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
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
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
Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic mode...Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic model using a retrospective study design.Methods:Clinical and pathological data of 1,788 PRCC patients were extracted from the SEER database based on defined inclusion and exclusion criteria.The cohort was randomly divided into a training set(n=1,252)and a validation set(n=536)in a 7:3 ratio.Univariate and multivariate Cox regression analyses were conducted to identify clinical factors influencing prognosis.Based on these factors,a nomogram was developed to predict 1-year,3-year,and 5-year Cancer-Specific Survival(CSS)rates.The model's discriminatory power and predictive performance were evaluated using the Concordance index(C-index),calibration curves,Area Under the Curve(AUC),and Receiver Operating Characteristic(ROC)analysis.Results:Univariate and multivariate Cox regression analyses identified age,gender,surgical method,pathological grade,and TNM stage as independent prognostic factors.These variables were incorporated into a Cox proportional hazards regression model to calculate risk scores and construct the nomogram.In the training set,the AUCs for 1-year,3-year,and 5-year CSS predictions were 0.7978,0.7813,and 0.7542,respectively.In the validation set,the AUCs were 0.6793,0.7114,and 0.7174,respectively.Calibration curves demonstrated good agreement between predicted and observed survival outcomes,indicating adequate predictive accuracy.Conclusion:The prognostic nomogram model for patients with papillary renal cell carcinoma developed based on SEER database data provides reliable prognostic predictions and may support clinical assessment and decision-making.展开更多
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.
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 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The Key Joint Project of the Hunan Provincial Natural Science Foundation and Municipal Science Foundation(2024JJ7428)。
文摘Objective:To utilize clinical data of patients diagnosed with Papillary Renal Cell Carcinoma(PRCC)from the Surveillance,Epidemiology,and End Results(SEER)database(2010–2015)to construct and validate a prognostic model using a retrospective study design.Methods:Clinical and pathological data of 1,788 PRCC patients were extracted from the SEER database based on defined inclusion and exclusion criteria.The cohort was randomly divided into a training set(n=1,252)and a validation set(n=536)in a 7:3 ratio.Univariate and multivariate Cox regression analyses were conducted to identify clinical factors influencing prognosis.Based on these factors,a nomogram was developed to predict 1-year,3-year,and 5-year Cancer-Specific Survival(CSS)rates.The model's discriminatory power and predictive performance were evaluated using the Concordance index(C-index),calibration curves,Area Under the Curve(AUC),and Receiver Operating Characteristic(ROC)analysis.Results:Univariate and multivariate Cox regression analyses identified age,gender,surgical method,pathological grade,and TNM stage as independent prognostic factors.These variables were incorporated into a Cox proportional hazards regression model to calculate risk scores and construct the nomogram.In the training set,the AUCs for 1-year,3-year,and 5-year CSS predictions were 0.7978,0.7813,and 0.7542,respectively.In the validation set,the AUCs were 0.6793,0.7114,and 0.7174,respectively.Calibration curves demonstrated good agreement between predicted and observed survival outcomes,indicating adequate predictive accuracy.Conclusion:The prognostic nomogram model for patients with papillary renal cell carcinoma developed based on SEER database data provides reliable prognostic predictions and may support clinical assessment and decision-making.
