Blood Journal
Leading the way in experimental and clinical research in hematology

A network model to predict the risk of death in sickle cell disease

  1. Paola Sebastiani1,
  2. Vikki G. Nolan1,2,
  3. Clinton T. Baldwin2,
  4. Maria M. Abad-Grau3,
  5. Ling Wang1,
  6. Adeboye H. Adewoye2,
  7. Lillian C. McMahon2,
  8. Lindsay A. Farrer2,
  9. James G. Taylor IV4,
  10. Gregory J. Kato4,
  11. Mark T. Gladwin4, and
  12. Martin H. Steinberg2
  1. 1Boston University School of Public Health, MA;
  2. 2Boston University School of Medicine, MA;
  3. 3The University of Granada, Granada, Spain;
  4. 4Vascular Medicine Branch, National Heart, Lung and Blood Institute and Critical Care Medicine Department, Clinical Center, National Institutes of Health, Bethesda, MD


Modeling the complexity of sickle cell disease pathophysiology and severity is difficult. Using data from 3380 patients accounting for all common genotypes of sickle cell disease, Bayesian network modeling of 25 clinical events and laboratory tests was used to estimate sickle cell disease severity, which was represented as a score predicting the risk of death within 5 years. The reliability of the model was supported by analysis of 2 independent patient groups. In 1 group, 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. Along with previously known risk factors for mortality, like renal insufficiency and leukocytosis, the network identified laboratory markers of the severity of hemolytic anemia and its associated clinical events as contributing risk factors. This model can be used to compute a personalized disease severity score allowing therapeutic decisions to be made according to the prognosis. The severity score could serve as an estimate of overall disease severity in genotype-phenotype association studies, and the model provides an additional method to study the complex pathophysiology of sickle cell disease.

  • Submitted April 11, 2007.
  • Accepted June 21, 2007.
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