Machine Learning Model for Early Prediction of Sepsis Outcomes Based on Immune Response

Abstract

Background: Host immune dysregulation participates in the prognosis of sepsis with high morbidity and mortality. The contribution of sepsis to alive or dead, and the early immunologic signature to which they are preventable, is unknown. Therefore, knowing the immunogenomic landscape in blood samples is of paramount importance. This study develops a machine learning model to learn signature IRGs associated with the dysregulation of the host immune in sepsis and to predict sepsis survival up to 24 h at diagnosis that may be useful for planning individualized therapies in the future.Methods: A total of 142 sepsis patients with corresponding clinical information were retrieved and analyzed from January 1, 2022, to March 31, 2022, as a secondary analysis of public data. The variables used for analysis included demographic characteristics, clinical conditions, and the differentially expressed immune-related genes (IRGs). The machine learning methods used included logistic regression, least absolute shrinkage, and selection operator (LASSO), random forest (RF). Prediction accuracy was randomly assigned to training, test, and validation sets. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC). We also utilized a calibration curve to explain the prediction model. Results: Our study cohort included 142 sepsis patients (mean [SD] age, 61.8 [15.8] years; 86 [60.6%] men; 104 [73.2%] survivor) downloaded from GEO database.The prognostic model based on IRGs and SOFA scores at diagnosis performed well in sepsis survival estimations (area under the curve, 0.842; 95% CI, 0.704-0.875). This model included a total of six survival-associated IRGs. Patients assigned to the high-risk group had worse survival than patients from the low-risk group (27 deaths [38%] vs 11 deaths [15.0%]; P < 0 .001). The cell adhesion molecules (CAMs), chemokine signaling, and antigen processing and presentation pathways were the associated pathways for survival (P < .001).Conclusions: This cohort study developed a prognostic modeling tool for sepsis survival based on IRG expression profiles, and has the capacity for early prediction of sepsis outcomes via monitoring the immunogenomic landscape, and also possibly the individualized therapies for sepsis survival.