Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection.

ABSTRACT:

Background

Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection.

Methods

Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then

SUBMITTER: Ren Y 

PROVIDER: S-EPMC8739695 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature