Network Model Predicts Risk Of Death In Sickle Cell Disease

Analysis of the sickle gene in different regions of Africa and the Middle East showed that the gene arose several times independently. The haplotypes are named for the geographic regions where they were identified: Senegal, Benin, Central African Republic (CAR) and Asian (Middle East).
"The beta-globin gene exists in a region of chromosome 11 called the "beta globin locus." The substitutions in the flanking regions of the gene (the haplotypes) show that Hb S arose separately at least four times in Africa, and once in Asia, possibly in India (Nagel and Fleming, 1992)." (Harvard University, http://sickle.bwh.harvard.edu/scdmanage.html)




Network Model Predicts Risk Of Death In Sickle Cell Disease

Science Daily — Researchers from Boston University School of Medicine (BUSM) and Boston University School of Public Health (BUSPH) have developed a method to estimate sickle cell disease severity and predict the risk of death in people with this disease. The study appears online in the June issue of the journal Blood.

Sickle cell disease is caused by mutations in the beta-hemoglobin gene (HBB). Individuals having identical pairs of genes for the HBB glu6val mutation (HbS) have sickle cell anemia; individuals with both HbS and HbC mutations have sickle cell-HbC (HbSC) disease. Both of these types of sickle cell-disease have extremely variable characteristics. While the median age of death in the United States was estimated to be in the fifth decade for patients with sickle cell anemia, some individuals die young while others live into their eight or ninth decade.

Using data from 3,380 adult and pediatric patients accounting for all common genotypes of sickle cell disease, researchers developed a predictive model of disease severity, using Bayesian network modeling. This type of network modeling can represent the mutual and hierarchal relationships among many variables using probalistic rules, making it more appropriate for prognostic and diagnostic applications, according to lead author, Paola Sebastiani, PhD, associate professor of biostatistics in BUSPH.

The analysis revealed the complex network of associations between laboratory tests and clinical events that modulate the risk of death in sickle cell disease. Along with previously known risk factors for mortality, like renal insufficiency and leukocytosis, the network identified laboratory markers of the severity of the hemolytic anemia and its associated clinical events as contributing risk factors. Researchers computed the risk of death within 5 years with a disease severity score ranging from zero (least severe) to one (most severe). Patients were followed on average for five years. Sepsis was among the most frequent case of death (14%) followed by cerebrovascular accident (10%).

The reliability of the model was supported by analysis of two independent patient groups. In group one, the severity score was related to disease severity based on the opinion of expert clinicians. In the other group, the severity score was related to the presence and severity of pulmonary hypertension and the risk of death.

"This model can be used to compute a personalized disease severity score allowing therapeutic decisions to be made according to the prognosis," said senior author Martin Steinberg, MD, professor of medicine at BUSM. "The severity score could also serve as an estimate of overall disease severity in genotype-phenotype association studies and provide an additional method to study the complex pathophysiology of sickle cell disease."