斑块构建一种个性化nomogram风险预测模型预测颈动脉粥样硬化(carotid atherosclerosis, CAS)患者发生缺血性脑卒中(cerebral ischemic stroke, CIS)的风险。方法:选取2021年3月1日至2022年3月1日在上海市第八人民医院神经内科住院的CAS患者214例,收集患者的基本特征、血常规指标及影像学检查数据。根据是否发生缺血性脑卒中分别分为两组,随机抽取全部数据按7∶3的比例拆分为建模组和验证组。采用单因素logistic回归和lasso回归筛选CAS患者发生缺血性脑卒中的独立风险预测因子,将其导入R软件构建nomogram预测模型。ROC曲线下面积(AUC)、校准曲线和DCA决策曲线对模型进行内部验证。结果:单因素logistic回归和lasso回归分析结果显示,红细胞分布宽度、大型血小板比率、血小板计数是CAS患者发生缺血性脑卒中的独立风险预测因子(P<0.05),由于年龄对于CIS具有重要临床意义,最终也将其纳入模型。基于上述预测因子导入R软件构建nomogram预测模型并进行模型内部验证。建模组受试者工作特征曲线下面积(area under the curve, AUC)为0.644,验证组AUC为0.677,表示该nomogram模型预测能力较好。Hosmer-Lemeshow拟合优度检验(P=0.058),表明该模型具有较好的区分度。DCA曲线显示风险阈值为8%~45%时使用该模型具有临床实用价值。结论:本研究构建并验证了一个预测CAS患者发生缺血性脑卒中的nomogram风险预测模型,该模型预测能力和区分能力较好,对临床评估CAS患者发生缺血性脑卒中具有较高的临床实用价值。
基金Supported by Natural Science Foundation of Hainan Province,No.823RC609.
文摘BACKGROUND The efficacy of neoadjuvant chemotherapy(NAC)in advanced gastric cancer(GC)is still a controversial issue.AIM To find factors associated with chemosensitivity to NAC treatment and to provide the optimal therapeutic strategies for GC patients receiving NAC.METHODS The clinical information was collected from 230 GC patients who received NAC treatment at the Central South University Xiangya School of Medicine Affiliated Haikou Hospital from January 2016 to December 2020.Least absolute shrinkage and selection operator logistic regression analysis was used to find the possible predictors.A nomogram model was employed to predict the response to NAC.RESULTS In total 230 patients were finally included in this study,including 154 males(67.0%)and 76 females(33.0%).The mean age was(59.37±10.60)years,ranging from 24 years to 80 years.According to the tumor regression grade standard,there were 95 cases in the obvious response group(grade 0 or grade 1)and 135 cases in the poor response group(grade 2 or grade 3).The obvious response rate was 41.3%.Least absolute shrinkage and selection operator analysis showed that four risk factors significantly related to the efficacy of NAC were tumor location(P<0.001),histological differentiation(P=0.001),clinical T stage(P=0.008),and carbohydrate antigen 724(P=0.008).The C-index for the prediction nomogram was 0.806.The calibration curve revealed that the predicted value exhibited good agreement with the actual value.Decision curve analysis showed that the nomogram had a good value in clinical application.CONCLUSION A nomogram combining tumor location,histological differentiation,clinical T stage,and carbohydrate antigen 724 showed satisfactory predictive power to the response of NAC and can be used by gastrointestinal surgeons to determine the optimal treatment strategies for advanced GC patients.
基金Supported by Peng-Cheng Talent-Medical Young Reserve Talent Training Program,No.XWRCHT20220002Xuzhou City Health and Health Commission Technology Project Contract,No.XWKYHT20230081and Key Research and Development Plan Project of Xuzhou City,No.KC22179.
文摘BACKGROUND Gastric cancer(GC)is prevalent and aggressive,especially when patients have distant lung metastases,which often places patients into advanced stages.By identifying prognostic variables for lung metastasis in GC patients,it may be po-ssible to construct a good prediction model for both overall survival(OS)and the cumulative incidence prediction(CIP)plot of the tumour.AIM To investigate the predictors of GC with lung metastasis(GCLM)to produce nomograms for OS and generate CIP by using cancer-specific survival(CSS)data.METHODS Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance,epidemiology,and end results program database.The major observational endpoint was OS;hence,patients were se-parated into training and validation groups.Correlation analysis determined va-rious connections.Univariate and multivariate Cox analyses validated the independent predictive factors.Nomogram distinction and calibration were performed with the time-dependent area under the curve(AUC)and calibration curves.To evaluate the accuracy and clinical usefulness of the nomograms,decision curve analysis(DCA)was performed.The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer(AJCC)staging system by utilizing Net Reclassification Improvement(NRI)and Integrated Discrimination Improvement(IDI).Finally,the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.RESULTS For the purpose of creating the OS nomogram,a CIP plot based on CSS was generated.Cox multivariate regression analysis identified eleven significant prognostic factors(P<0.05)related to liver metastasis,bone metastasis,primary site,surgery,regional surgery,treatment sequence,chemotherapy,radiotherapy,positive lymph node count,N staging,and time from diagnosis to treatment.It was clear from the DCA(net benefit>0),time-de-pendent ROC curve(training/validation set AUC>0.7),and calibration curve(reliability slope closer to 45 degrees)results that the OS nomogram demonstrated a high level of predictive efficiency.The OS prediction model(New Model AUC=0.83)also performed much better than the old Cox-AJCC model(AUC difference between the new model and the old model greater than 0)in terms of risk stratification(P<0.0001)and verification using the IDI and NRI.CONCLUSION The OS nomogram for GCLM successfully predicts 1-and 3-year OS.Moreover,this approach can help to ap-propriately classify patients into high-risk and low-risk groups,thereby guiding treatment.
文摘Transoral endoscopic resections in treating upper gastrointestinal submucosal lesions have the advantages of maintaining the integrity of the gastrointestinal lumen,avoiding perforation and reducing gastrointestinal fistulae.They are becoming more widely used in clinical practice,but,they may also present a variety of complications.Gas-related complications are one of the most common,which can be left untreated if the symptoms are mild,but in severe cases,they can lead to rapid changes in the respiratory and circulatory systems in a short period,which can be life-threatening.Therefore,it is important to predict the occurrence of gas-related complications early and take preventive measures actively.Based on the authors'results in the prepublication of the article“Nomogram to predict gas-related complications during transoral endoscopic resection of upper gastrointestinal submucosal lesions,”and in conjunction with our evaluation and additions to the relevant content,radiographs may help screen patients at high risk for gas-related complications.Controlling blood glucose levels,shortening the duration of surgery,and choosing the most appropriate surgical resection may positively impact the prognosis of patients at high risk for gas-related complications during transoral endoscopic resection of upper gastrointestinal submucosal lesions.
