<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, survival curves and Cox regression models were used to form an integrated disease risk factor model. Stepwise Cox regression models using the AIC criterion and the BIC criterion were included in the comparative models, penalties were imposed on the regression parameters for variable selection, and important covariates were selected to analyze censored data so as to analyze the dependence of dichotomous outcome events and survival time on the covariates. Applying the model of this paper to investigate postoperative infection and seizure risk in glioma, the results showed that 104 strains of pathogenic bacteria were detected. Gram-positive bacteria were the most prevalent infection-causing organisms with 67 strains, with Streptococcus grass-green and Staphylococcus epidermidis being the most common. There were 34 strains (32.69%) of gram-negative bacteria, and Acinetobacter baumannii was the most common one. 3 strains (2.88%) of fungi were present. Cox regression analysis was used to screen for five independent risk factors for surgical site infections in patients undergoing glioma surgery: neutrophil ratio, lymphocyte ratio, surgical grade (III), duration of antibiotic use, and postoperative day three temperature. The presence or absence of preoperative epilepsy has an impact on the prognosis of glioma patients, with fluctuating seizures being the most common postoperative condition in patients with grade II-III gliomas, which often recur after 12 months of seizure-free epilepsy. To sum up, the findings of this report are highly instructive in determining the prognosis for glioma treatment.</p>
</abstract>

In this paper, survival curves and Cox regression models were used to form an integrated disease risk factor model. Stepwise Cox regression models using the AIC criterion and the BIC criterion were included in the comparative models, penalties were imposed on the regression parameters for variable selection, and important covariates were selected to analyze censored data so as to analyze the dependence of dichotomous outcome events and survival time on the covariates. Applying the model of this paper to investigate postoperative infection and seizure risk in glioma, the results showed that 104 strains of pathogenic bacteria were detected. Gram-positive bacteria were the most prevalent infection-causing organisms with 67 strains, with Streptococcus grass-green and Staphylococcus epidermidis being the most common. There were 34 strains (32.69%) of gram-negative bacteria, and Acinetobacter baumannii was the most common one. 3 strains (2.88%) of fungi were present. Cox regression analysis was used to screen for five independent risk factors for surgical site infections in patients undergoing glioma surgery: neutrophil ratio, lymphocyte ratio, surgical grade (III), duration of antibiotic use, and postoperative day three temperature. The presence or absence of preoperative epilepsy has an impact on the prognosis of glioma patients, with fluctuating seizures being the most common postoperative condition in patients with grade II-III gliomas, which often recur after 12 months of seizure-free epilepsy. To sum up, the findings of this report are highly instructive in determining the prognosis for glioma treatment.

Analysis of clinical outcome risk factors in glioma

Shandong Traditional Chinese Medicine University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Analysis of clinical outcome risk factors in glioma"}

In this paper, survival curves and Cox regression models were used to form an integrated disease risk factor model. Stepwise Cox regression models using the...

Analysis of clinical outcome risk factors in glioma

Publicado en línea: 07 jun 2024

Páginas: -

Recibido: 15 feb 2024

Aceptado: 29 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1416

Palabras clave
Survival curves, Cox regression model, Postoperative infection, Seizures, Risk factor analysis

© 2024 Yuru Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of clinical outcome risk factors in glioma

Publicado en línea: 07 jun 2024

Páginas: -

Recibido: 15 feb 2024

Aceptado: 29 abr 2024

DOI: https://doi.org/10.2478/amns-2024-1416

Palabras claveSurvival curves, Cox regression model, Postoperative infection, Seizures, Risk factor analysis

© 2024 Yuru Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Survival curves, Cox regression model, Postoperative infection, Seizures, Risk factor analysis