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  • Title: Risk assessment in the management of patients with ocular hypertension.
    Author: Weinreb RN, Friedman DS, Fechtner RD, Cioffi GA, Coleman AL, Girkin CA, Liebmann JM, Singh K, Wilson MR, Wilson R, Kannel WB.
    Journal: Am J Ophthalmol; 2004 Sep; 138(3):458-67. PubMed ID: 15364230.
    Abstract:
    PURPOSE: To develop a model for estimating the global risk of disease progression in patients with ocular hypertension and to calculate the "number-needed-to-treat" (NNT) to prevent progression to blindness as an aid to practitioners in clinical decision making. DESIGN: Development of a mathematical model for estimating risk of glaucoma progression. METHODS: Population-based studies of patients with ocular hypertension and glaucoma were reviewed by a panel of glaucoma specialists. Measures of disease progression risks derived from three long-term studies and assumptions based on the available data were used to estimate the risk of progression from ocular hypertension to glaucoma and glaucoma to unilateral blindness for untreated and treated patients over a 15-year period. Using these estimates, the NNT (1/absolute risk reduction on treatment) to prevent unilateral blindness in one patient with ocular hypertension was calculated. RESULTS: In untreated patients, the estimated risk of progression from ocular hypertension to unilateral blindness was 1.5% to 10.5% and in treated patients, the estimated risk of progression was 0.3% to 2.4% over 15 years. From these estimates, between 12 and 83 patients with ocular hypertension will require treatment to prevent one patient from progressing to unilateral blindness over a 15-year period. CONCLUSION: Global risk assessment that incorporates all available data plays a vital role in managing patients with ocular hypertension. A more precise understanding of long-term vision loss should be factored into decisions pertaining to the initiation of glaucoma therapy. Undoubtedly, these estimates will evolve and change with the availability of new population-based epidemiologic information and improvements in multivariable model testing.
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