Early Hospital Discharge of Older Adults Admitted to the Emergency Department: Effect of Different Types of Recommendations Made by a Mobile Geriatric Team

International audienc

Who is at risk of long hospital stay among patients admitted to geriatric acute care unit? Results from a prospective cohort study

1) To confirm that vitamin D deficiency, defined as serum 25-hydroxyvitamin D (25OHD) concentration < 25nmol/L, was associated with long length-of-stay (LOS) among older inpatients admitted to geriatric acute care unit; and 2) to examine which combination of risk factors of longer LOS including vitamin D deficiency best predicted longer LOS.Based on a prospective cohort study with a 25-day follow-up on average, 531 consecutive older inpatients (mean age 85.0 +/- 7.2 years, 59.1% women) admitted to the geriatric acute care unit of Angers University Hospital, France, were included. Linear regression models showed that male gender (P < 0.025), delirium (P < 0.015) and vitamin D deficiency (P < 0.001) were independently associated with a longer LOS. The highest risk of a longer LOS was shown while combining vitamin D deficiency with male gender (Odds ratio (OR)=3.70 with P < 0.001). The risk increased significantly while delirium was associated with these two baseline characteristics (OR=4.76 with P=0.001). Kaplan-Meier distributions of discharge differed significantly between participants who had or not the combination of the 3 criteria (P < 0.007). Vitamin D deficiency, delirium and male gender were significant risk factors for a longer LOS in the studied sample of older inpatients

Association of Depressive Symptoms with Recurrent Falls: A Cross-Sectional Elderly Population Based Study and a Systematic Review

Background: Screening of depressive symptoms is recommended in recurrent fallers. Compared to the 30-item and 15-item Geriatric Depression Scales (GDS), the 4-item GDS is easier to administer and quicker to perform. The association between abnormal 4-item GDS score and recurrent falls has not yet been examined. In addition, while depressive symptoms-related gait instability is well known, the association with recurrent falls has been few studied. Objective: 1) To examine the association between abnormal 4-item GDS score and recurrent falls in community-dwelling older adults using original data from health examination centers (HEC) of French health insurance of Lyon, and 2) to perform a systematic review of studies that examined the association of depressive symptoms with recurrent falls among older adults. Methods: Firstly, based on a cross-sectional design, 2,594 community-dwellers (mean age 72.1 +/- 5.4years; 49.8% women) were recruited in HEC of Lyon, France. The 4-item GDS score (abnormal if score >= 1) and recurrent falls (i.e., 2 or more falls in the past year) were used as main outcomes. Secondly, a systematic English and French Medline literature search was conducted on May 28, 2012 with no limit of date using the following Medical Subject Heading (MeSH) terms "Aged OR aged, 80 and over", "Accidental falls", "Depressive disorder" and "Reccurence". The search also included the reference lists of the retrieved articles. Results: A total of 19.0% (n=494) participants were recurrent fillers in the cross-sectional study. Abnormal 4-item GDS score was more prevalent among recurrent fallers compared to non-recurrent fallers (44.7% versus 25.0%, with P<0.001), and was significantly associated with recurrent falls (Odd ratio (OR)=1.82 with P<0.001 for full model; OR=1.86 with P<0.001 for stepwise backward model). In addition to the current study, the systematic review found only four other studies on this topic, three of them examining the association of depressive symptoms with recurrent falls using 30-item or 15-item GDS. All studies showed a significant association of depressive symptoms with recurrent falls. Conclusions: The current cross-sectional study shows an association between abnormal 4-item GDS score and recurrent falls. This association of depressive symptoms with recurrent falls was confirmed by the systematic review. Based on these results, we suggest that recurrent falls risk assessment should involve a systematic screening of depressive symptoms using the 4-item GDS

Mobile Geriatric Team Advice: Effect on Length of Hospital Stay in Older Adults

International audienc

Age effect on the prediction of risk of prolonged length hospital stay in older patients visiting the emergency department: results from a large prospective geriatric cohort study.

With the rapid growth of elderly patients visiting the Emergency Department (ED), it is expected that there will be even more hospitalisations following ED visits in the future. The aim of this study was to examine the age effect on the performance criteria of the 10-item brief geriatric assessment (BGA) for the prolonged length of hospital stay (LHS) using artificial neural networks (ANNs) analysis. Based on an observational prospective cohort study, 1117 older patients (i.e., aged ≥ 65 years) ED users were admitted to acute care wards in a University Hospital (France) were recruited. The 10-items of BGA were recorded during the ED visit and prior to discharge to acute care wards. The top third of LHS (i.e., ≥ 13 days) defined the prolonged LHS. Analysis was successively performed on participants categorized in 4 age groups: aged ≥ 70, ≥ 75, ≥ 80 and ≥ 85 years. Performance criteria of 10-item BGA for the prolonged LHS were sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], likelihood ratios [LR], area under receiver operating characteristic curve [AUROC]). The ANNs analysis method was conducted using the modified multilayer perceptron (MLP). Values of criteria performance were high (sensitivity> 89%, specificity≥ 96%, PPV > 87%, NPV > 96%, LR+ > 22; LR- ≤ 0.1 and AUROC> 93), regardless of the age group. Age effect on the performance criteria of the 10-item BGA for the prediction of prolonged LHS using MLP was minimal with a good balance between criteria, suggesting that this tool may be used as a screening as well as a predictive tool for prolonged LHS

Fear of falling, falls, and gait variability in older community-dwelling individuals: is there an association?

International audienc

Determining Risk of Falls in Community-Dwelling Older Adults: A Systematic Review and Meta-analysis Using Posttest Probability

BACKGROUND: Falls and their consequences are significant concerns for older adults, caregivers, and health care providers. Identification of fall risk is crucial for appropriate referral to preventive interventions. Falls are multifactorial; no single measure is an accurate diagnostic tool. There is limited information on which history question, self-report measure, or performance-based measure, or combination of measures, best predicts future falls. PURPOSE: First, to evaluate the predictive ability of history questions, self-report measures, and performance-based measures for assessing fall risk of community-dwelling older adults by calculating and comparing posttest probability (PoTP) values for individual test/measures. Second, to evaluate usefulness of cumulative PoTP for measures in combination. DATA SOURCES: To be included, a study must have used fall status as an outcome or classification variable, have a sample size of at least 30 ambulatory community-living older adults (≥65 years), and track falls occurrence for a minimum of 6 months. Studies in acute or long-term care settings, as well as those including participants with significant cognitive or neuromuscular conditions related to increased fall risk, were excluded. Searches of Medline/PubMED and Cumulative Index of Nursing and Allied Health (CINAHL) from January 1990 through September 2013 identified 2294 abstracts concerned with fall risk assessment in community-dwelling older adults. STUDY SELECTION: Because the number of prospective studies of fall risk assessment was limited, retrospective studies that classified participants (faller/nonfallers) were also included. Ninety-five full-text articles met inclusion criteria; 59 contained necessary data for calculation of PoTP. The Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS) was used to assess each study\u27s methodological quality. DATA EXTRACTION: Study design and QUADAS score determined the level of evidence. Data for calculation of sensitivity (Sn), specificity (Sp), likelihood ratios (LR), and PoTP values were available for 21 of 46 measures used as search terms. An additional 73 history questions, self-report measures, and performance-based measures were used in included articles; PoTP values could be calculated for 35. DATA SYNTHESIS: Evidence tables including PoTP values were constructed for 15 history questions, 15 self-report measures, and 26 performance-based measures. Recommendations for clinical practice were based on consensus. LIMITATIONS: Variations in study quality, procedures, and statistical analyses challenged data extraction, interpretation, and synthesis. There was insufficient data for calculation of PoTP values for 63 of 119 tests. CONCLUSIONS: No single test/measure demonstrated strong PoTP values. Five history questions, 2 self-report measures, and 5 performance-based measures may have clinical usefulness in assessing risk of falling on the basis of cumulative PoTP. Berg Balance Scale score (≤50 points), Timed Up and Go times (≥12 seconds), and 5 times sit-to-stand times (≥12) seconds are currently the most evidence-supported functional measures to determine individual risk of future falls. Shortfalls identified during review will direct researchers to address knowledge gaps

Effects of Enriched Physical Activity Environments on Balance and Fall Prevention in Older Adults: A Scoping Review

The incidence of falling, due to ageing, is related to both personal and environmental factors. There is a clear need to understand the nature of the major risk factors and design features of a safe and navigable living environment for potential fallers. The aim of this scoping review was to identify studies that have examined the effectiveness of environments which promote physical activity and have an impact on falls prevention. Selected studies were identified and categorised into four main topics: built environment; environment modifications; enriched environments, and task constraints. The results of this analysis showed that there are a limited number of studies aiming to enhance dynamic postural stability and fall prevention through designing more functional environments. This scoping review study suggests that the design of interventions and the evaluation of an environment to support fall prevention is a topic for future research

Predicting of falls in the elderly : using of non-linear of mathematical models

En 2015, la chute de la personne âgée reste toujours un événement majeur, quel que soit l’angle de vue considéré. Elle est toujours associée à une forte morbi-mortalité, nombreuses incapacités, altération la qualité de vie du chuteur, mais aussi, en raison du vieillissement de la population, avec le nombre croissant de chuteurs requérant une prise en charge médicale. Cette situation repose en bonne partie sur notre incapacité à identifier la personne âgée qui est le plus à risque de chute, cette étape étant la première de toute stratégie d’intervention efficace et efficiente. Il est donc nécessaire voir obligatoire aujourd’hui de redoubler nos efforts sur l’amélioration de la prédiction de la chute. En contrepartie de nouvelles opportunités s’ouvrent à nous en raison de l’implantation et de l’informatisation des données médicales. La chute doit être considérée comme un événement chaotique et sa prédiction doit se faire via de nouveaux modèles mathématiques intégrant la particularité de ce comportement. C’est pour cette raison que des méthodes d’analyse basée sur l'intelligence artificielle semblent être une solution appropriée. C’est à partir de ce constat que nous avons émis l’hypothèse que les modèles mathématiques issus de l’intelligence artificielle devaient permettre d’atteindre une qualité de la prédiction meilleure. L’objectif principal de cette thèse est d’étudier la qualité de la prédiction de la chute, récurrente ou non, chez des personnes âgées de 65 ans et plus, en utilisant les réseaux neuronaux et un modèle de logique floue, en les comparant avec des modèles mathématiques linéaires utilisés classiquement dans la littérature. L’ensemble de nos résultats confirme notre hypothèse de départ en montrant que le choix du modèle mathématique influence la qualité de la prédiction de la chute, les modèles non linéaires, et notamment les réseaux neuronaux et les systèmes de logique flous, étant plus performants que les modèles linéaires pour la prédiction des chutes surtout lorsqu’elles sont récurrentes.Falls in the elderly are still a major issue in 2015 because they are associated with high rate of morbidity, mortality and disability, which affect the quality of life. From the patient’s perspective, it is still associated with high morbidity, mortality and disability, which affect the quality of life. The number of fallers requiring medical and/or social care is growing up due to aging population. This fact seems paradoxical since during the recent years the knowledge about the mechanisms of falls and the quality of interventions to support fallers significantly increased. This is largely based on our inability to predict correctly the risk of falling among the elderly person, knowing that this is the first step of any efficient and effective intervention strategies. Therefore it is necessary today to double our efforts in improving the prediction of falls. Nonetheless, new opportunities and advanced technologies provide to us the possibility of computerizing of medical data and research, and also to improve prediction of falls using new approaches. A fall should be considered as a chaotic event, and its prediction should be done via new mathematical models incorporating the feature of this behaviour. Thus, the methods ofartificial intelligence-based analysis seem to be an appropriate solution to analyse complex medical data. These artificial intelligence techniques have been already used in many medical areas, but rarely in the field of fall prediction. Artificial neural networks are the most commonly used methods while other promising techniques based on fuzzy logic are less often applied.Based on this observation we have formulated the hypothesis that non-linear mathematical models using artificial intelligence are the models, which are the most likely to achieve the bestquality of the prediction. The main objective of this thesis is to study the quality of theprediction of falls, recurrent or not, among the adults aged 65 years and more,applying neuralnetworks and fuzzy logic models, and comparing them either among themselves or with the linear mathematical models conventionally employed in the literature for fall prediction. The first cross-sectional study was conducted by using a decision tree to explore the risk of recurrent falls in various combinations of fall risk factors compared to a logistic regression model. The second study was designed to examine the efficiency of artificial neural networks (Multilayer Perceptron and Neuroevolution of Augmenting Topologies) to classify recurrent and nonrecurrent fallers by using a set of clinical characteristics corresponding to risk factors measured among seniors living in the community. Finally, in the third study we compared the results of different statistical methods (linear and nonlinear) in order to identify the risk of falls using 7 clinical variables, separating the collection mode (retrospective and prospective) of the fall and its recurrence. The results confirm our hypothesis showing that the choice of the mathematical model affects the quality of fall prediction. Nonlinear models, such as neural networks and fuzzy logic systems, are more efficient than linear models for the prediction of falls especially for recurrent falls. However, the results show that the balance between different criteria used to judge the quality of the forecast (sensitivity, specificity, positive and negative predictive value, area under the curve, positive and negative likelihood ratio, and accuracy) has not been always correct, emphasizing the need to continue the development of the models whose intelligence should specifically predict the fall