Melillo Paolo, Orrico Ada, Chirico Franco, Pecchia Leandro, Rossi Settimio, Testa Francesco, Simonelli Francesca
Eye Clinic, Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania Luigi Vanvitelli, Naples, Italy.
School of Engineering, University of Warwick, Coventry, United Kingdom.
PLoS One. 2017 Mar 23;12(3):e0174083. doi: 10.1371/journal.pone.0174083. eCollection 2017.
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.
开发并验证一种工具，旨在帮助眼科医生在常规眼科检查期间识别下一年跌倒风险较高的患者。
在我们的眼科诊所招募了141名受试者（年龄：73.2±11.4岁），他们接受了基线眼科检查和标准化问卷调查，包括生活方式、总体健康状况、社交参与度和视力问题。此外，通过日常视觉活动量表（ADVS）评估视觉残疾情况。对受试者进行了12个月的随访，以记录前瞻性跌倒情况。在基线评估后一年内报告至少一次跌倒的受试者被视为跌倒者，否则视为非跌倒者。使用不同的基于树的算法（即C4.5、AdaBoost和随机森林）开发自动分类器，并通过交叉验证方法评估其性能。
在随访期间，13名患者报告了25次跌倒。逻辑回归分析显示以下变量是前瞻性跌倒的重要预测因素：人工晶状体和使用处方眼镜作为保护因素，近期视力恶化作为风险因素。随机森林将最佳矫正视力、睡眠时间和工作类型列为最重要的特征。最后，AdaBoost能够识别出在接下来12个月内跌倒风险较高的受试者，灵敏度为69.2%，特异度为76.6%。
本研究提出了一种新方法，基于应用于自我报告因素和眼科检查期间通过标准化问卷评估的健康信息的分类树，以识别在接下来12个月内跌倒风险较高的眼科患者。本研究结果为在转诊至眼科诊所的更大规模视力障碍患者队列中验证所提出的新型跌倒风险筛查工具铺平了道路。
