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

ABSTRACT: 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.

SUBMITTER: Sebastiani P

PROVIDER: S-EPMC1988954 | biostudies-literature | 2007 Oct

REPOSITORIES: biostudies-literature

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

Sebastiani Paola P Nolan Vikki G VG Baldwin Clinton T CT Abad-Grau Maria M MM Wang Ling L Adewoye Adeboye H AH McMahon Lillian C LC Farrer Lindsay A LA Taylor James G JG Kato Gregory J GJ Gladwin Mark T MT Steinberg Martin H MH

Blood 20070628 7

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 ...[more]

PMID: 17600133

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Project description:BackgroundSickle cell retinopathy (SCR) is one of the most important ocular manifestations of sickle cell disease (SCD). This study aims to assess the prevalence of SCR in SCD, identify risk factors for its development and progression to proliferative sickle cell retinopathy (PSCR), and evaluate the potential implications of these results on clinical practice.MethodsThis research is a secondary analysis of patients diagnosed with SCD from the epidemiological, multicenter Cooperative Study of Sickle Cell Disease (CSSCD). We included all patients who completed a full ophthalmic evaluation. We identified clinical and laboratory SCD characteristics associated with SCR using multivariate logistic regression models. Proliferative sickle cell retinopathy (PSCR) was diagnosed according to the Goldberg classification system.ResultsOf the 1904 study participants with SCD who met the inclusion criteria, 953 (50.1%) had retinopathy; of which 642 (67.3%) had bilateral disease. SCR was associated with older age (p < 0.001), history of smoking (p = 0.001), hematuria (p = 0.050), and a lower hemoglobin F (HbF) level (p < 0.001). PSCR risk increased with smoking (p = 0.005), older age (p < 0.001) higher hemoglobin level (p < 0.001) and higher white blood cell count (p = 0.011). Previous blood transfusion (p = 0.050), higher reticulocyte count (p = 0.019) and higher HbF level (p < 0.001) were protective factors against the development of PSCR. Ocular symptoms were associated with progression to PSCR in patients with SCR (p = 0.021).ConclusionIn this cohort of individuals with SCD, half of the participants had signs of SCR. Smoking and blood hemoglobin level were the two modifiable risk factors associated with increased retinopathy progression. Screening to identify the different stages of retinopathy, actively promoting smoking cessation, and optimizing the hematological profile of patients with SCD should guide treatment protocols designed to prevent the vision-threatening complications of the disease.

Dataset Information

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

Publications

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

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