A risk model for the prediction of recurrent falls in community-dwelling elderly

Stalenhoef, Paul A.; Diederiks, J.P.M.; Knottnerus, J. A.; Kester, A.D.M.; Crebolder, H. F. J. M.

doi:10.1016/s0895-4356(02)00502-4

Cited by 428 publications

(350 citation statements)

References 34 publications

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“…The 10 variables in bold are the most important factors got from both connections weights approach and sensitivity analysis, including 'Total BMD value', 'Self-assessed health comparison with 1 year ago', 'Self-assessed health-Current', 'history of fall at home', 'Height', 'BMI', 'Hypertension', 'MMSE score', 'fecal incontinence', and 'Education', of which, 7 variables are the same factors with our previous study (Lan et al, 2010;Tseng et al, 2013), and other important variables, such as health status, are key factors causing falls for elders from a previous research (Stalenhoef et al, 2002). …”

Section: Results Of Sensitivity Analysis and Connection Weights Approachmentioning

confidence: 63%

Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

Liu

Cui

Chou

et al. 2015

Biomedical Signal Processing and Control

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Hip bone fracture is one of the most important causes of morbidity and mortality in the elder adults. It is necessary to establish a prediction model to provide suggestions for elders. A total of 725 subjects were involved, including 228 patients with first low-trauma hip fracture and 497 ages-, sex-, and living area-matched controls (215 from the same hospital and 282 from community). All the subjects were interviewed with the same questionnaire, and the answers of the interviewees were recorded to be the database. Three-layer back-propagation Artificial Neural Networks (ANN) models were applied for females and males separately in this study to predict the risk of hip bone fracture for elders. Furthermore, to improve the accuracies and the generalizations of the models, the ensemble ANNs method was applied. To understand variables contributions and find the important variables for predicting hip fracture, sensitivity analysis and connection weights approach were applied. In this study, three ANNs prediction models were tested with different architectures. With the fivefold crossvalidation method evaluating the performances, one of the three models turned out to be the best prediction model and achieved a big success of prediction. The best area under the receiver operating characteristic (ROC) curve and the accuracy of the prediction model are 0.91 ± 0.028 (mean ± SD) and 0.85 ± 0.029 for females, while for males are 0.99 ± 0.015 and 0.93 ± 0.020. With the method of sensitivity analysis and connection weights, input variables were ranked according to contributions/importance, and the top 10 variables show great proportion of contribution to predict hip fracture. The top 10 important variables causing hip fracture for both females and males are similar to our previous results got from logistic regression model and other related researches. In conclusion, ANNs has successfully been to establish prediction models for predicting the risk of hip bone fracture for both female and male elder adults respectively and identified the top 10 important variables from 74 input variables to predict hip bone fracture of elders. This study verified the performance of ANNs to be a highly complex prediction model. Sept 2014Dear Prof. R. Allen, I use the electronic version to send this manuscript to you. The manuscript title is: "Ensemble back-propagation neural networks for predicting the risk of hip bone fracture for elders in Taiwan". We are submitting this material for possible publication in "Biomedical Signal Processing and Control". This material has not been submitted for publication or published elsewhere in whole or part. We believe this manuscript represents an original and significant contribution to the field of "Neural networks for predicting the risk of hip bone fracture" and therefore would like to be considered for publication in "Original Articles". AbstractHip bone fracture is one of the most important causes of morbidity and mortality in the elder adults. It is necessary to establish a prediction mod...

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Section: Results Of Sensitivity Analysis and Connection Weights Approachmentioning

confidence: 63%

Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

Liu

Cui

Chou

et al. 2015

Biomedical Signal Processing and Control

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“…The most important of these appears to be a history of falling and specific chronic diseases, including osteoarthritis, impaired mobility and balance and muscle weakness [5][6][7][8][16][17][18][19]. Based on these predictors, several investigators have made efforts to construct risk profiles to identify community-dwelling elderly with a high risk of falling [5][6][7][8][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

A risk profile for identifying community-dwelling elderly with a high risk of recurrent falling: results of a 3-year prospective study

Smit²,

et al. 2006

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Introduction: The aim of the prospective study reported here was to develop a risk profile that can be used to identify community-dwelling elderly at a high risk of recurrent falling. Materials and methods: The study was designed as a 3-year prospective cohort study. A total of 1365 community-dwelling persons, aged 65 years and older, of the population-based Longitudinal Aging Study Amsterdam participated in the study. During an interview in 1995/1996, physical, cognitive, emotional and social aspects of functioning were assessed. A follow-up on the number of falls and fractures was conducted during a 3-year period using fall calendars that participants filled out weekly. Recurrent fallers were identified as those who fell at least twice within a 6-month period during the 3-year follow-up. Results: The incidence of recurrent falls at the 3-year follow-up point was 24.9% in women and 24.4% in men. Of the respondents, 5.5% reported a total of 87 fractures that resulted from a fall, including 20 hip fractures, 21 wrist fractures and seven humerus fractures. Recurrent fallers were more prone to have a fall-related fracture than those who were not defined as recurrent fallers (11.9% vs. 3.4%; OR: 3.8; 95% CI: 2.3-6.1). Backward logistic regression analysis identified the following predictors in the risk profile for recurrent falling: two or more previous falls, dizziness, functional limitations, weak grip strength, low body weight, fear of falling, the presence of dogs/cats in the household, a high educational level, drinking 18 or more alcoholic consumptions per week and two interaction terms (high education×18 or more alcohol consumptions per week and two or more previous falls × fear of falling) (AUC=0.71). Discussion: At a cut-off point of 5 on the total risk score (range 0-30), the model predicted recurrent falling with a sensitivity of 59% and a specificity of 71%. At a cut-off point of 10, the sensitivity and specificity were 31% and 92%, respectively. A risk profile including nine predictors that can easily be assessed seems to be a useful tool for the identification of community-dwelling elderly with a high risk of recurrent falling.

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“…Previous reports were screened to extract factors that have been associated with the occurrence of significant findings on CT brain scanning in patients with a history of suspected head trauma [5][6][7][8]. A total of 12 potential factors were identified: falls prior to the index fall; atrial fibrillation; signs of head, face or scalp trauma; new focal neurological signs; taking warfarin; international normalised ratio (INR) above therapeutic range, antecedent dementia, Glasgow coma score (GCS) ,14, history of alcohol abuse, vomiting, headache and previous stroke.…”

Section: Methodsmentioning

confidence: 99%

Development of a risk score to guide brain imaging in older patients admitted with falls and confusion

Brown¹,

Witham²,

George³

2011

BJR

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Objectives: CT scanning of the brain is commonly performed in older people admitted to hospital with a fall, but the yield of positive findings is low. We used audit data to develop a risk-stratification score to guide more efficient use of CT scanning. Methods: 12 potential predictors of positive CT findings were derived from a literature review. Case notes of consecutive patients presenting with falls and confusion who had undergone brain imaging were reviewed as part of an ongoing audit. Correlation of each factor with positive CT findings was undertaken and a final risk score was developed. Receiver-operating characteristic analysis was undertaken, an optimum cut-off identified, and positive and negative predictive values were calculated. Results: 66 patients with a mean age of 74.8 years were included. 13 of the 66 (20%) brain imaging studies revealed a new pathology. Previous history of falls, atrial fibrillation, head or face trauma, focal neurological signs, warfarin use and a Glasgow coma score of ,14 were significant univariate positive predictors. Antecedent dementia was included as a negative predictor. The final weighted score (range -1 to 8 points) gave an area under the curve of 0.83 (95% confidence interval 0.70 to 0.96, p,0.001). When using a cut-off of 3 points, sensitivity for significant new pathology on brain imaging was 83%, specificity was 89%, positive predictive value was 63% and negative predictive value was 96%. Conclusion: A simple weighted risk score may be able to guide the need for brain imaging in older people presenting to hospital with falls. The score requires validation in a larger, prospectively collected cohort. Patients who fall and present to medical admissions units with confusion pose a diagnostic problem because of their inability to give a coherent history. A pre-existing diagnosis of dementia often further hampers the clinical assessment; because of these reasons, older people with falls and confusion often under go CT of the head. The diagnostic yield of these investigations is often low. A study of 294 patients with acute confusion found a diagnostic yield of only 14% if clinical suspicion was the sole reason for referral [1]. A large number of these investigations could therefore, in theory, be avoided, enabling better use of resources, reducing healthcare costs and minimising patient exposure to unnecessary radiation. To better target the use of brain imaging in this patient group, a risk scoring system is required. Such scoring systems have been developed for use in general [2] and paediatric patients [3] presenting with head trauma, but old age is usually included as part of the indication for scanning. As such, existing risk scoring systems may lack discrimination when used in older patients. Pre-existing falls risk scoring systems that are widely used, such as the Tinetti score [4], are mainly designed to calculate risk of falls rather than to help preselect patients that might have significant intracranial pathology as a cause (or result) of their fall.We the...

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A risk model for the prediction of recurrent falls in community-dwelling elderly

Cited by 428 publications

References 34 publications

Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

Ensemble artificial neural networks applied to predict the key risk factors of hip bone fracture for elders

A risk profile for identifying community-dwelling elderly with a high risk of recurrent falling: results of a 3-year prospective study

Development of a risk score to guide brain imaging in older patients admitted with falls and confusion

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