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New Prognostic Model Enhances Prediction of Mortality in Severe Drug Reactions

New Prognostic Model Enhances Prediction of Mortality in Severe Drug Reactions

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A new prognostic model utilizing routine blood tests offers an effective way to predict mortality risk in patients with severe drug reactions like DRESS, enabling earlier and more personalized interventions.

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A recent study introduces a novel prognostic tool that leverages routine blood tests to predict mortality risk in patients suffering from drug reaction with eosinophilia and systemic symptoms (DRESS), a rare but potentially fatal adverse reaction to certain medications. Early identification of high-risk patients is vital for improving outcomes, as current prognostic options are limited, especially compared to other severe skin reactions like Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN).

The research team from National Taiwan University, led by Professor Chia-Yu Chu, focused on simple inflammatory markers accessible through standard complete blood counts, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and red blood cell distribution width (RDW). These markers are indicative of systemic inflammation and immune response, which are central to DRESS pathophysiology.

Conducted across hospitals in Taiwan, Singapore, and Japan, the study analyzed blood samples taken within three days of DRESS diagnosis. The findings revealed that a lower hemoglobin-to-RDW ratio (HRR), higher PLR, and decreased monocyte counts were significantly associated with increased mortality risk. Using these variables, the researchers developed a predictive model that demonstrates high accuracy and reliability, validated internally within the cohort.

This practical approach allows clinicians to quickly assess patient risk using standard blood tests and consider more aggressive treatment or monitoring for those identified as high risk. Lead author Tyng-Shiuan Hsieh emphasized the importance of integrating these markers with clinical data to enhance risk stratification. Professor Chu highlighted that this model fosters personalized treatment strategies, ultimately aiming to reduce mortality from this severe adverse drug reaction.

This advancement in risk prediction tools provides a promising step toward improving clinical management of DRESS and aligns with the broader goal of personalized medicine in dermatology and pharmacovigilance.

Source: https://medicalxpress.com/news/2025-09-prognostic-severe-cutaneous-adverse-reaction.html

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