Ophthalmology science · 2025
Age-Related Scattered Hypofluorescent Spots as an Adverse Prognostic Factor for Polypoidal Choroidal Vasculopathy.
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Kim Seo Hee, Ong Kai Tzu-Iunn, Choi Seonghee, Chung Eun Jee, Kim Min, Lee Christopher Seungkyu, Yeo Jinyoung, Choi Eun Young
Аннотация
PURPOSE: Polypoidal choroidal vasculopathy (PCV) demonstrates significant prognostic variability, and the impact of age-related scattered hypofluorescent spots observed in late-phase indocyanine green angiography (ASHS-LIA) on the prognosis of PCV remains under-researched. This study aims to investigate the association between ASHS-LIA in PCV and prognosis using the AdaBoost machine learning model.
DESIGN: A cross-sectional study.
PARTICIPANTS: The study included patients diagnosed with PCV and treated with anti-VEGF therapy at 2 medical institutions between 2012 and 2021.
METHODS: We conducted a retrospective analysis of the clinical characteristics, anti-VEGF treatment history, and outcomes of the participants, classifying them based on the presence or absence of ASHS-LIA. An AdaBoost meta-estimator was applied to predict prognosis, including disease stability, injection frequency, and time to first remission, utilizing features selected through principal component analysis.
MAIN OUTCOME MEASURES: The prognostic significance of ASHS-LIA was assessed by feature importance, with the mean decrease in impurity serving as the evaluation metric.
RESULTS: Of 57 eyes with PCV, 31 exhibited ASHS-LIA and 26 did not. Compared with the non-ASHS-LIA group, the ASHS-LIA group had fewer patients who achieved a super-stable status without recurrence for >18 months postremission (P = 0.03), required a longer time to reach first remission (P = 0.04), and needed more injections (P < 0.001). AdaBoost models confirmed the importance of ASHS-LIA for predicting disease stability, injection demand, and time to first remission, ranking it as the third, seventh, and eighth top contributory factor, respectively.
CONCLUSIONS: Machine learning analysis identified ASHS-LIA as a negative prognostic factor in PCV, correlating with reduced disease stability, higher recurrence rates, and increased treatment requirements. These findings suggest that ASHS-LIA could serve as a valuable marker for assessing prognosis and guiding treatment strategies in PCV management.
FINANCIAL DISCLOSURES: The author(s) have no proprietary or commercial interest in any materials discussed in this article.
DESIGN: A cross-sectional study.
PARTICIPANTS: The study included patients diagnosed with PCV and treated with anti-VEGF therapy at 2 medical institutions between 2012 and 2021.
METHODS: We conducted a retrospective analysis of the clinical characteristics, anti-VEGF treatment history, and outcomes of the participants, classifying them based on the presence or absence of ASHS-LIA. An AdaBoost meta-estimator was applied to predict prognosis, including disease stability, injection frequency, and time to first remission, utilizing features selected through principal component analysis.
MAIN OUTCOME MEASURES: The prognostic significance of ASHS-LIA was assessed by feature importance, with the mean decrease in impurity serving as the evaluation metric.
RESULTS: Of 57 eyes with PCV, 31 exhibited ASHS-LIA and 26 did not. Compared with the non-ASHS-LIA group, the ASHS-LIA group had fewer patients who achieved a super-stable status without recurrence for >18 months postremission (P = 0.03), required a longer time to reach first remission (P = 0.04), and needed more injections (P < 0.001). AdaBoost models confirmed the importance of ASHS-LIA for predicting disease stability, injection demand, and time to first remission, ranking it as the third, seventh, and eighth top contributory factor, respectively.
CONCLUSIONS: Machine learning analysis identified ASHS-LIA as a negative prognostic factor in PCV, correlating with reduced disease stability, higher recurrence rates, and increased treatment requirements. These findings suggest that ASHS-LIA could serve as a valuable marker for assessing prognosis and guiding treatment strategies in PCV management.
FINANCIAL DISCLOSURES: The author(s) have no proprietary or commercial interest in any materials discussed in this article.