advertisement
PRCIS: Treatment strategy of primary angle closure (PAC) is not clear due to the large number of clinical and anatomical-topographic parameters in PAC, influencing treatment algorithm. Using the machine learning method DD-SIMCA, we justify the expediency of early lens extraction in PAC. PURPOSE: to compare the anatomical and functional efficacy of lens extraction (LE) and laser peripheral iridotomy (LPI) in patients with primary angle closure (PAC) using Machine Learning. METHODS: This prospective study included 120 patients aged 41 to 80 years: 60 eyes with PAC, 30 - with PAC suspects, and 30 healthy eyes (control). 30 PAC eyes with IOP up to 30 mm Hg were treated using LE with intraocular lens implantation, and 30 eyes with LPI. All subjects underwent Swept Source optical coherence tomography. We analyzed 35 parameters of each eye including lens vault (LV), the choroidal thickness, anterior chamber angle (ACA) and iris specifications such as iris curvature (ICurv). Considering the correlations between them, the machine learning method DD-SIMCA one-class classification was applied: the proximity of each sample to the target class (control) was characterized by the total distance to it. RESULTS: After LE, IOP was significantly lower than after LPI (P=0.000). Every third eye with PAC after LE reached the target class: specificity according to DD-SIMCA equals 0.67. This was not observed for the eyes after LPI: specificity equals 1.0. After LE, all parameters of ACA did not differ from the control (all P>0.05). After LPI, there was an increase in ACD (P=0.000) and a decrease in LV (P=0.000), but results comparable to the control was achieved only for ICurv (P=1.000). CONCLUSION: The efficacy of LE in PAC is higher than LPI due to the better postoperative anterior chamber topography and lower IOP. This study lends further clinical and anatomic support to the emerging notion of lens extraction as an effective treatment for PAC.
The Ophthalmological Center of the Federal Medical and Biological Agency of the Russian Federation, 15 Gamalei Street, Moscow, Russian Federation, 123098.
Full article