Machine Learning Reveals the Value of Unconventional T Lymphocytes in Sepsis and Prognosis of Elderly Patients With Severe Lower Respiratory Tract Infections.

ABSTRACT:

Objective

This study enrolled 119 elderly patients with severe lower respiratory tract infections (LRTIs) and used machine learning (ML) to evaluate the predictive value of unconventional T lymphocytes (uT cells) in sepsis and 90-day prognosis.

Methods

We used random forest (RF) and LASSO analyses to screen model uT cells (identified by RF-LASSO overlapping). The ML models, including LR, LDA, RandomForest, XGBoost, KNN, QDA, NaiveBayes, and ANN, were developed. These models were evaluated and compared based on accuracy, precision, recall, F1 score, sensitivity, specificity, area under the ROC curve (AUROC), and Brier score.

Results

Two T cells were identified as factors of sepsis diagnosis: CD3⁺ and CD4⁺CD25⁺CD127dim. The LDA model dem

SUBMITTER: Qi T 

PROVIDER: S-EPMC12287663 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature