Using interpretable machine learning to analyze the trajectory changes of serum albumin to predict the mortality rate...

Although hypoalbuminemia at ICU admission is associated with increased in-hospital mortality in septic patients, the prognostic value of serum albumin trajectory changes based on repeated measurements remains unclear. This study aimed to identify longitudinal serum albumin trajectory changes, evaluate their association with clinical outcomes at different temporal windows, and construct interpretable machine learning (ML) models for risk prediction.

Using the MIMIC-IV v3.0 database, trajectory analysis (via the traje R package) classified serum albumin trajectory changes into three groups: Persistently Low, Persistently Moderate, and Persistently High. Kaplan-Meier survival curves and Cox regression models were employed to analyze associations between trajectory groups and ICU, 28-day, and 90-day mortality. The Boruta algorithm screened feature variables, and seven ML models (XGBoost, SVM, LightGBM, RandomForest, e.g.) were developed. Model performance was evaluated using ROC curves, and SHapley Additive exPlanations (SHAP) values provided interpretability. A nomogram was constructed for clinical utility.

Among 1,714 included patients, trajectory groups comprised Persistently Low (n = 477), Persistently Moderate (n = 749), and Persistently High (n = 488). Multivariable Cox regression (fully adjusted Model 3) revealed that compared to the Persistently Low group, both the Persistently High group and Persistently Moderate group exhibited significantly lower 28-day mortality risk (HR = 0.72, 95% CI: 0.56-0.93 for High; HR = 0.77, 95% CI: 0.61-0.96 for Moderate) and 90-day mortality risk (HR = 0.74, 95% CI: 0.60-0.92 for High; HR = 0.72, 95% CI: 0.60-0.87). For ML predictions, LightGBM achieved optimal AUCs for ICU mortality (0.758) and 28-day mortality (0.753), while RandomForest performed best for 90-day mortality (AUC = 0.721). SHAP analysis ranked serum albumin trajectories as the third most critical predictor for 90-day mortality. The nomogram showed good discrimination for 90-day mortality prediction, with an AUC of 0.765, and the calibration curve suggested acceptable agreement between predicted and observed risks.

Serum albumin trajectory changes in sepsis were associated with mortality outcomes at different time windows. In the fully adjusted Cox models, these trajectory changes remained significantly associated with 28-day and 90-day all-cause mortality, whereas their association with ICU mortality was attenuated after comprehensive adjustment. The integrated machine learning framework, particularly LightGBM and RandomForest, provides clinicians with interpretable tools for prognosis stratification in patients with sepsis.