Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke

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.

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

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.