New and Improved Score Predicts Severe Infections in Lupus Patients More Accurately
Key Takeaways
- SLESIS-R is a revised score for predicting severe infections in lupus patients.
- The revised tool is more accurate and feasible than the original version.
- Key predictive factors include age, hospitalization, past infections, and glucocorticoid use.
Did You Know?
Introduction to Lupus and Severe Infections
Systemic lupus erythematosus (SLE) is an autoimmune disease that affects various parts of the body. One of the major concerns for patients with SLE is the risk of severe infections. Understanding and predicting these risks can help in better managing the condition and implementing preventive measures.
The Need for an Accurate Prediction Tool
Dr. Inigo Rua-Figueroa and his team from the Hospital Universitario Gran Canaria Doctor Negrin in Spain emphasized the importance of accurately estimating the risk of infections in SLE patients. They noted the lack of a widely validated and suitable score used in daily clinical practice.
The Original SLE Severe Infection Score
The original SLE Severe Infection Score (SLESIS) was developed using retrospective data from the Spanish Rheumatology Society Systemic Lupus Erythematosus Registry. It aimed to predict severe infections but showed only moderate performance.
Development and Improvement of SLESIS-R
To improve the accuracy, the researchers developed the SLE Severe Infection Score-Revised (SLESIS-R) using high-quality data from a prospective phase of the registry. This version included new markers identified through literature review.
The study included 1,459 patients with an average age of 49 years, analyzing variables previously part of SLESIS and additional indicators to construct a multivariable logistic model.
Key Predictive Variables
The revised score identified four key variables predictive of severe infections within the following year:
- Age 60 years or older
- Previous SLE-related hospitalization
- Past serious infection
- Glucocorticoid usage of 30 mg per day or greater
These variables contributed to a total score ranging from zero to 17.
Model Performance
The performance of SLESIS-R showed a significant improvement, with an area under the receiving operating curve (AUC) of 0.861, compared to the original SLESIS with an AUC of 0.79. A cutoff score of six or greater demonstrated an 85.9% accuracy.
Enhanced Feasibility
The updated measure also improved feasibility by excluding complex components like the Katz severity index. This simplification enhances its usability in both clinical practice and research.
Validation and Practical Usage
Internal validation using a 100-sample bootstrapping procedure provided robust discrimination parameters with a C statistic of 0.81.
The researchers highlight that SLESIS-R, due to its simplicity and reliance on clinical parameters rather than laboratory results, could be effective in daily practice and clinical trials.
Conclusion
With its accurate predictive capacity and practical application, SLESIS-R enhances informed decision-making and supports the implementation of effective preventive measures for severe infections in lupus patients.
