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Article Dans Une Revue Computer Methods and Programs in Biomedicine Année : 2024

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

Résumé

Background and Objective: 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. Methods: 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. Results: 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. Conclusions: 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.
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Dates et versions

hal-04385119 , version 1 (10-01-2024)

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  • HAL Id : hal-04385119 , version 1

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Rahma Hellali, Zaineb Chelly Dagdia, Ahmed Ktaish, Karine Zeitouni, Djillali Annane. Corticosteroid sensitivity detection in sepsis patients using a personalized data mining approach: a clinical investigation. Computer Methods and Programs in Biomedicine, inPress. ⟨hal-04385119⟩
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