The prevalence of type 2 diabetes mellitus(T2DM)is rising,with hypertension as a common comorbidity that significantly increases cardiovascular and microva-scular risks.Accurate prediction of hypertension in T2DM is e...The prevalence of type 2 diabetes mellitus(T2DM)is rising,with hypertension as a common comorbidity that significantly increases cardiovascular and microva-scular risks.Accurate prediction of hypertension in T2DM is essential for early intervention and personalized management.In this editorial,we comment on a recent retrospective study by Zhao et al,which developed a nomogram model using a large cohort of 26850 patients to predict hypertension risk in patients with T2DM.The model incorporated key independent risk factors,including age,body mass index,duration of diabetes,low-density lipoprotein cholesterol and urine protein levels,demonstrating promising discriminative power and predictive accuracy in internal validation.However,its external applicability requires fur-ther confirmation.This editorial discusses the clinical value and limitations of the predictive model,highlighting the unfavorable impact of hypertension on T2DM patients.Future research should evaluate the potential contribution of other risk factors to enhance risk prediction and improve the management of T2DM co-morbidities.展开更多
BACKGROUND Locally advanced rectal cancer(LARC)carries a substantial risk of recurrence,prompting the use of neoadjuvant chemoradiotherapy(nCRT)to improve tumor resectability and long-term outcomes.However,individual ...BACKGROUND Locally advanced rectal cancer(LARC)carries a substantial risk of recurrence,prompting the use of neoadjuvant chemoradiotherapy(nCRT)to improve tumor resectability and long-term outcomes.However,individual treatment responses vary considerably,highlighting the need for robust predictive tools to guide clinical decision-making.AIM To develop a nomogram model integrating clinical characteristics and biomarkers to predict the likelihood of poor response to nCRT in LARC.METHODS A retrospective analysis was performed on 178 patients with stage II-III LARC treated from January 2021 to December 2023.All patients underwent standardized nCRT followed by total mesorectal excision.Clinical data,inflammatory markers[C-reactive protein(CRP),interleukin-6(IL-6),tumor necrosis factoralpha],and tumor markers[carcinoembryonic antigen(CEA),carbohydrate antigen 19-9]were collected.Logistic regression was used to identify independent predictors of poor nCRT response.A nomogram was constructed using significant predictors and validated via concordance index(C-index),receiver operating characteristic curve,calibration plot,and decision curve analysis(DCA).RESULTS A total of 178 patients were enrolled,with 36(20.2%)achieving a good response and 142(79.8%)exhibiting a poor response to nCRT.Baseline factors,including age and comorbidities,showed no significant differences.However,poor responders more frequently had lymph node metastasis,advanced tumor node metastasis/T stage,larger tumor diameter,and elevated CRP,IL-6,and CEA levels.Logistic regression confirmed CRP,IL-6,and CEA as independent predictors of poor response.The nomogram demonstrated high accuracy(area under the curve=0.928),good calibration(Hosmer-Lemeshow P=0.928),and a sensitivity of 88.1%with 82.6%specificity.Internal validation via bootstrap resampling(n=1000)yielded an adjusted C-index of 0.716,and DCA confirmed substantial clinical utility.CONCLUSION A nomogram incorporating serum CRP,IL-6,and CEA accurately predicts poor nCRT response in patients with LARC.This model provides a valuable framework for individualized treatment planning,potentially improving clinical outcomes.展开更多
Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modula...Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modularity was constructed.This model resolves the technical limitation of traditional clinical statistical models that overemphasize analysis while neglecting deployment readiness,thereby providing a reusable technical solution for the implementation of medical risk prediction systems.Methods:A standardized dataset was built using data from 300 ACI patients who underwent rt-PA thrombolysis.Automated data cleaning,outlier correction,and feature engineering optimization(recursive feature elimination+variance inflation factor test)were applied to select core features.An L2-regularized logistic regression algorithm was employed to construct the prediction model,which was visualized via a nomogram.Multi-dimensional performance validation was conducted through cross-validation and probability calibration techniques.Results:The incidence of HT was20.0%.Multivariate analysis identified NIHSS score(OR=1.626),blood glucose(OR=1.662),platelet count(OR=0.975),age(OR=1.091),and history of diabetes(OR=0.343)as core predictors(P<0.05).Engineering validation demonstrated excellent discrimination(AUC=0.800,95%CI:0.752-0.856).After optimization with Platt scaling,the calibration curve was highly consistent with the actual probability,with a Brier score of0.092.The prediction time per sample was<50 ms,and the convergence efficiency of model training was improved by 30%.Conclusion:This model integrates core technologies including data preprocessing,feature optimization,and algorithm improvement,exhibiting low-latency,high stability,and deployability.It provides a standardized technical framework for transforming medical prediction models from statistical conclusions into engineered products.展开更多
BACKGROUND The characteristics of cerebral hemodynamic indexes of patients with different types of auditory verbal hallucinations(AVHs)was not clear.AIM To explore the characteristics of cerebral hemodynamic indexes o...BACKGROUND The characteristics of cerebral hemodynamic indexes of patients with different types of auditory verbal hallucinations(AVHs)was not clear.AIM To explore the characteristics of cerebral hemodynamic indexes of patients with different types of AVHs and construct the risk nomogram prediction model of patients with different types of AVHs.METHODS Patients with different types of verbal hallucinations who visited Wenzhou Seventh People’s Hospital were retrospectively selected from March 2021 to March 2023,and these patients were classified into 117 cases of schizophrenia(SCZ)with AVHs,108 cases of post-traumatic stress disorder(PTSD)with AVHs,and 105 cases of recurrent depressive disorder with AVHs according to type.Transcranial doppler was performed to measure the hemodynamic parameters of the anterior cerebral artery(ACA),middle cerebral artery(MCA),posterior cerebral artery(PCA),basilar artery(BA)and vertebral artery(VA).Logistic regression modelling was used to explore the factors affecting patients with different types of AVHs and odds ratio,95%confidence interval(CI).A clinical prediction model was constructed,and the efficacy of the clinical prediction model was evaluated by using receiver operating characteristic,Hosmer-Lemeshow Goodness-of-Fit test,calibration curves and decision curve analysis.RESULTS The differences between the three groups of patients in mean velocity(Vm)-MCA,end-diastolic velocity(Vd)-MCA,Vm-ACA,pulsatility index(PI)-ACA,Vm-PCA,peak systolic velocity(Vs)-PCA,Vd-PCA,Vm-BA,Vs-BA,Vd-BA,PI-BA,resistance index(RI)-BA,Vm-VA,Vs-VA,Vd-VA,PI-VA,and RI-VA indexes were statistically significant.Rising Vm-ACA is an independent risk factor for SCZ with AVHs,and falling Vm-VA,Vd-MCA,and Vd-VA are independent risk factors for SCZ with AVHs.Rising Vm-ACA,Vm-PCA,Vs-PCA,Vd-PCA,Vm-BA,and Vs-BA are independent risk factors for PTSD with AVHs,and Vm-MCA,Vs-MCA,Vd-MCA,PI-PCA,and RIBA are independent protective factors for PTSD with AVHs.Elevated Vm-MCA,Vd-MCA,RI-BA,Vm-VA,and Vd-VA were independent risk factors,and elevated Vm-ACA,Vs-ACA,Vm-PCA,Vs-PCA,and Vd-PCA were independent protective factors.The areas under the curve of the three models were 0.82(95%CI:0.76-0.87),0.88(95%CI:0.83-0.92),and 0.81(95%CI:0.77-0.86),respectively;the Hosmer-Lemeshow Goodness-of-Fit test of the calibration curves of the three models suggests that P>0.05.CONCLUSION Monitoring the cerebral hemodynamic indexes of patients with AVHs is of practical significance in determining the type of mental disorder,which helps clinicians identify the type of AVHs and adopt more efficient treatment strategies to help patients recover.展开更多
BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the p...BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the popularization of percutaneous coronary intervention.However,the risk is significantly increased for those who fail to undergo revascularization in time,and the mortality rate remains high.The current core contradiction in clinical practice lies in the selection of surgical timing,and the disparity in medical resources significantly affects prognosis.There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.AIM To investigate the clinical features,determine the prognostic factors,and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture(AMI-VSR)residing in high-altitude regions.METHODS This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024,with the establishment of survival(n=30)and mortality(n=18)groups based on patients’survival status.Risk factors were identified by univariate and multivariate logistic regression analyses.A nomogram model was developed using R software and validated via receiver operating characteristic(ROC)analysis and calibration curves.RESULTS Age,uric acid(UA),interleukin-6(IL-6),and low hemoglobin(Hb)were independent risk factors for 30-day mortality(odds ratios:1.147,1.006,1.034,and 0.941,respectively;P<0.05).The nomogram demonstrated excellent discrimination(area under the ROC curve=0.939)and calibration(Hosmer-Lemeshowχ²=2.268,P=0.971).In addition,patients’poor outcomes could be synergistically predicted by IL-6 and UA,advanced age,and reduced Hb.CONCLUSION This study highlights age,UA,IL-6,and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes.The validated nomogram provides a practical tool for early risk stratification and tailored interventions,addressing gaps in managing this high-risk population in resource-limited settings.展开更多
BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still...BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.展开更多
Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Met...Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.展开更多
BACKGROUND Colon cancer is one of the most common malignant tumors of the digestive system.Liver metastasis after colon cancer surgery is the primary cause of death in patients with colon cancer.AIM To construct a nov...BACKGROUND Colon cancer is one of the most common malignant tumors of the digestive system.Liver metastasis after colon cancer surgery is the primary cause of death in patients with colon cancer.AIM To construct a novel nomogram model including various factors to predict liver metastasis after colon cancer surgery.METHODS We retrospectively analyzed 242 patients with colon cancer who were admitted and underwent radical resection for colon cancer in Zhejiang Provincial People’s Hospital from December 2019 to December 2022.Patients were divided into liver metastasis and non-liver metastasis groups.Sex,age,and other general and clinicopathological data(preoperative blood routine and biochemical test indexes)were compared.The risk factors for liver metastasis were analyzed using singlefactor and multifactorial logistic regression.A predictive model was then constructed and evaluated for efficacy.RESULTS Systemic inflammatory index(SII),C-reactive protein/albumin ratio(CAR),red blood cell distribution width(RDW),alanine aminotransferase,preoperative carcinoembryonic antigen level,and lymphatic metastasis were different between groups(P<0.05).SII,CAR,and RDW were risk factors for liver metastasis after colon cancer surgery(P<0.05).The area under the curve was 0.93 for the column-line diagram prediction model constructed based on these risk factors to distinguish whether liver metastasis occurred postoperatively.The actual curve of the column-line diagram predicting the risk of postoperative liver metastasis was close to the ideal curve,with good agreement.The prediction model curves in the decision curve analysis showed higher net benefits for a larger threshold range than those in extreme cases,indicating that the model is safer.CONCLUSION Liver metastases after colorectal cancer surgery could be well predicted by a nomogram based on the SII,CAR,and RDW.展开更多
Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features ext...Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.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 Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection...BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.展开更多
AIM：To represent mathematically the intersection between the ectatic corneal geometry and the plane of intracorneal ring implants（ICRS）in order to determine the corneal response to ICRS surgery in keratoconus（KC）...AIM：To represent mathematically the intersection between the ectatic corneal geometry and the plane of intracorneal ring implants（ICRS）in order to determine the corneal response to ICRS surgery in keratoconus（KC）.Thereafter,to present the concept and early results of a newly derived topography-guided nomogram for ICRS surgery for the treatment of keratectasia. METHODS：The corneal rings plane intersection was modelled to a conic section. Ring effect was the result of：the ring size,position（steep vs flat）,location（distance from the geometric centre of the cornea）,and the discrepancy between the ring＇s curvature and the tunnel＇s curvature.Femtosecond laser was used to create the tunnels and the incision sites were chosen according to the nomogram in order to place the thickest ring in the steepest portion of the cornea regardless of the astigmatism axis of refraction.RESULTS：The conic section had a more prolate shape in the steep area of the cornea than in the flat area,depending on the corneal sagittal curvature. Equal ring size had more flattening effect in the steep area than in the flat area. Thick segment should be implanted under the steep portion of the cornea regardless of the cylinder axis of refraction. Single segment in the steep area was sufficient in early and moderate cases of KC. The new nomogram provided more topographic regularity with significant reduction of astigmatism and better improvement in uncorrected visual acuity（UCVA）and best-corrected visual acuity（BCVA）than the conventional nomogram. CONCLUSION：The newly derived nomogram can produce better results than the conventional nomogram. Moreover,based on this concept,a new nomogram can be integrated into the femtosecond laser software to create topography-guided,customized,elliptical tunnels with modified focal asphericity that allows for customized focal flattening of the irregularly steepened ectatic cornea.展开更多
BACKGROUND Patients with deep venous thrombosis(DVT)residing at high altitudes can only rely on anticoagulation therapy,missing the optimal window for surgery or thrombolysis.Concurrently,under these conditions,patien...BACKGROUND Patients with deep venous thrombosis(DVT)residing at high altitudes can only rely on anticoagulation therapy,missing the optimal window for surgery or thrombolysis.Concurrently,under these conditions,patient outcomes can be easily complicated by high-altitude polycythemia(HAPC),which increases the difficulty of treatment and the risk of recurrent thrombosis.To prevent reaching this point,effective screening and targeted interventions are crucial.Thus,this study analyzes and provides a reference for the clinical prediction of thrombosis recurrence in patients with lower-extremity DVT combined with HAPC.AIM To apply the nomogram model in the evaluation of complications in patients with HAPC and DVT who underwent anticoagulation therapy.METHODS A total of 123 patients with HAPC complicated by lower-extremity DVT were followed up for 6-12 months and divided into recurrence and non-recurrence groups according to whether they experienced recurrence of lower-extremity DVT.Clinical data and laboratory indices were compared between the groups to determine the influencing factors of thrombosis recurrence in patients with lowerextremity DVT and HAPC.This study aimed to establish and verify the value of a nomogram model for predicting the risk of thrombus recurrence.RESULTS Logistic regression analysis showed that age,immobilization during follow-up,medication compliance,compliance with wearing elastic stockings,and peripheral blood D-dimer and fibrin degradation product levels were indepen-dent risk factors for thrombosis recurrence in patients with HAPC complicated by DVT.A Hosmer-Lemeshow goodness-of-fit test demonstrated that the nomogram model established based on the results of multivariate logistic regression analysis was effective in predicting the risk of thrombosis recurrence in patients with lowerextremity DVT complicated by HAPC(χ^(2)=0.873;P>0.05).The consistency index of the model was 0.802(95%CI:0.799-0.997),indicating its good accuracy and discrimination.CONCLUSION The column chart model for the personalized prediction of thrombotic recurrence risk has good application value in predicting thrombotic recurrence in patients with lower-limb DVT combined with HAPC after discharge.展开更多
Gastrointestinal stromal tumors(GISTs),the most prevalent mesenchymal tumors,often have poor outcomes due to high recurrence rates.However,the specific risk factors for GISTs,particularly those concerning the innate i...Gastrointestinal stromal tumors(GISTs),the most prevalent mesenchymal tumors,often have poor outcomes due to high recurrence rates.However,the specific risk factors for GISTs,particularly those concerning the innate immune-inflammatory response,remain poorly understood.This editorial highlights key prognostic factors that impact GIST progression and prognosis,while discussing the findings of a recent study that investigated the prognostic value of systemic inflammatory markers:systemic immune-inflammation index,neutrophil/lym-phocyte ratio,platelet/lymphocyte ratio,and monocyte/lymphocyte ratio,on recurrence-free survival in GIST patients.This editorial examines strategies to enhance the clinical applicability of the nomogram developed in the study,ensuring its effectiveness for robust implementation.Future directions outlined in the editorial stress the importance of integrating molecular insights,including KIT and PDGFRA mutations,tumor staging,and mitotic rates to refine predictive models.The editorial also underscores the value of multi-center studies to enhance the generalizability and clinical relevance of these approaches.By bridging inflammatory biomarkers with genetic and clinicopathologic factors,a more comprehensive understanding of GIST pathophysiology can be developed,paving the way for improved management strategies and patient outcomes.This perspective serves as a call to action for continued research into the interplay between genetic mutations,inflammatory marker modulation,and GIST progression,aiming to expand the scope of personalized oncology through a deeper understanding of GIST progression.展开更多
Zhao et al’s investigation on the assessment of inflammatory markers prognostic value for relapse-free survival in patients with gastrointestinal stromal tumor(GIST)using a nomogram-based approach is a scientific app...Zhao et al’s investigation on the assessment of inflammatory markers prognostic value for relapse-free survival in patients with gastrointestinal stromal tumor(GIST)using a nomogram-based approach is a scientific approach.This study explored the potential of an inflammatory marker-based nomograph model,highlighting the relapse-free survival-associated risk factors prognostic potential in patients with GIST.The author assessed 124 samples from patients with GIST to find an association between inflammatory markers and tumor size in a retrospective study using multivariate regression analysis.Further,a nomogram model was developed to identify the independent risk factors for the prognosis.GIST clinical treatment can use preoperative monocyte/lymphocyte ratio and platelet/lymphocyte ratio for relapse-free survival prognosis as independent factors.展开更多
Objectiveperitoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic con...Objectiveperitoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients.MethodsAll enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group.ResultsThere was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively.ConclusionsGRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.展开更多
BACKGROUND Post-burn anxiety and depression affect considerably the quality of life and recovery of patients;however,limited research has demonstrated risk factors associated with the development of these conditions.A...BACKGROUND Post-burn anxiety and depression affect considerably the quality of life and recovery of patients;however,limited research has demonstrated risk factors associated with the development of these conditions.AIM To predict the risk of developing post-burn anxiety and depression in patients with non-mild burns using a nomogram model.METHODS We enrolled 675 patients with burns who were admitted to The Second Affiliated Hospital,Hengyang Medical School,University of South China between January 2019 and January 2023 and met the inclusion criteria.These patients were randomly divided into development(n=450)and validation(n=225)sets in a 2:1 ratio.Univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with post-burn anxiety and depression dia-gnoses,and a nomogram model was constructed.RESULTS Female sex,age<33 years,unmarried status,burn area≥30%,and burns on the head,face,and neck were independent risk factors for developing post-burn anxiety and depression in patients with non-mild burns.The nomogram model demonstrated predictive accuracies of 0.937 and 0.984 for anxiety and 0.884 and 0.923 for depression in the development and validation sets,respectively,and good predictive per-formance.Calibration and decision curve analyses confirmed the clinical utility of the nomogram.CONCLUSION The nomogram model predicted the risk of post-burn anxiety and depression in patients with non-mild burns,facilitating the early identification of high-risk patients for intervention and treatment.展开更多
Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk commun...Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.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 Oral rehydration solution(ORS)is predominantly utilized in the management of hypovolemic postural orthostatic tachycardia syndrome(POTS).This study aimed to identify effective indicators and develop models ...Background Oral rehydration solution(ORS)is predominantly utilized in the management of hypovolemic postural orthostatic tachycardia syndrome(POTS).This study aimed to identify effective indicators and develop models to assess the impact of ORS on pediatric patients diagnosed with POTS.Methods We utilized a retrospective analysis of totally 158 pediatric patients with POTS receiving a 3-month course of ORS treatment.All patients were classified into training set(n=98)and validation set(n=60).Within the training set,univariate analysis and binary logistic regression were employed to select candidate predictors.To predict the efficacy of ORS in pediatric patients with POTS,a nomogram model and a scoring model were constructed and demonstrated.Additionally,the predictive ability and calibration performance were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow(H-L)goodness of fit test,and calibration plots.Decision curve analysis(DCA)was employed to assess the clinical applicability of the predictive models.Results Body mass index(BMI)z-score,serum chlorine,and urine specific gravity(USG)before treatment were identified as significant and independent predictors of efficacy of ORS in pediatric patients with POTS.Consequently,these indicators were included in the predictive models.A nomogram model was constructed in the training set(AUC=0.87,which yields a sensitivity of 84.5%and a specificity of 85.0%)and validated in the validation set(the sensitivity,specificity,and accuracy were 87.5%,85%,and 86.7%,respectively).A scoring model was advanced in the training set(AUC=0.88,which yields a sensitivity of 79.3%and a specificity of 82.5%)and validated in the validation set(the sensitivity,specificity,and accuracy were 77.5%,80.0%,and 78.3%,respectively).The H-L test results indicated a good model fit.The calibration plots and DCA for both models exhibited excellent calibration and satisfactory net benefit.Conclusions Based on pre-treatment BMI z-score,serum chlorine,and USG,a nomogram model and a scoring model were developed and validated.The models can effectively assess the efficacy of ORS in pediatric patients with POTS,offering an accurate and individualized therapeutic strategy.展开更多
基金Supported by National Natural Science Foundation of China,No.82170327 and No.82370332Tianjin Key Medical Discipline(Specialty)Construction Project,No.TJYXZDXK-029A.
文摘The prevalence of type 2 diabetes mellitus(T2DM)is rising,with hypertension as a common comorbidity that significantly increases cardiovascular and microva-scular risks.Accurate prediction of hypertension in T2DM is essential for early intervention and personalized management.In this editorial,we comment on a recent retrospective study by Zhao et al,which developed a nomogram model using a large cohort of 26850 patients to predict hypertension risk in patients with T2DM.The model incorporated key independent risk factors,including age,body mass index,duration of diabetes,low-density lipoprotein cholesterol and urine protein levels,demonstrating promising discriminative power and predictive accuracy in internal validation.However,its external applicability requires fur-ther confirmation.This editorial discusses the clinical value and limitations of the predictive model,highlighting the unfavorable impact of hypertension on T2DM patients.Future research should evaluate the potential contribution of other risk factors to enhance risk prediction and improve the management of T2DM co-morbidities.
文摘BACKGROUND Locally advanced rectal cancer(LARC)carries a substantial risk of recurrence,prompting the use of neoadjuvant chemoradiotherapy(nCRT)to improve tumor resectability and long-term outcomes.However,individual treatment responses vary considerably,highlighting the need for robust predictive tools to guide clinical decision-making.AIM To develop a nomogram model integrating clinical characteristics and biomarkers to predict the likelihood of poor response to nCRT in LARC.METHODS A retrospective analysis was performed on 178 patients with stage II-III LARC treated from January 2021 to December 2023.All patients underwent standardized nCRT followed by total mesorectal excision.Clinical data,inflammatory markers[C-reactive protein(CRP),interleukin-6(IL-6),tumor necrosis factoralpha],and tumor markers[carcinoembryonic antigen(CEA),carbohydrate antigen 19-9]were collected.Logistic regression was used to identify independent predictors of poor nCRT response.A nomogram was constructed using significant predictors and validated via concordance index(C-index),receiver operating characteristic curve,calibration plot,and decision curve analysis(DCA).RESULTS A total of 178 patients were enrolled,with 36(20.2%)achieving a good response and 142(79.8%)exhibiting a poor response to nCRT.Baseline factors,including age and comorbidities,showed no significant differences.However,poor responders more frequently had lymph node metastasis,advanced tumor node metastasis/T stage,larger tumor diameter,and elevated CRP,IL-6,and CEA levels.Logistic regression confirmed CRP,IL-6,and CEA as independent predictors of poor response.The nomogram demonstrated high accuracy(area under the curve=0.928),good calibration(Hosmer-Lemeshow P=0.928),and a sensitivity of 88.1%with 82.6%specificity.Internal validation via bootstrap resampling(n=1000)yielded an adjusted C-index of 0.716,and DCA confirmed substantial clinical utility.CONCLUSION A nomogram incorporating serum CRP,IL-6,and CEA accurately predicts poor nCRT response in patients with LARC.This model provides a valuable framework for individualized treatment planning,potentially improving clinical outcomes.
文摘Objective:To address the prediction of hemorrhagic transformation(HT)after rt-PA thrombolysis in acute cerebral infarction(ACI)patients,a predictive model integrating data engineering,algorithm optimization,and modularity was constructed.This model resolves the technical limitation of traditional clinical statistical models that overemphasize analysis while neglecting deployment readiness,thereby providing a reusable technical solution for the implementation of medical risk prediction systems.Methods:A standardized dataset was built using data from 300 ACI patients who underwent rt-PA thrombolysis.Automated data cleaning,outlier correction,and feature engineering optimization(recursive feature elimination+variance inflation factor test)were applied to select core features.An L2-regularized logistic regression algorithm was employed to construct the prediction model,which was visualized via a nomogram.Multi-dimensional performance validation was conducted through cross-validation and probability calibration techniques.Results:The incidence of HT was20.0%.Multivariate analysis identified NIHSS score(OR=1.626),blood glucose(OR=1.662),platelet count(OR=0.975),age(OR=1.091),and history of diabetes(OR=0.343)as core predictors(P<0.05).Engineering validation demonstrated excellent discrimination(AUC=0.800,95%CI:0.752-0.856).After optimization with Platt scaling,the calibration curve was highly consistent with the actual probability,with a Brier score of0.092.The prediction time per sample was<50 ms,and the convergence efficiency of model training was improved by 30%.Conclusion:This model integrates core technologies including data preprocessing,feature optimization,and algorithm improvement,exhibiting low-latency,high stability,and deployability.It provides a standardized technical framework for transforming medical prediction models from statistical conclusions into engineered products.
文摘BACKGROUND The characteristics of cerebral hemodynamic indexes of patients with different types of auditory verbal hallucinations(AVHs)was not clear.AIM To explore the characteristics of cerebral hemodynamic indexes of patients with different types of AVHs and construct the risk nomogram prediction model of patients with different types of AVHs.METHODS Patients with different types of verbal hallucinations who visited Wenzhou Seventh People’s Hospital were retrospectively selected from March 2021 to March 2023,and these patients were classified into 117 cases of schizophrenia(SCZ)with AVHs,108 cases of post-traumatic stress disorder(PTSD)with AVHs,and 105 cases of recurrent depressive disorder with AVHs according to type.Transcranial doppler was performed to measure the hemodynamic parameters of the anterior cerebral artery(ACA),middle cerebral artery(MCA),posterior cerebral artery(PCA),basilar artery(BA)and vertebral artery(VA).Logistic regression modelling was used to explore the factors affecting patients with different types of AVHs and odds ratio,95%confidence interval(CI).A clinical prediction model was constructed,and the efficacy of the clinical prediction model was evaluated by using receiver operating characteristic,Hosmer-Lemeshow Goodness-of-Fit test,calibration curves and decision curve analysis.RESULTS The differences between the three groups of patients in mean velocity(Vm)-MCA,end-diastolic velocity(Vd)-MCA,Vm-ACA,pulsatility index(PI)-ACA,Vm-PCA,peak systolic velocity(Vs)-PCA,Vd-PCA,Vm-BA,Vs-BA,Vd-BA,PI-BA,resistance index(RI)-BA,Vm-VA,Vs-VA,Vd-VA,PI-VA,and RI-VA indexes were statistically significant.Rising Vm-ACA is an independent risk factor for SCZ with AVHs,and falling Vm-VA,Vd-MCA,and Vd-VA are independent risk factors for SCZ with AVHs.Rising Vm-ACA,Vm-PCA,Vs-PCA,Vd-PCA,Vm-BA,and Vs-BA are independent risk factors for PTSD with AVHs,and Vm-MCA,Vs-MCA,Vd-MCA,PI-PCA,and RIBA are independent protective factors for PTSD with AVHs.Elevated Vm-MCA,Vd-MCA,RI-BA,Vm-VA,and Vd-VA were independent risk factors,and elevated Vm-ACA,Vs-ACA,Vm-PCA,Vs-PCA,and Vd-PCA were independent protective factors.The areas under the curve of the three models were 0.82(95%CI:0.76-0.87),0.88(95%CI:0.83-0.92),and 0.81(95%CI:0.77-0.86),respectively;the Hosmer-Lemeshow Goodness-of-Fit test of the calibration curves of the three models suggests that P>0.05.CONCLUSION Monitoring the cerebral hemodynamic indexes of patients with AVHs is of practical significance in determining the type of mental disorder,which helps clinicians identify the type of AVHs and adopt more efficient treatment strategies to help patients recover.
基金Supported by Science and Technology Department of Yunnan Province-Kunming Medical University,Kunming Medical Joint Special Project-Surface Project,No.202401AY070001-164Yunnan Provincial Department of Science and Technology Science and Technology Plan Project-Major Science and Technology Special Projects,No.202405AJ310003+1 种基金Yunnan Provincial Department of Science and Technology Science and Technology Plan Project-Key Research and Development Program,No.202103AC100004Yunnan Province Science and Technology Department Key Research and Development Plan,No.202103AC100002.
文摘BACKGROUND Acute myocardial infarction(AMI)combined with ventricular septal perforation(VSR)is still a highly fatal condition in the era of reperfusion therapy.The incidence rate has decreased to 0.2%-0.4%due to the popularization of percutaneous coronary intervention.However,the risk is significantly increased for those who fail to undergo revascularization in time,and the mortality rate remains high.The current core contradiction in clinical practice lies in the selection of surgical timing,and the disparity in medical resources significantly affects prognosis.There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.AIM To investigate the clinical features,determine the prognostic factors,and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture(AMI-VSR)residing in high-altitude regions.METHODS This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024,with the establishment of survival(n=30)and mortality(n=18)groups based on patients’survival status.Risk factors were identified by univariate and multivariate logistic regression analyses.A nomogram model was developed using R software and validated via receiver operating characteristic(ROC)analysis and calibration curves.RESULTS Age,uric acid(UA),interleukin-6(IL-6),and low hemoglobin(Hb)were independent risk factors for 30-day mortality(odds ratios:1.147,1.006,1.034,and 0.941,respectively;P<0.05).The nomogram demonstrated excellent discrimination(area under the ROC curve=0.939)and calibration(Hosmer-Lemeshowχ²=2.268,P=0.971).In addition,patients’poor outcomes could be synergistically predicted by IL-6 and UA,advanced age,and reduced Hb.CONCLUSION This study highlights age,UA,IL-6,and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes.The validated nomogram provides a practical tool for early risk stratification and tailored interventions,addressing gaps in managing this high-risk population in resource-limited settings.
基金Supported by the Project of NINGBO Leading Medical Health Discipline,No.2022-B11Ningbo Natural Science Foundation,No.202003N4206Public Welfare Foundation of Ningbo,No.2021S108.
文摘BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.
基金supported by Wuhan Scientific Research Project(No.EX20B05)National Nature Science Foundation of China(No.82000521).
文摘Objective:This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding(DUGIB),and identify high-risk patients who require emergent therapy.Methods:From January 2020 to April 2022,the clinical data of 256 DUGIB patients who received treatments in the intensive care unit(ICU)were retrospectively collected from Renmin Hospital of Wuhan University(n=179)and the Eastern Campus of Renmin Hospital of Wuhan University(n=77).The 179 patients were treated as the training cohort,and 77 patients as the validation cohort.Logistic regression analysis was used to calculate the independent risk factors,and R packages were used to construct the nomogram model.The prediction accuracy and identification ability were evaluated by the receiver operating characteristic(ROC)curve,C index and calibration curve.The nomogram model was also simultaneously externally validated.Decision curve analysis(DCA)was then used to demonstrate the clinical value of the model.Results:Logistic regression analysis showed that hematemesis,urea nitrogen level,emergency endoscopy,AIMS65,Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB.The ROC curve analysis indicated the area under curve(AUC)of the training cohort was 0.980(95%CI:0.962-0.997),while the AUC of the validation cohort was 0.790(95%CI:0.685-0.895).The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts(P=0.778,P=0.516).Conclusion:The developed nomogram is an effective tool for risk stratification,early identification and intervention for DUGIB patients.
基金reviewed and approved by the Institutional Review Board of Zhejiang Provincial People’s Hospital(Approval No.2023-338).
文摘BACKGROUND Colon cancer is one of the most common malignant tumors of the digestive system.Liver metastasis after colon cancer surgery is the primary cause of death in patients with colon cancer.AIM To construct a novel nomogram model including various factors to predict liver metastasis after colon cancer surgery.METHODS We retrospectively analyzed 242 patients with colon cancer who were admitted and underwent radical resection for colon cancer in Zhejiang Provincial People’s Hospital from December 2019 to December 2022.Patients were divided into liver metastasis and non-liver metastasis groups.Sex,age,and other general and clinicopathological data(preoperative blood routine and biochemical test indexes)were compared.The risk factors for liver metastasis were analyzed using singlefactor and multifactorial logistic regression.A predictive model was then constructed and evaluated for efficacy.RESULTS Systemic inflammatory index(SII),C-reactive protein/albumin ratio(CAR),red blood cell distribution width(RDW),alanine aminotransferase,preoperative carcinoembryonic antigen level,and lymphatic metastasis were different between groups(P<0.05).SII,CAR,and RDW were risk factors for liver metastasis after colon cancer surgery(P<0.05).The area under the curve was 0.93 for the column-line diagram prediction model constructed based on these risk factors to distinguish whether liver metastasis occurred postoperatively.The actual curve of the column-line diagram predicting the risk of postoperative liver metastasis was close to the ideal curve,with good agreement.The prediction model curves in the decision curve analysis showed higher net benefits for a larger threshold range than those in extreme cases,indicating that the model is safer.CONCLUSION Liver metastases after colorectal cancer surgery could be well predicted by a nomogram based on the SII,CAR,and RDW.
基金supported by the National Key Research and Development Program of China (No. 2017YFC1309100)the National Natural Scientific Foundation of China (No. 81771912, 81701782 and 81601469)
文摘Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use nomogram.Results： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.
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.
基金Shandong Province Grassroots Health Technology Innovation Program Project,No.JCK22007.
文摘BACKGROUND Post-stroke infection is the most common complication of stroke and poses a huge threat to patients.In addition to prolonging the hospitalization time and increasing the medical burden,post-stroke infection also significantly increases the risk of disease and death.Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke(AIS)is of great significance.It can guide clinical practice to perform corresponding prevention and control work early,minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.AIM To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.METHODS The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected.Baseline data and post-stroke infection status of all study subjects were assessed,and the risk factors for poststroke infection in patients with AIS were analyzed.RESULTS Totally,48 patients with AIS developed stroke,with an infection rate of 23.3%.Age,diabetes,disturbance of consciousness,high National Institutes of Health Stroke Scale(NIHSS)score at admission,invasive operation,and chronic obstructive pulmonary disease(COPD)were risk factors for post-stroke infection in patients with AIS(P<0.05).A nomogram prediction model was constructed with a C-index of 0.891,reflecting the good potential clinical efficacy of the nomogram prediction model.The calibration curve also showed good consistency between the actual observations and nomogram predictions.The area under the receiver operating characteristic curve was 0.891(95%confidence interval:0.839–0.942),showing predictive value for post-stroke infection.When the optimal cutoff value was selected,the sensitivity and specificity were 87.5%and 79.7%,respectively.CONCLUSION Age,diabetes,disturbance of consciousness,NIHSS score at admission,invasive surgery,and COPD are risk factors for post-stroke infection following AIS.The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.
文摘AIM：To represent mathematically the intersection between the ectatic corneal geometry and the plane of intracorneal ring implants（ICRS）in order to determine the corneal response to ICRS surgery in keratoconus（KC）.Thereafter,to present the concept and early results of a newly derived topography-guided nomogram for ICRS surgery for the treatment of keratectasia. METHODS：The corneal rings plane intersection was modelled to a conic section. Ring effect was the result of：the ring size,position（steep vs flat）,location（distance from the geometric centre of the cornea）,and the discrepancy between the ring＇s curvature and the tunnel＇s curvature.Femtosecond laser was used to create the tunnels and the incision sites were chosen according to the nomogram in order to place the thickest ring in the steepest portion of the cornea regardless of the astigmatism axis of refraction.RESULTS：The conic section had a more prolate shape in the steep area of the cornea than in the flat area,depending on the corneal sagittal curvature. Equal ring size had more flattening effect in the steep area than in the flat area. Thick segment should be implanted under the steep portion of the cornea regardless of the cylinder axis of refraction. Single segment in the steep area was sufficient in early and moderate cases of KC. The new nomogram provided more topographic regularity with significant reduction of astigmatism and better improvement in uncorrected visual acuity（UCVA）and best-corrected visual acuity（BCVA）than the conventional nomogram. CONCLUSION：The newly derived nomogram can produce better results than the conventional nomogram. Moreover,based on this concept,a new nomogram can be integrated into the femtosecond laser software to create topography-guided,customized,elliptical tunnels with modified focal asphericity that allows for customized focal flattening of the irregularly steepened ectatic cornea.
基金Supported by Guiding Project of Qinghai Provincial Health Commission,No.2021-wjzdx-89.
文摘BACKGROUND Patients with deep venous thrombosis(DVT)residing at high altitudes can only rely on anticoagulation therapy,missing the optimal window for surgery or thrombolysis.Concurrently,under these conditions,patient outcomes can be easily complicated by high-altitude polycythemia(HAPC),which increases the difficulty of treatment and the risk of recurrent thrombosis.To prevent reaching this point,effective screening and targeted interventions are crucial.Thus,this study analyzes and provides a reference for the clinical prediction of thrombosis recurrence in patients with lower-extremity DVT combined with HAPC.AIM To apply the nomogram model in the evaluation of complications in patients with HAPC and DVT who underwent anticoagulation therapy.METHODS A total of 123 patients with HAPC complicated by lower-extremity DVT were followed up for 6-12 months and divided into recurrence and non-recurrence groups according to whether they experienced recurrence of lower-extremity DVT.Clinical data and laboratory indices were compared between the groups to determine the influencing factors of thrombosis recurrence in patients with lowerextremity DVT and HAPC.This study aimed to establish and verify the value of a nomogram model for predicting the risk of thrombus recurrence.RESULTS Logistic regression analysis showed that age,immobilization during follow-up,medication compliance,compliance with wearing elastic stockings,and peripheral blood D-dimer and fibrin degradation product levels were indepen-dent risk factors for thrombosis recurrence in patients with HAPC complicated by DVT.A Hosmer-Lemeshow goodness-of-fit test demonstrated that the nomogram model established based on the results of multivariate logistic regression analysis was effective in predicting the risk of thrombosis recurrence in patients with lowerextremity DVT complicated by HAPC(χ^(2)=0.873;P>0.05).The consistency index of the model was 0.802(95%CI:0.799-0.997),indicating its good accuracy and discrimination.CONCLUSION The column chart model for the personalized prediction of thrombotic recurrence risk has good application value in predicting thrombotic recurrence in patients with lower-limb DVT combined with HAPC after discharge.
文摘Gastrointestinal stromal tumors(GISTs),the most prevalent mesenchymal tumors,often have poor outcomes due to high recurrence rates.However,the specific risk factors for GISTs,particularly those concerning the innate immune-inflammatory response,remain poorly understood.This editorial highlights key prognostic factors that impact GIST progression and prognosis,while discussing the findings of a recent study that investigated the prognostic value of systemic inflammatory markers:systemic immune-inflammation index,neutrophil/lym-phocyte ratio,platelet/lymphocyte ratio,and monocyte/lymphocyte ratio,on recurrence-free survival in GIST patients.This editorial examines strategies to enhance the clinical applicability of the nomogram developed in the study,ensuring its effectiveness for robust implementation.Future directions outlined in the editorial stress the importance of integrating molecular insights,including KIT and PDGFRA mutations,tumor staging,and mitotic rates to refine predictive models.The editorial also underscores the value of multi-center studies to enhance the generalizability and clinical relevance of these approaches.By bridging inflammatory biomarkers with genetic and clinicopathologic factors,a more comprehensive understanding of GIST pathophysiology can be developed,paving the way for improved management strategies and patient outcomes.This perspective serves as a call to action for continued research into the interplay between genetic mutations,inflammatory marker modulation,and GIST progression,aiming to expand the scope of personalized oncology through a deeper understanding of GIST progression.
文摘Zhao et al’s investigation on the assessment of inflammatory markers prognostic value for relapse-free survival in patients with gastrointestinal stromal tumor(GIST)using a nomogram-based approach is a scientific approach.This study explored the potential of an inflammatory marker-based nomograph model,highlighting the relapse-free survival-associated risk factors prognostic potential in patients with GIST.The author assessed 124 samples from patients with GIST to find an association between inflammatory markers and tumor size in a retrospective study using multivariate regression analysis.Further,a nomogram model was developed to identify the independent risk factors for the prognosis.GIST clinical treatment can use preoperative monocyte/lymphocyte ratio and platelet/lymphocyte ratio for relapse-free survival prognosis as independent factors.
基金supported by the Ningbo Public Welfare Fund(No.2021S185).
文摘Objectiveperitoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients.MethodsAll enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group.ResultsThere was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively.ConclusionsGRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.
基金the Natural Science Foundation of Hunan Provincial Department of Science and Technology,Departmental Joint Fund,No.2023JJ60360.
文摘BACKGROUND Post-burn anxiety and depression affect considerably the quality of life and recovery of patients;however,limited research has demonstrated risk factors associated with the development of these conditions.AIM To predict the risk of developing post-burn anxiety and depression in patients with non-mild burns using a nomogram model.METHODS We enrolled 675 patients with burns who were admitted to The Second Affiliated Hospital,Hengyang Medical School,University of South China between January 2019 and January 2023 and met the inclusion criteria.These patients were randomly divided into development(n=450)and validation(n=225)sets in a 2:1 ratio.Univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with post-burn anxiety and depression dia-gnoses,and a nomogram model was constructed.RESULTS Female sex,age<33 years,unmarried status,burn area≥30%,and burns on the head,face,and neck were independent risk factors for developing post-burn anxiety and depression in patients with non-mild burns.The nomogram model demonstrated predictive accuracies of 0.937 and 0.984 for anxiety and 0.884 and 0.923 for depression in the development and validation sets,respectively,and good predictive per-formance.Calibration and decision curve analyses confirmed the clinical utility of the nomogram.CONCLUSION The nomogram model predicted the risk of post-burn anxiety and depression in patients with non-mild burns,facilitating the early identification of high-risk patients for intervention and treatment.
基金Natural Science Foundation of Hainan Province(No.819MS128)。
文摘Objective:To assess the predictive value of systemic immune inflammation index(SII)for sepsis in low-and medium-risk community-acquired pneumonia.Methods:A total of 589 elderly patients with low-and medium-risk community-acquired pneumonia admitted to the Emergency Department of the Second Affiliated Hospital of Hainan Medical University from January 2020 to January 2023 were included as the research subjects,and the general information and laboratory test results of the patients were collected,and the optimal cut-off value of continuous variables for predicting sepsis in elderly patients with low-and medium-risk community-acquired pneumonia was determined by plotting the receiver work characteristic(ROC)curve,which was converted into dichotomous variables and univariate and multivariate logistic Regression analysis of the influencing factors of sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.Based on this,a nomogram model is constructed to predict the risk of sepsis.The differentiation,consistency and accuracy of the model were verified by calibration curve and subject operating characteristic(ROC)curve,and the clinical utility of the model was determined by decision curve analysis.Results:A total of 589 elderly patients with low-and intermediate-risk community-acquired pneumonia were included in this study,of which 96(16.30%)developed sepsis.There were significant differences in age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,PCT,SII and other indexes between sepsis and non-sepsis groups(P<0.05).Logistics regression analysis showed that age,diabetes mellitus and chronic obstructive pulmonary disease,Lac,and SII were independent risk factors for sepsis in elderly patients with low-and medium-risk community-acquired pneumonia.The nomogram prediction model was used to verify the results,and the AUC was 0.826(95%CI:0.780-0.872),and the calibration curve tended to the ideal curve with good accuracy.The decision curve shows that when the threshold of the model is between 0.10~0.78,the model has the advantage of clinical benefit.Conclusion:The nomogram prediction model constructed based on SII to predict sepsis in elderly patients with low-and medium-risk community-acquired pneumonia has good accuracy,which can predict the occurrence of sepsis early,help early identification of high-risk groups and timely intervention,and thus improve the prognosis of patients.
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provided by Clinical Medicine Plus X-Young Scholars Project(PKU2022LCXQ028)the Fundamental Research Funds for the Central Universities,China.
文摘Background Oral rehydration solution(ORS)is predominantly utilized in the management of hypovolemic postural orthostatic tachycardia syndrome(POTS).This study aimed to identify effective indicators and develop models to assess the impact of ORS on pediatric patients diagnosed with POTS.Methods We utilized a retrospective analysis of totally 158 pediatric patients with POTS receiving a 3-month course of ORS treatment.All patients were classified into training set(n=98)and validation set(n=60).Within the training set,univariate analysis and binary logistic regression were employed to select candidate predictors.To predict the efficacy of ORS in pediatric patients with POTS,a nomogram model and a scoring model were constructed and demonstrated.Additionally,the predictive ability and calibration performance were evaluated using receiver operating characteristic(ROC)curves,Hosmer-Lemeshow(H-L)goodness of fit test,and calibration plots.Decision curve analysis(DCA)was employed to assess the clinical applicability of the predictive models.Results Body mass index(BMI)z-score,serum chlorine,and urine specific gravity(USG)before treatment were identified as significant and independent predictors of efficacy of ORS in pediatric patients with POTS.Consequently,these indicators were included in the predictive models.A nomogram model was constructed in the training set(AUC=0.87,which yields a sensitivity of 84.5%and a specificity of 85.0%)and validated in the validation set(the sensitivity,specificity,and accuracy were 87.5%,85%,and 86.7%,respectively).A scoring model was advanced in the training set(AUC=0.88,which yields a sensitivity of 79.3%and a specificity of 82.5%)and validated in the validation set(the sensitivity,specificity,and accuracy were 77.5%,80.0%,and 78.3%,respectively).The H-L test results indicated a good model fit.The calibration plots and DCA for both models exhibited excellent calibration and satisfactory net benefit.Conclusions Based on pre-treatment BMI z-score,serum chlorine,and USG,a nomogram model and a scoring model were developed and validated.The models can effectively assess the efficacy of ORS in pediatric patients with POTS,offering an accurate and individualized therapeutic strategy.
