Sickle cell disease is caused by mutations in the beta-haemoglobin gene (HBB). Individuals having identical pairs of genes for the HBB glu6val mutation (HbS) have sickle cell anaemia; 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 anaemia, some individuals die young while others live into their eight or ninth decade.
Using data from 3,380 adult and paediatric patients accounting for all common genotypes of sickle cell disease, researchers developed a predictive model of disease severity, using Bayesian network modelling. This type of network modelling 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 haemolytic anemia and its associated clinical events as contributing risk factors. Researchers computed the risk of death within five 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.”
