Corticosteroid sensitivity detection in sepsis patients using a personalized data mining approach: A clinical investigation

Sepsis is a life-threatening disease with high mortality, incidence, and morbidity. Corticosteroids (CS) are a recommended treatment for sepsis, but some patients respond negatively to CS therapy. Early prediction of corticosteroid responsiveness can help intervene and reduce mortality. In this study, we aim to develop a data mining methodology for predicting CS responsiveness of septic patients. We used data from a randomized controlled trial called APROCCHSS, which recruited 1241 septis patients to study the effectiveness of corticotherapy. We conducted a thorough study of multiple machine learning models to select the most efficient prediction model, called "signature". We evaluated the performance of the signature using precision, sensitivity, and specificity values. We found that Logistic Regression was the best model with an AUC of 72%. We conducted further experiments to examine the impact of additional features and the model's generalizability to different groups of patients. We also performed a statistical analysis to analyze the effect of the treatment at the individual level and on the population as a whole. Our data mining methodology can accurately predict cortico-sensitivity or resistance in septis patients. The signature has been deployed into the Assistance Publique – Hôpitaux de Paris (APHP) information system as a web service, taking patient information as input and providing a prediction of cortico-sensitivity or resistance. Early prediction of corticosteroid responsiveness can help clinicians intervene promptly and improve patient outcomes.