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Updated: Sep 22, 2025
A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy
A feasibility study of risk prediction modelling for vasoocclusive crisis in children with sickle cell disease
Merve Türkegün Şengül1, Bahar Taşdelen1, Selma Ünal2
1Departments of Biostatistics and Medical Information, Mersin University Faculty of Medicine, Mersin.
Insights
Tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and white blood cell count (WBC) can predict vaso-occlusive crisis (VOC) risk in children with sickle cell disease (SCD). TNF-α showed the strongest predictive capability.
Area of Science:
- Biomarkers and disease risk prediction
- Statistical modeling in clinical research
- Pediatric hematology
Background:
- Biomarkers are crucial for managing sickle cell disease (SCD).
- Predictiveness curves (PC) classify individuals into low- and high-risk categories.
- Identifying predictive biomarkers for vaso-occlusive crisis (VOC) is essential for guiding preventive strategies.
Purpose of the Study:
- To introduce and define the predictiveness curve (PC) statistical method.
- To identify biomarkers that predict VOC risk in pediatric SCD patients.
- To guide preventive treatment decisions for VOC in children with SCD.
Main Methods:
- A feasibility study included 38 pediatric SCD patients.
- Biomarkers measured: Leucocytes (WBC), C-reactive protein (CRP), IL-6, TNF-α, and YKL-40.
- Predictiveness curves (PC) and ROC curves evaluated risk prediction and classification performance.
Main Results:
- The PC identified TNF-α, IL-6, and WBC as predictors of high VOC risk.
- TNF-α demonstrated the highest predictive value for VOC risk (TPF = 0.67).
Conclusions:
- The PC method aids clinicians by integrating multiple biomarkers for disease risk assessment.
- This study is the first to assess biomarkers for predicting VOC risk in SCD patients.
- Findings can inform preventive treatment strategies for VOC in children with SCD.
Background:
The availability of a selection of biomarkers that includes information about disease risk is very important in the treatment of sickle cell disease (SCD). We used the predictiveness curve (PC), which classifies diseased individuals according to low- and high-risk thresholds, for this purpose. Our aim was to define this new statistical method and to determine the biomarkers that predict vaso-occlusive crisis (VOC) in children with SCD to guide preventive treatment.
Methods:
Thirty-eight pediatric patients with SCD were included in this feasibility study. Leucocytes (WBC), C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor (TNF-α), and YKL-40 were studied in patients with VOC and without VOC. The patient group with a low or high risk of VOC was assessed using the PC. Risk prediction and classification performance were evaluated using the PC and receiver operating characteristic (ROC) curve.
Results:
According to the PC, patients with a high risk of VOC could be detected via TNF-α, IL-6, and WBC, and TNF-α was the best risk prediction marker (TPF = 0.67).
Conclusions:
The PC provides disease risk information by comparing more than one biomarker and can thereby help clinicians determine appropriate preventive treatments. This is the first study to evaluate biomarkers to predict VOC risk in SCD patients.
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