Highlights
- •Predicting prolonged length of hospital stay of older patients is a challenge.
- •No study has use artificial neural networks to predict prolonged length of hospital stay.
- •The brief geriatric assessment plus artificial neural networks predicted length of hospital stay.
Abstract
Objective
To examine performance criteria (i.e., sensitivity, specificity, positive predictive
value [PPV], negative predictive value [NPV], likelihood ratios [LR], area under receiver
operating characteristic curve [AUROC]) of a 10-item brief geriatric assessment (BGA)
for the prediction of prolonged length hospital stay (LHS) in older patients hospitalized
in acute care wards after an emergency department (ED) visit, using artificial neural
networks (ANNs); and to describe the contribution of each BGA item to the predictive
accuracy using the AUROC value.
Methods
A total of 993 geriatric ED users admitted to acute care wards were included in this
prospective cohort study. Age >85 years, gender male, polypharmacy, non use of formal and/or informal home-help services,
history of falls, temporal disorientation, place of living, reasons and nature for
ED admission, and use of psychoactive drugs composed the 10 items of BGA and were
recorded at the ED admission. The prolonged LHS was defined as the top third of LHS.
The ANNs were conducted using two feeds forward (multilayer perceptron [MLP] and modified
MLP).
Results
The best performance was reported with the modified MLP involving the 10 items (sensitivity = 62.7%; specificity = 96.6%; PPV = 87.1; NPV = 87.5; positive LR = 18.2; AUC = 90.5). In this model, presence of chronic conditions had the highest contributions
(51.3%) in AUROC value.
Conclusions
The 10-item BGA appears to accurately predict prolonged LHS, using the ANN MLP method,
showing the best criteria performance ever reported until now. Presence of chronic
conditions was the main contributor for the predictive accuracy.
Abbreviations:
ANNS (artificial neural networks), AUROC (area under receiver operating characteristic curve), BGA (brief geriatric assessment), ED (emergency department), LHS (length hospital stay), LR (likelihood ratios), MLP (multilayer perceptron), NPV (negative predictive value), PPV (positive predictive value)Keywords
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Article info
Publication history
Published online: June 30, 2015
Accepted:
June 2,
2015
Received in revised form:
May 26,
2015
Received:
March 11,
2015
Identification
Copyright
© 2015 European Federation of Internal Medicine. Published by Elsevier Inc. All rights reserved.
