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Blood, 1 October 2007, Vol. 110, No. 7, pp. 2727-2735. Prepublished online as a Blood First Edition Paper on June 28, 2007; DOI 10.1182/blood-2007-04-084921. This Article Full Text Full Text (PDF) Supplemental Methods and Tables All Versions of this Article: blood-2007-04-084921v1 110/7/2727    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Rights and Permissions Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via Google Scholar Google Scholar Articles by Sebastiani, P. Articles by Steinberg, M. H. Search for Related Content PubMed PubMed Citation Articles by Sebastiani, P. Articles by Steinberg, M. H. Related Collections Red Cells Clinical Trials and Observations Social Bookmarking                   What's this?  Previous Article  |  Table of Contents  |  Next Article  RED CELLS A network model to predict the risk of death in sickle cell disease Paola Sebastiani1, Vikki G. Nolan1,2, Clinton T. Baldwin2, Maria M. Abad-Grau3, Ling Wang1, Adeboye H. Adewoye2, Lillian C. McMahon2, Lindsay A. Farrer2, James G. Taylor, IV4, Gregory J. Kato4, Mark T. Gladwin4, and Martin H. Steinberg2 1 Boston University School of Public Health, MA; 2 Boston University School of Medicine, MA; 3 The University of Granada, Granada, Spain; 4 Vascular 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. CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this? This article has been cited by other articles: J. D. Beckman, J. D. Belcher, J. V. Vineyard, C. Chen, J. Nguyen, M. O. Nwaneri, M. G. O'Sullivan, E. Gulbahce, R. P. Hebbel, and G. M. Vercellotti Inhaled carbon monoxide reduces leukocytosis in a murine model of sickle cell disease Am J Physiol Heart Circ Physiol, October 1, 2009; 297(4): H1243 - H1253. [Abstract] [Full Text] [PDF] C. Brugnara Commentary Clin. Chem., June 1, 2009; 55(6): 1231 - 1232. [Full Text] [PDF] B. E. Himes, Y. Dai, I. S. Kohane, S. T. Weiss, and M. F. Ramoni Prediction of Chronic Obstructive Pulmonary Disease (COPD) in Asthma Patients Using Electronic Medical Records J. Am. Med. Inform. Assoc., May 1, 2009; 16(3): 371 - 379. [Abstract] [Full Text] [PDF] M. d. Montalembert Management of sickle cell disease BMJ, September 8, 2008; 337(sep08_1): a1397 - a1397. [Full Text]

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