Development and validation of optical coherence tomography (OCT) and OCT angiography prediction model for short-term
Shiyu Cheng1,2, Xinyu Zhao1,2, Qing Zhao1,2
1Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Aims:
To construct and validate an optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) image model for predicting the occurrence of short-term vitreous haemorrhage (VH) in polypoidal choroidal vasculopathy (PCV) patients.
Methods:
We retrospectively collected clinical and imaging information from patients diagnosed with PCV at Peking Union Medical College Hospital, Beijing, China, between January 2015 and October 2022. Six different screening strategies, including univariate analysis, multivariate analysis, least absolute shrinkage and selection operator, stepwise logistic regression, random forest and clinical-data-only approach, were used to select variables and build models. The nomogram was constructed based on the model with the best area under the curve (AUC) and was evaluated using receiver operating characteristic curves, calibration curves, decision curve analysis and clinical impact curves.
Results:
A total of 147 PCV patients were included and randomly divided into a training set (103 patients) and a validation set (44 patients), with an average follow-up time of 17.56±14.99 months. The optimal model that achieved higher AUC in both training and validation sets incorporated seven significant variables identified through univariate analysis: male [OR=2.76, p=0.022], central macular thickness [OR=1.003, p=0.002], the presence of haemorrhagic pigment epithelial detachment (HPED) [OR=6.99, p<0.001], the height of HPED [OR=1.002, p<0.001], the area of HPED [OR=1.16, p<0.001], the presence of multiple PEDs [OR=2.94, p=0.016] and the presence of subretinal haemorrhage [OR=3.11, p=0.011]. A predictive nomogram based on these variables yielded an AUC of 0.896 (95% CI 0.827 to 0.965) in the training set and 0.861 (95% CI 0.749 to 0.973) in the validation set, demonstrating good calibration and clinical usefulness.
Conclusion:
The proposed OCT/OCTA-based image nomogram, as a novel and non-invasive tool, achieved satisfactory prediction of VH secondary to PCV.
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