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Purpose <p>This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve the discrimination between clinically diagnosed normal-tension glaucoma (NTG) and non-glaucomatous high myopia (HM) within a highly myopic population.</p> Methods <p>In this prospective cross-sectional diagnostic accuracy study, we consecutively enrolled two groups of participants: patients with high myopia (HM) and those with HM complicated by normal-tension glaucoma (HM-NTG). All clinical and imaging data were collected at a single time point using standardized protocols. Baseline clinical data were collected for all participants. Optic disc structural parameters were acquired using optical coherence tomography (OCT), while optic disc perfusion parameters were obtained via OCT angiography (OCTA). Receiver operating characteristic (ROC) curve analysis was first conducted to evaluate the diagnostic performance of individual parameters. Least absolute shrinkage and selection operator (LASSO) regression was employed for preliminary dimensionality reduction and feature selection, followed by multivariate logistic regression (backward stepwise method) to identify the optimal parameter combination. A diagnostic model was developed based on logistic regression and rigorously validated through bootstrap resampling, calibration assessment, decision curve analysis, and clinical impact curve. A machine learning diagnostic model was constructed using the support vector machine (SVM) algorithm and compared with the conventional regression model. Finally, Shapley additive explanations (SHAP) were applied to interpret the SVM model’s decision-making mechanism and elucidate the individualized contribution of each feature to the prediction outcomes.</p> Results <p>A total of 87 patients were enrolled, comprising 42 in the HM group and 45 in the HM-NTG group. Overall, OCT parameters demonstrated superior diagnostic clarity compared with OCTA microvascular indices. The inferior macular ganglion cell complex (GCC) exhibited the strongest discriminatory performance, with an area under the receiver operating characteristic curve (AUC) of 0.85. Among OCTA parameters, the inferior optic disc vessel density (VD) achieved the best diagnostic performance (AUC = 0.72). LASSO regression combined with multivariate logistic regression identified three variables for model construction: inferior GCC thickness (odds ratio [OR] = 0.72), temporal-inferior retinal nerve fiber layer (RNFL) thickness (OR = 0.82), and inferior VD (OR = 0.77). The OCT + OCTA combined model achieved an AUC of 0.909 (95% CI: 0.833–0.984). While this did not significantly exceed the OCT-only model (AUC = 0.888, 95% CI: 0.803–0.973; DeLong test, <i>P</i> = 0.164), the addition of OCTA-derived inferior vessel density yielded significant net reclassification improvement (NRI = 0.679, <i>P</i> = 0.003) and integrated discrimination improvement (IDI = 0.067, <i>P</i> = 0.029), indicating enhanced risk stratification. Bootstrap resampling with 500 iterations yielded an AUC of 0.861 (95% CI: 0.773–0.949). The Hosmer-Lemeshow goodness-of-fit test indicated adequate calibration (χ² = 6.64, <i>P</i> = 0.575), with a Brier score of 0.109. Calibration curves demonstrated close adherence to the ideal diagonal across both low-risk and high-risk probability thresholds. Decision curve analysis indicated favorable net benefit. An SVM model constructed using the same three predictors achieved a comparable AUC of 0.908 (95% CI: 0.833–0.982), providing cross-algorithmic validation of the logistic model’s robustness. SHAP analysis was applied to elucidate individualized feature contributions to patient-specific predictions, confirming that lower values of inferior GCC thickness exerted the greatest directional influence on HM-NTG classification.</p> Conclusions <p>Within highly myopic eyes, the combination of inferior GCC thickness, temporal-inferior RNFL thickness, and inferior VD may help distinguish clinically diagnosed HM-NTG from non-glaucomatous HM. The addition of OCTA microvascular parameters did not significantly increase the AUC but improved patient risk reclassification. This multimodal approach, paired with conventional regression and machine learning, offers a promising adjunctive tool for diagnostic evaluation in highly myopic eyes.</p>

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Combining structural and microvascular parameters via machine learning for enhanced diagnosis of normal-tension glaucoma among highly myopic eyes: a prospective cross-sectional diagnostic accuracy study

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  • 发表日期：

    2026年6月26日

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  • Chang-Zhu He,
  • Qin Qiu,
  • Heng Lai,
  • Chun-Yan Lai,
  • Yu He,
  • Lin Jing

摘要

Purpose

This study aimed to evaluate whether a combination of optical coherence tomography (OCT) and OCT angiography (OCTA) parameters could improve the discrimination between clinically diagnosed normal-tension glaucoma (NTG) and non-glaucomatous high myopia (HM) within a highly myopic population.

Methods

In this prospective cross-sectional diagnostic accuracy study, we consecutively enrolled two groups of participants: patients with high myopia (HM) and those with HM complicated by normal-tension glaucoma (HM-NTG). All clinical and imaging data were collected at a single time point using standardized protocols. Baseline clinical data were collected for all participants. Optic disc structural parameters were acquired using optical coherence tomography (OCT), while optic disc perfusion parameters were obtained via OCT angiography (OCTA). Receiver operating characteristic (ROC) curve analysis was first conducted to evaluate the diagnostic performance of individual parameters. Least absolute shrinkage and selection operator (LASSO) regression was employed for preliminary dimensionality reduction and feature selection, followed by multivariate logistic regression (backward stepwise method) to identify the optimal parameter combination. A diagnostic model was developed based on logistic regression and rigorously validated through bootstrap resampling, calibration assessment, decision curve analysis, and clinical impact curve. A machine learning diagnostic model was constructed using the support vector machine (SVM) algorithm and compared with the conventional regression model. Finally, Shapley additive explanations (SHAP) were applied to interpret the SVM model’s decision-making mechanism and elucidate the individualized contribution of each feature to the prediction outcomes.

Results

A total of 87 patients were enrolled, comprising 42 in the HM group and 45 in the HM-NTG group. Overall, OCT parameters demonstrated superior diagnostic clarity compared with OCTA microvascular indices. The inferior macular ganglion cell complex (GCC) exhibited the strongest discriminatory performance, with an area under the receiver operating characteristic curve (AUC) of 0.85. Among OCTA parameters, the inferior optic disc vessel density (VD) achieved the best diagnostic performance (AUC = 0.72). LASSO regression combined with multivariate logistic regression identified three variables for model construction: inferior GCC thickness (odds ratio [OR] = 0.72), temporal-inferior retinal nerve fiber layer (RNFL) thickness (OR = 0.82), and inferior VD (OR = 0.77). The OCT + OCTA combined model achieved an AUC of 0.909 (95% CI: 0.833–0.984). While this did not significantly exceed the OCT-only model (AUC = 0.888, 95% CI: 0.803–0.973; DeLong test, P = 0.164), the addition of OCTA-derived inferior vessel density yielded significant net reclassification improvement (NRI = 0.679, P = 0.003) and integrated discrimination improvement (IDI = 0.067, P = 0.029), indicating enhanced risk stratification. Bootstrap resampling with 500 iterations yielded an AUC of 0.861 (95% CI: 0.773–0.949). The Hosmer-Lemeshow goodness-of-fit test indicated adequate calibration (χ² = 6.64, P = 0.575), with a Brier score of 0.109. Calibration curves demonstrated close adherence to the ideal diagonal across both low-risk and high-risk probability thresholds. Decision curve analysis indicated favorable net benefit. An SVM model constructed using the same three predictors achieved a comparable AUC of 0.908 (95% CI: 0.833–0.982), providing cross-algorithmic validation of the logistic model’s robustness. SHAP analysis was applied to elucidate individualized feature contributions to patient-specific predictions, confirming that lower values of inferior GCC thickness exerted the greatest directional influence on HM-NTG classification.

Conclusions

Within highly myopic eyes, the combination of inferior GCC thickness, temporal-inferior RNFL thickness, and inferior VD may help distinguish clinically diagnosed HM-NTG from non-glaucomatous HM. The addition of OCTA microvascular parameters did not significantly increase the AUC but improved patient risk reclassification. This multimodal approach, paired with conventional regression and machine learning, offers a promising adjunctive tool for diagnostic evaluation in highly myopic eyes.