Establishment and Validation of a Machine-Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra-Abdominal Candidiasis in Septic Patients.

ABSTRACT: This study aimed to develop and validate a nomogram based on lymphocyte subtyping and clinical factors for the early and rapid prediction of Intra-abdominal candidiasis (IAC) in septic patients. A prospective cohort study of 633 consecutive patients diagnosed with sepsis and intra-abdominal infection (IAI) was performed. We assessed the clinical characteristics and lymphocyte subsets at the onset of IAI. A machine-learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. A nomogram model was constructed, and the discrimination, calibration, and clinical effectiveness of the model were verified. High-dose corticosteroids receipt, the CD4⁺T/CD8⁺ T ratio, total parenteral nut

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11747989 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature