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Prognostic Utility of Optical Coherence Tomography for Long-Term Visual Recovery Following Pituitary Tumor Surgery
Michael T M Wang1, James King2, R C Andrew Symons3
1Department of Ophthalmology, New Zealand National Eye Centre, University of Auckland, Auckland, New Zealand.
Purpose:
To investigate the association between optical coherence tomography (OCT) parameters and long-term visual recovery following optic chiasm decompression surgery.
Design:
Prospective cohort study.
Methods:
Consecutive patients who underwent pituitary or parasellar tumor resection between January 2009 to December 2018 were recruited in a single-center, 2-year prospective, longitudinal cohort study. Best-corrected visual acuity, visual fields, and OCT retinal nerve fiber layer (RNFL) thickness, macular thickness and volume were assessed preoperatively, and at 6 weeks, 6 months, and 2 years postoperatively. Long-term visual field recovery and maintenance were defined as a mean deviation of >-3 at 24 months, and visual acuity recovery and maintenance were defined as a logarithm of minimal angle of resolution (logMAR) of 0 (Snellen 20/20) or better at 24 months.
Results:
A total of 239 patients (129 men, 110 women; mean ± SD age: 52 ± 16 years) were included. Multiple logistic regression analysis demonstrated that increased inferior RNFL thickness (per 10 μm) was associated with higher odds of long-term visual field recovery and maintenance (odds ratio [OR]: 1.26; 95% confidence interval [CI]: 1.12-1.41; Q < 0.001), and greater superior RNFL thickness (per 10 μm) was associated with higher odds of visual acuity recovery and maintenance (OR: 1.13; 95% CI: 1.03-1.27; Q = 0.031). A multivariable risk prediction model developed for long-term visual field recovery and maintenance that incorporated age, preoperative visual function, and RNFL thickness demonstrated C-statistics of 0.83 (95% CI: 0.72-0.94).
Conclusion:
Preoperative RNFL thickness was associated with long-term visual recovery and maintenance following chiasmal decompression. The multivariable risk prediction model developed in the present study may assist with preoperative patient counseling and prognosis.
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