Identifying fallers among ophthalmic patients using classification tree methodology

• Published in: PloS one 2017-03, Vol.12 (3), p.e0174083-e0174083
• Main Authors: Melillo, Paolo, Orrico, Ada, Chirico, Franco, Pecchia, Leandro, Rossi, Settimio, Testa, Francesco, Simonelli, Francesca
• Format: Article
• Language: English
• Subjects: Accidental falls; Accidental Falls - prevention & control; Acuity; Aged; Algorithms; Analysis; Biology and Life Sciences; Blindness; Classification; Computer and Information Sciences; Diabetic retinopathy; Diagnosis; Engineering and Technology; Eye; Eye Diseases - classification; Eye Diseases - complications; Eyewear; Falling; Falls; Female; Glaucoma; Health Status; Humans; Hypertension; Interpersonal Relations; Life Style; Machine learning; Male; Medical personnel; Medicine and Health Sciences; Methods; Older people; Ophthalmologists; Patients; People with disabilities; Physical Sciences; Practice; Prevention; Prospective Studies; Questionnaires; Regression analysis; Research and Analysis Methods; Risk Assessment; Risk Factors; Social Sciences; Studies; Surveys and Questionnaires; Visual acuity; Visual aspects
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0174083

To develop and validate a tool aiming to support ophthalmologists in identifying, during routine ophthalmologic visits, patients at higher risk of falling in the following year. A group of 141 subjects (age: 73.2 ± 11.4 years), recruited at our Eye Clinic, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. Moreover, visual disability was assessed by the Activity of Daily Vision Scale (ADVS). The subjects were followed up for 12 months in order to record prospective falls. A subject who reported at least one fall within one year from the baseline assessment was considered as faller, otherwise as non-faller. Different tree-based algorithms (i.e., C4.5, AdaBoost and Random Forests) were used to develop automatic classifiers and their performances were evaluated by the cross-validation approach. Over the follow-up, 25 falls were referred by 13 patients. The logistic regression analysis showed the following variables as significant predictors of prospective falls: pseudophakia and use of prescribed eyeglasses as protective factors, recent worsening of visual acuity as risk factor. Random Forest ranked best corrected visual acuity, number of sleeping hours and job type as the most important features. Finally, AdaBoost enabled the identification of subjects at higher risk of falling in the following 12 months with a sensitivity rate of 69.2% and a specificity rate of 76.6%. The current study proposes a novel method, based on classification trees applied to self-reported factors and health information assessed by a standardized questionnaire during ophthalmological visits, to identify ophthalmic patients at higher risk of falling in the following 12 months. The findings of the current study pave the way to the validation of the proposed novel tool for fall risk screening on a larger cohort of patients with visual impairment referred to eye clinics.