OBJECTIVE: To develop a model to predict visual functional improvement after cataract extraction with intraocular lens implantation based on preoperative data. DESIGN: A prospective study with serial evaluations of visual function preoperatively and at 3 and 12 months after surgery. SETTING: The General Eye Service of the Massachusetts Eye and Ear Infirmary. Boston, Mass, and 33 ophthalmology practices in Boston. PATIENTS: Patients (N = 426; ages, > or = 65 years) who were undergoing cataract surgery. METHODS: Twelve-month improvement in visual function was measured by using the Activities of Daily Vision Scale (ADVS). Ordinal logistic regression was used to identify correlates of improved ADVS scores in 281 patients (derivative set). Potential factors included the preoperative visual acuity, preoperative ADVS score, four chronic ocular diseases, eight medical conditions, and demographic characteristics. Five predictors were identified and used to construct a prediction rule. The accuracy of the prediction rule was evaluated in an independent group of 145 patients (validation set). RESULTS: Postoperatively, 40% of the 281 patients in the derivative set had substantial improvement in their ADVS scores, and 53 (19%) had some improvement. Predictors of improvement included younger age (P < .001), a poorer preoperative ADVS score (P < .001), posterior subcapsular cataract (P = .09), and absence of age-related cataract (P = .09), and absence of age-related macular degeneration (P = .07) and/or diabetes (P = .006). When applied to the independent sample of 145 patients, these five characteristics classified the patients into three groups in which the probabilities of substantial improvement were 85%, 34%, and 3%, thus verifying the discriminatory power of the prediction rule. CONCLUSIONS: Preoperative data can identify patients who are likely to have improvements in visual function after cataract surgery. Such findings may be useful in the selection of patients for this high-volume procedure.
OBJECTIVE: To develop a model to predict visual functional improvement after cataract extraction with intraocular lens implantation based on preoperative data. DESIGN: A prospective study with serial evaluations of visual function preoperatively and at 3 and 12 months after surgery. SETTING: The General Eye Service of the Massachusetts Eye and Ear Infirmary. Boston, Mass, and 33 ophthalmology practices in Boston. PATIENTS: Patients (N = 426; ages, > or = 65 years) who were undergoing cataract surgery. METHODS: Twelve-month improvement in visual function was measured by using the Activities of Daily Vision Scale (ADVS). Ordinal logistic regression was used to identify correlates of improved ADVS scores in 281 patients (derivative set). Potential factors included the preoperative visual acuity, preoperative ADVS score, four chronic ocular diseases, eight medical conditions, and demographic characteristics. Five predictors were identified and used to construct a prediction rule. The accuracy of the prediction rule was evaluated in an independent group of 145 patients (validation set). RESULTS: Postoperatively, 40% of the 281 patients in the derivative set had substantial improvement in their ADVS scores, and 53 (19%) had some improvement. Predictors of improvement included younger age (P < .001), a poorer preoperative ADVS score (P < .001), posterior subcapsular cataract (P = .09), and absence of age-related cataract (P = .09), and absence of age-related macular degeneration (P = .07) and/or diabetes (P = .006). When applied to the independent sample of 145 patients, these five characteristics classified the patients into three groups in which the probabilities of substantial improvement were 85%, 34%, and 3%, thus verifying the discriminatory power of the prediction rule. CONCLUSIONS: Preoperative data can identify patients who are likely to have improvements in visual function after cataract surgery. Such findings may be useful in the selection of patients for this high-volume procedure.
Authors: Mary Kay Margolis; Karin Coyne; Tessa Kennedy-Martin; Timothy Baker; Oliver Schein; Dennis A Revicki Journal: Pharmacoeconomics Date: 2002 Impact factor: 4.981
Authors: J M Quintana; S Garcia; U Aguirre; N Gonzalez; E Arteta; A Escobar; M Bare; J A Blasco; J Martínez-Tapias Journal: Eye (Lond) Date: 2013-05-24 Impact factor: 3.775
Authors: Joshua R Ehrlich; Charlie Frank; Josiah Smiley; Hong-Gam Le; Sanil Joseph; Stephen G Schilling; Brian C Stagg; Joshua D Stein; R D Ravindran; Aravind Haripriya Journal: Health Qual Life Outcomes Date: 2018-01-30 Impact factor: 3.186