Nomogram-based model to predict prognosis in anisometropic amblyopia

This study aimed to identify predictive factors and develop an effective nomogram to estimate the prognosis of anisometropic amblyopia.

We retrospectively analyzed 2,897 patients aged 3-18 years diagnosed with anisometropic amblyopia, with at least 12 months of follow-up. The cure criterion was a best corrected visual acuity (BCVA) of 0.1 LogMAR or better in the amblyopic eye, with less than one line of interocular difference. The potential predictors included 15 baseline clinical characteristics. Participants were randomly assigned (7:3) to the training and validation sets. A nomogram to predict the prognosis of amblyopia was computed using a logistic regression model with least absolute shrinkage and a selection operator. Model performance was assessed by discrimination (area under the curve [AUC]) and calibration (calibration plots).

This study included 2,897 patients, of whom 35.2% met the criteria for cured amblyopia. The training and validation sets comprised 2,040 and 857 participants, respectively. The predictors included in the nomogram were baseline age (AUC: 0.703 [95% CI 0.679-0.727]), difference in interocular BCVA (AUC: 0.688 [95% CI 0.664-0.711]), and spherical equivalence (SE) (AUC: 0.653 [95% CI 0.629-0.678]). The calibration curves of the nomogram showed good agreement between the predicted and observed probabilities, with an AUC of 0.783 (95% CI 0.763-0.803) in the training set and 0.782 (95% CI 0.750-0.814) in the validation set.

The nomogram, incorporating baseline age, differences in interocular BCVA, and SE, provides individualized prognostic predictions for anisometropic amblyopia treatment, aiding clinicians in personalized treatment planning and better resource allocation. Furthermore, the nomogram could enhance shared decision-making with parents by providing objective prognostic data, thus improving treatment compliance.