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

Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?

BACKGROUND: Identification of the risk of recurrent falls is complex in older adults. The aim of this study was to examine the efficiency of 3 artificial neural networks (ANNs: multilayer perceptron [MLP], modified MLP, and neuroevolution of augmenting topologies [NEAT]) for the classification of recurrent fallers and nonrecurrent fallers using a set of clinical characteristics corresponding to risk factors of falls measured among community-dwelling older adults. METHODS: Based on a cross-sectional design, 3289 community-dwelling volunteers aged 65 and older were recruited. Age, gender, body mass index (BMI), number of drugs daily taken, use of psychoactive drugs, diphosphonate, calcium, vitamin D supplements and walking aid, fear of falling, distance vision score, Timed Up and Go (TUG) score, lower-limb proprioception, handgrip strength, depressive symptoms, cognitive disorders, and history of falls were recorded. Participants were separated into 2 groups based on the number of falls that occurred over the past year: 0 or 1 fall and 2 or more falls. In addition, total population was separated into training and testing subgroups for ANN analysis. RESULTS: Among 3289 participants, 18.9% (n = 622) were recurrent fallers. NEAT, using 15 clinical characteristics (ie, use of walking aid, fear of falling, use of calcium, depression, use of vitamin D supplements, female, cognitive disorders, BMI/m(2), number of drugs daily taken \u3e4, vision score9 seconds, handgrip strength score ≤29 (N), and age ≥75 years), showed the best efficiency for identification of recurrent fallers, sensitivity (80.42%), specificity (92.54%), positive predictive value (84.38), negative predictive value (90.34), accuracy (88.39), and Cohen κ (0.74), compared with MLP and modified MLP. CONCLUSIONS: NEAT, using a set of 15 clinical characteristics, was an efficient ANN for the identification of recurrent fallers in older community-dwellers

Risk of unplanned emergency department readmission after an acute-care hospital discharge among geriatric inpatients: results from the geriatric EDEN cohort study

International audienceThe study aims 1) to examine whether items of the brief geriatric assessment (BGA) or their combinations predicted the risk of unplanned emergency department readmission after an acute care hospital discharge among geriatric inpatients, and 2) to determine whether BGA could be used as a prognostic tool for unplanned emergency department readmission

Falls Risk Prediction for Older Inpatients in Acute Care Medical Wards: Is There an Interest to Combine an Early Nurse Assessment and the Artificial Neural Network Analysis?

Identification of the risk of falls is important among older inpatients. This study aims to examine performance criteria (i.e.; sensitivity, specificity, positive predictive value, negative predictive value and accuracy) for fall prediction resulting from a nurse assessment and an artificial neural networks (ANNs) analysis in older inpatients hospitalized in acute care medical wards. A total of 848 older inpatients (mean age, 83.0±7.2 years; 41.8% female) admitted to acute care medical wards in Angers University hospital (France) were included in this study using an observational prospective cohort design. Within 24 hours after admission of older inpatients, nurses performed a bedside clinical assessment. Participants were separated into non-fallers and fallers (i.e.; ≥1 fall during hospitalization stay). The analysis was conducted using three feed forward ANNs (multilayer perceptron [MLP], averaged neural network, and neuroevolution of augmenting topologies [NEAT]). Seventy-three (8.6%) participants fell at least once during their hospital stay. ANNs showed a high specificity, regardless of which ANN was used, and the highest value reported was with MLP (99.8%). In contrast, sensitivity was lower, with values ranging between 98.4 to 14.8%. MLP had the highest accuracy (99.7). Performance criteria for fall prediction resulting from a bedside nursing assessment and an ANNs analysis was associated with a high specificity but a low sensitivity, suggesting that this combined approach should be used more as a diagnostic test than a screening test when considering older inpatients in acute care medical ward