BackgroundTo evaluate factors influencing best corrected visual acuity (BCVA) in paediatric patients with bilateral congenital cataracts (CC) after cataract extraction and intraocular lens (IOL) implantation, as well as develop a robust model for predicting long-term visual acuity.MethodsThis retrospective study followed 194 paediatric patients with bilateral CC from January 2008 to December 2021. The endpoint event was defined as a final BCVA < 0.22 Log MAR at the last follow-up, which indicated good outcome. The probability of reaching this endpoint event was modelled using Cox proportional hazards regression analysis and internally validated through 200 iteration of 5-fold cross-validation.ResultsA prognostic model for long-term visual acuity in bilateral CC after surgical treatment was established as follows: ln h(t) = −0.009 × “age at cataract extraction” − 0.015 × “age at IOL implantation” − 2.934 × “without nystagmus at last follow − up” + ln h0(0), in which h0(t) represents the baseline risk equation that can be any non-negative equation for time (t); h(t) represents the probability of the endpoint event occurring at time (t) without any endpoint event occurring before it. The model was visualized using a nomogram and contour plot to facilitate clinical practice. The model demonstrated reasonably accurate discrimination with an area under the receiver operating characteristic curve of 0.712 (95% confidence interval [CI]: 0.589–0.835) and a C-index of 0.797 (95% CI: 0.683–0.911). According to the model, children with bilateral CC had a higher likelihood of achieving a good outcome (BCVA < 0.22 Log MAR) if they underwent cataract extraction before the age of six months (hazard ratio [HR] 1.80, 95% CI: 0.92–3.70), received IOL implantation before the age of thirty-one months (HR 3.70, 95% CI: 1.77–7.80), and presented without nystagmus during their last follow-up visit (HR 11.20, 95% CI: 3.96–31.80).ConclusionsThis long-term visual acuity prognostic model demonstrates adequate performance for individualized prediction and assists in clinical decision-making. The risk stratification index guides optimal timing for surgery.
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