Risk factors of falls in elderly patients with visual impairment - Ouyang, Shuyi; Zheng, Chunwen; Lin, Zhanjie; Zhang, Xiaoni; Li, Haojun; Fang, Ying; Hu, Yijun; Yu, Honghua; Wu, Guanrong (Frontiers in Public Health, 2022)
Objective: To examine the risk factors for falls in elderly patients diagnosed with visual impairment (VI) after ophthalmic surgeries and develop a fall risk prediction tool for individuals. Methods: Between January 2019 to March 2021, a total of 251 elderly patients aged 65-92 years diagnosed with VI after ophthalmic surgeries were enrolled and then prospectively followed up for 12 months to evaluate outcomes of accidental falls via telephone interviews. Information of demographics and lifestyle, gait and balance deficits, and ophthalmic and systemic conditions were collected during baseline visits. Forward stepwise multivariable logistic regression analysis was performed to identify independent risk factors of falls in elderly patients with VI, and a derived nomogram was constructed. Results: A total of 143 falls were reported in 251 elderly patients during follow-up, with an incidence of 56.97%. The risk factors for falls in elderly patients diagnosed with VI after ophthalmic surgeries identified by multivariable logistic regression were women (odds ratio [OR], 95% confidence interval [CI]: 2.32, 1.23-4.38), smoking (3.24, 1.24-8.52), outdoor activities /3 months (1.28, 1.06-1.54), waking up frequently during the night (2.04, 1.14-3.68), disorders of balance and gait (3.13, 1.58-6.21), glaucoma (3.70, 1.38-9.93), and other retinal degenerations (4.01, 1.46-10.97). A nomogram was developed based on the abovementioned multivariate analysis results. The area under receiver operating characteristic curve of the predictive model was 0.772. Conclusions: Gender, smoking, outdoor activities, waking up at night, disorders of balance and gait, glaucoma, and other retinal degeneration were independent risk factors for falls in elderly patients diagnosed with VI after ophthalmic surgeries. The predictive model and derived nomogram achieved a satisfying prediction of fall risk in these individuals.