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?

Détails

ID Serval
serval:BIB_94DF1FDB180A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
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?
Périodique
The journal of nutrition, health & aging
Auteur(s)
Beauchet O., Noublanche F., Simon R., Sekhon H., Chabot J., Levinoff E.J., Kabeshova A., Launay C.P.
ISSN
1760-4788 (Electronic)
ISSN-L
1279-7707
Statut éditorial
Publié
Date de publication
2018
Peer-reviewed
Oui
Volume
22
Numéro
1
Pages
131-137
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
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.

Mots-clé
80 and over, Accidental fall, aged, artificial neural network, prediction
Pubmed
Web of science
Création de la notice
02/04/2018 16:08
Dernière modification de la notice
17/04/2018 6:15
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