ABSTRACT
BACKGROUND AND OBJECTIVE: The risk of falling increases with age. The problem of falls in medical care constantly rises in priority due to demographic changes in Germany. Therefore, the risk of falling should be assessed in all patients. The present study was intended to evaluate the frequency of falls in daily ENT outpatient medical care and the importance of risk factors. PATIENTS/METHODS: A multicentre study was performed in six outpatient facilities based on a survey of falls and fall risk factors in patients living at home (n = 673) aged 60 years and older. RESULTS: Falls are a frequent problem in the elderly. A total of 23% had one and 13.7% more than one fall within the previous year. Of these, 15% suffered injury, while only 13% used some form of fall prevention. Joint pain and problems of the hip and lower extremities (42.6%), abnormal gait (41.3%), dizziness and balance disorders (38%) as well as impaired activities of daily living (35%) are the most common risk factors for falls and multiple falls in all age groups. Impaired hearing is a fall risk factor in elderly patients. CONCLUSIONS: Falls are a frequent problem in patients in ENT outpatient medical care. Fall prevention is advisable in the context of an interdisciplinary approach.
Subject(s)
Accidental Falls/prevention & control , Accidental Falls/statistics & numerical data , Activities of Daily Living , Ambulatory Care/statistics & numerical data , Joint Diseases/epidemiology , Vertigo/epidemiology , Vestibular Diseases/epidemiology , Aged , Aged, 80 and over , Causality , Comorbidity , Female , Germany/epidemiology , Humans , Male , Middle Aged , Otolaryngology , Prevalence , Risk Assessment , Risk FactorsABSTRACT
BACKGROUND: Posturography methods have been applied in clinical neurootology to evaluate the equilibrium function of patients. Methods of statistical analysis play an important role for improving data processing and to support the interpretation of the results. In contrast to conventional statistics, artificial neural networks are model-free and non-parametric. The aim of the presented study was to investigate how accurately these methods are able to discriminate between healthy and equilibrium-disturbed subjects. PATIENTS AND METHODS: 51 healthy volunteers participated in this study. 2 static posturography measurements were recorded before and 40 min after alcohol intake (0.4-0.6). Recorded signals were processed by 4 different methods in order to estimate power spectral densities (0 Hz-25 Hz). 11 different methods of artificial neural networks were investigated. The ability of artificial neural networks for classification was evaluated in patients with an acute unilateral vestibular loss. RESULTS: It turned out that estimating power spectral densities by means of autoregressive modelling and subsequent classification by Support-Vector Machine or by Learning Vector Quantization Networks are most accurate. Validation analysis yielded mean classification errors for the test set of 4.2 ± 2.2%. CONCLUSIONS: Analysis of neurootological data by artificial neural networks proved to be a sensitive recognition method of even small changes of the postural system.
