A novel risk classifier to predict the in-hospital death risk of nosocomial infections in elderly cancer patients.

ABSTRACT:

Background

Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population.

Methods

Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. T

SUBMITTER: Jiang A 

PROVIDER: S-EPMC10206213 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature