iScienceJan 26, 2026

Using understandable machine learning to predict how well low-dose methylprednisolone works in long COVID

Updated Jun 27, 2026

Abstract

Essence

An interpretable logistic-regression model may help predict which long COVID patients respond to low-dose methylprednisolone.

Evidence

This retrospective three-hospital machine-learning study used 330 treated long COVID patients split into training, test, and two external validation sets, with logistic regression AUCs of 0.8715, 0.7198, 0.8419, and 0.8676.

Caveat

The model predicts observed treatment response in retrospective treated cohorts and does not prove methylprednisolone effectiveness or prospective clinical utility.

Simplified

Long COVID is a chronic, multisystem disease with limited response to conventional treatments. While low-dose methylprednisolone has shown effectiveness in some patients, individual responses vary, and accurate predictive tools are lacking. This retrospective study included 330 long COVID patients who received low-dose methylprednisolone treatment across three hospitals. Patients were divided into training (= 202), test (= 33), and external validation sets (= 53,= 42). Using least absolute shrinkage and selection operator (LASSO) regression, 38 variables were analyzed to develop six machine learning models. The logistic regression (LR) model showed stable performance across all datasets (AUCs: 0.8715, 0.7198, 0.8419, and 0.8676), making it the final model selected. SHapley Additive exPlanations (SHAP) analysis identified seven key variables, which were used to construct a nomogram for predicting treatment efficacy. The LR model and nomogram demonstrated strong predictive performance and clinical interpretability, offering a valuable decision-support tool for individualized treatment of long COVID with low-dose methylprednisolone. n n n n

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Using understandable machine learning to predict how well low-dose methylprednisolone works in long COVID

Abstract

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