Nomogram Prediction Model for Risk Factors of Secondary Infection in Sepsis Patients.
Jiaqi Lu, Shuai Liu, Jiaheng Zhang, Guangzhi Shi
Abstract
Open AccessObjectivesThis study aimed to investigate the clinical characteristics and risk factors associated with secondary infection in patients diagnosed with sepsis.MethodsClinical data of patients diagnosed with sepsis on the first day of admission to the Intensive Care Unit (ICU) were extracted from the MIMIC-IV version 2.0 database. Univariate and multivariate logistic regression analyses were employed to analyze the influencing factors. A nomogram for predicting the risk of secondary infection was constructed using R software.ResultsA total of 2247 patients met the eligibility criteria for this study. Lower oxygen saturation, reduced lymphocyte proportion, decreased platelet count, higher neutrophil proportion, elevated lactic acid levels within 24 h after ICU admission, and the use of invasive mechanical ventilation were identified as independent risk factors for secondary infection in sepsis patients during their ICU stay. These predictors were integrated into a nomogram to estimate the risk of secondary infection in sepsis patients, and R software was employed to construct this Nomogram.ConclusionsThe Nomogram, incorporating oxygen saturation, lymphocyte ratio, platelet count, neutrophil ratio, lactic acid levels, and mechanical ventilation, can provide individualized risk predictions for secondary infection in patients with sepsis.