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Objective <p>This study aimed to develop and validate a nomogram prediction model based on the clinical characteristics, optic nerve status, and visual function to predict treatment outcomes after primary glaucoma surgery, providing a tool for clinical decision-making.</p> Methods <p>Clinical data from patients who underwent primary glaucoma surgery and received timolol maleate eye drops between January 2021 and March 2024 were retrospectively analyzed. Univariate and multivariate Logistic regression analyses were used to identify independent predictors. A nomogram was constructed based on regression coefficients. Model performance was assessed using the concordance index (C-index) and calibration curves.</p> Results <p>Among 220 enrolled patients, 154 were assigned to the training set and 66 to the validation set. Poor treatment outcomes occurred in 27.27% and 25.76% of each group, respectively. Multivariate analysis identified cup-to-disc ratio, vascular resistance index, distant visual acuity, mean visual field level, central visual field sensitivity, and frequency contrast sensitivity (low, medium and high) as independent predictors of poor outcomes (all <i>P</i> &lt; 0.05). The nomogram demonstrated high predictive accuracy, with area under the curve values of 0.928 (95% CI 0.879–0.978) in the training set and 0.823 (95% CI 0.640–1.000) in the validation set. Sensitivity and specificity were 0.879 and 0.827 in the training set, and 0.778 and 0.919 in the validation set, respectively. Calibration curves indicated good model fit.</p> Conclusion <p>The nomogram model accurately predicts treatment outcomes after primary glaucoma surgery, showing strong discriminative ability and calibration. It may assist clinicians in personalizing postoperative treatment strategies. Further large-sample, multicenter studies are warranted for validation and broader application.</p>

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Integrating contrast sensitivity and vascular resistance into a nomogram to predict postoperative treatment outcomes in primary glaucoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Ruibao Liu,
  • Hao Guo,
  • Tianyu Gao,
  • Qin Huo,
  • Mingyi Shao

摘要

Objective

This study aimed to develop and validate a nomogram prediction model based on the clinical characteristics, optic nerve status, and visual function to predict treatment outcomes after primary glaucoma surgery, providing a tool for clinical decision-making.

Methods

Clinical data from patients who underwent primary glaucoma surgery and received timolol maleate eye drops between January 2021 and March 2024 were retrospectively analyzed. Univariate and multivariate Logistic regression analyses were used to identify independent predictors. A nomogram was constructed based on regression coefficients. Model performance was assessed using the concordance index (C-index) and calibration curves.

Results

Among 220 enrolled patients, 154 were assigned to the training set and 66 to the validation set. Poor treatment outcomes occurred in 27.27% and 25.76% of each group, respectively. Multivariate analysis identified cup-to-disc ratio, vascular resistance index, distant visual acuity, mean visual field level, central visual field sensitivity, and frequency contrast sensitivity (low, medium and high) as independent predictors of poor outcomes (all P < 0.05). The nomogram demonstrated high predictive accuracy, with area under the curve values of 0.928 (95% CI 0.879–0.978) in the training set and 0.823 (95% CI 0.640–1.000) in the validation set. Sensitivity and specificity were 0.879 and 0.827 in the training set, and 0.778 and 0.919 in the validation set, respectively. Calibration curves indicated good model fit.

Conclusion

The nomogram model accurately predicts treatment outcomes after primary glaucoma surgery, showing strong discriminative ability and calibration. It may assist clinicians in personalizing postoperative treatment strategies. Further large-sample, multicenter studies are warranted for validation and broader application.