Predicting six-month mortality in adult hemophagocytic lymphohistiocytosis with machine learning: a prognostic approach utilizing laboratory data.

ABSTRACT:

Background

Hemophagocytic lymphohistiocytosis (HLH) is associated with high mortality rates. This study was conducted to develop and validate a predictive model for adult HLH patients at high risk of six months mortality using machine learning (ML) algorithms.

Methods

The study utilized univariate analysis and LASSO regression, incorporating eleven ML algorithms, to perform a comprehensive analysis of both admission and discharge variables. Model performance was assessed using metrics such as AUC, F1 score, Kaplan Meier (KM) curves, calibration curves and decision curve analysis (DCA).

Results

A total of 136 patients meeting the HLH-2004 criteria were included in this study. Among them, 59 patients received chemotherapy or other cytotoxic drugs, while the remaining 77

SUBMITTER: Zhou J 

PROVIDER: S-EPMC12498379 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature