Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
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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 ( 0.03), required a longer time to reach first remission ( = 0.04), and needed more injections ( < 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.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12179699 | PMC |
http://dx.doi.org/10.1016/j.xops.2025.100818 | DOI Listing |