A pilot study for development of a novel tool for clinical decision making to identify fallers among ophthalmic patients

Abstract

BackgroundFalls in the elderly is a major problem. Although falls have a multifactorial etiology, a commonly cited cause of falls in older people is poor vision. This study proposes a method to discriminate fallers and non-fallers among ophthalmic patients, based on data-mining algorithms applied to health and socio-demographic information.MethodsA group of 150 subjects aged 55 years and older, recruited at the Eye Clinic of the Second University of Naples, underwent a baseline ophthalmic examination and a standardized questionnaire, including lifestyles, general health, social engagement and eyesight problems. A subject who reported at least one fall within one year was considered as faller, otherwise as non-faller. Different tree-based data-mining algorithms (i.e., C4.5, Adaboost and Random Forest) were used to develop automatic classifiers and their performances were evaluated by assessing the receiver-operator characteristics curve estimated with the 10-fold-crossvalidation approach.ResultsThe best predictive model, based on Random Forest, enabled to identify fallers with a sensitivity and specificity rate of 72.6% and 77.9%, respectively. The most informative variables were: intraocular pressure, best corrected visual acuity and the answers to the total difficulty score of the Activities of Daily Vision Scale (a questionnaire for the measurement of visual disability).ConclusionsThe current study confirmed that some ophthalmic features (i.e. cataract surgery, lower intraocular pressure values) could be associated with a lower fall risk among visually impaired subjects. Finally, automatic analysis of a combination of visual function parameters (either self-evaluated either by ophthalmological tests) and other health information, by data-mining algorithms, could be a feasible tool for identifying fallers among ophthalmic patients.

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

“…Table 3 showed the comparison of the performances of the proposed algorithm for fall risk assessment and several functional tests for fall prediction, confirming that the system achieved satisfactory performances. The system will be also integrated with a novel method to identify ophthalmological patients at higher risk of falls, which was described elsewhere [42]. Moreover, the system appeared to be well accepted by almost all the patients (95 %) with a limited amount of data lost (<20 %), thus showing that the electrocardiographic data could be reliably acquired by the developed system.…”

Identifying fallers among ophthalmic patients using classification tree methodology

“…Two hundred subjects were enrolled into the first part of the study, which was to identify retrospective fallers and which was reported previously [20]. Those 200 subjects were invited to complete a follow-up prospective study, which is the currently reported study.…”

“…Socio-demographic and medical data were recorded with a standardized questionnaire that was developed ad hoc and validated on retrospective data as detailed in our previous study [20]. Briefly, we selected variables, which have been investigated as potential risk factors for falls in previous studies, including but not limited to those on visual impaired subjects [4, 11, 16, 24–31].…”

“…Conversely, as recently concluded in a literature review [19], understanding what components of vision are most likely to be responsible for increased fall risk, as well as other factors that can cause poor vision (e.g., incorrect prescription of lenses, not wearing glasses when needed, the inability to afford lenses, health issues such as diabetes, polypharmacy) need to be evaluated. For that reason, in our previous study, we developed a standardized questionnaire to collect information about the most relevant potential risk factors (about 80 variables) for falls, including lifestyles, general health, social engagement and eyesight problems, and data were collected from 150 ophthalmic patients [20]. Piloting this questionnaire in a retrospective study, we showed that tree-based classifiers enable the identification of patients with a previous history of falls with satisfactory sensitivity and specificity rates (>70% and >80%, respectively)[20].…”

“…For that reason, in our previous study, we developed a standardized questionnaire to collect information about the most relevant potential risk factors (about 80 variables) for falls, including lifestyles, general health, social engagement and eyesight problems, and data were collected from 150 ophthalmic patients [20]. Piloting this questionnaire in a retrospective study, we showed that tree-based classifiers enable the identification of patients with a previous history of falls with satisfactory sensitivity and specificity rates (>70% and >80%, respectively)[20]. These findings motivated the analysis of prospective 1-year follow-up data to assess the subjects who undergo at least one fall after the baseline assessment, as described in this manuscript.…”