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 demonstrated superior performance, achieving an accuracy of 0.806, AUROC of 0.771, F1 score of 0.720, and a low Brier score of 0.182. Four T cells were identified for predicting the 90-day prognosis: CD3⁺, CD3⁺CD4⁺, CD4⁺CD28⁺, and CD4⁺CD25⁺CD127dim. For the 90-day prognosis, the LDA model again performed best, with an accuracy of 0.972, F1 score of 0.952, AUROC of 0.935, and a low Brier score of 0.059.

Conclusions: The LDA model is optimal for both diagnosing sepsis and predicting the 90-day prognosis in elderly patients with severe LRTIs. Key T-cell markers identified for sepsis include CD3⁺ and CD4⁺CD25⁺CD127dim, while the 90-day prognosis model includes CD3⁺, CD3⁺CD4⁺, CD4⁺CD28⁺, and CD4⁺CD25⁺CD127dim T cells. These markers should be prioritized for clinical testing.

Trial registration: Not applicable.