Development and validation of a risk prediction model for visual impairment in older adults

OBJECTIVES: This study aimed to determine the risk factors that affect visual impairment in older adults for developing and evaluating a visual impairment risk prediction model. METHODS: In this hospital-based unmatched case-control design study, we enrolled 586 participants (411 in the training set and 175 in the internal test set) from the ophthalmology clinic and physical examination center of a teaching hospital in Liaoning Province, China, from June to December 2020. Visual impairment was defined as best-corrected visual acuity <6/18 (The WHO definition). Possible influencing factors of visual impairment were assessed, including demographic factors, socioeconomic factors, disease and medication factors, and lifestyle. A visual impairment risk prediction model was developed using binary logistic regression analysis. The area under the ROC curve (AUC) was used to evaluate the effectiveness of the proposed prediction model. RESULTS: Six independent influencing factors of visual impairment in older adults were identified: age, systolic blood pressure, physical activity scores, diabetes, self-reported ocular disease history, and education level. A visual impairment risk prediction model for older adults was developed, showing powerful predictive ability in the training set and internal test set with AUCs of 0.87 (95%CI 0.83–0.90) and 0.81 (95%CI 0.74–0.88), respectively. CONCLUSIONS: The risk prediction model for visual impairment in older adults had high predictive power. Identifying older adults at risk for developing visual impairment can help healthcare workers to adopt appropriate targeted programs for early education and intervention to prevent or delay visual impairment and prevent injuries due to visual impairment in older adults.