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Construction of a prediction model for the early efficacy of 577-nm-SML in treating CSC based on Bayesian network
Liying Fang1, Huiyi Zuo1, Minli Huang2
1Ophthalmology Department, First Affiliated Hospital of GuangXi Medical University, Nanning, 530021, China.
Background:
Central Serous Chorioretinopathy(CSC) is a chorioretinal disorder, predominantly affects young to middle-aged adults, resulting serious vision disorder. This study aimed to develop a Bayesian network model to predict the key factors influencing the early therapeutic efficacy of 577 nm-SML in patients with CSC.
Methods:
A total of 159 patients (159 eyes) diagnosed with CSC and treated with 577 nm-SML at the First Affiliated Hospital of Guangxi Medical University from January 2019 to November 2023 were retrospectively analyzed. Baseline data including age, sex, eye side, disease course, and past medical history were collected. Ophthalmic imaging detects central macular thickness (CMT), macular foveal volume (MFV within 1mm, 3mm, 6mm diameter), height and area of subretinal fluid (SRF), structural changes in the outer retinal layers (ORL), type and area of leakage lesions, etc. Influential variables significantly associated with 577nm-SML efficacy were screened using LASSO regression, then construct a Bayesian network model to predict factors that significantly affect the therapeutic effect.
Results:
LASSO regression identified 19 significant variables related to treatment outcomes from the 40 possible risk factors included, including disease duration, sex, eye Side, smoking, hormone, macular foveal volumes (3 mm and 6 mm diameters), and the height and area of SRF, ORL integrity, typical PED, location of PED, location of RPE bulging, heterogeneity of NPL, HF of ORL, HF of choroid, leakage type, leakage location, leakage correlate with OCT. The Bayesian network presents complex interactions among these factors, shows that patients with smaller macular foveal volumes (within 3 mm diameter), shorter disease duration, and focal leakage exhibited superior responses to 577nm-SML treatment.
Conclusion:
The therapeutic response to 577nm-SML in CSC is influenced by multifactorial dynamics. Bayesian network can well present the complex network relationship between the therapeutic effect of 577nm-SML and related influencing factors, and identify potential risk factors that affect early efficacy.