Predicting prolonged length of hospital stay in older emergency department users: Use of a novel analysis method, the Artificial Neural Network

  • C.P. Launay
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • H. Rivière
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • A. Kabeshova
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • O. Beauchet
    Corresponding author at: Department of Neuroscience, Division of Geriatric Medicine, Angers University Hospital, 49933 Angers cedex 9, France. Tel.: +33 2 41 35 45 27; fax: +33 2 41 35 48 94.
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France

    Department of Medicine, Division of Geriatrics, Jewish General Hospital, McGill University, Montreal, Canada

    Biomathics, Paris, France
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      • 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.



      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.


      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).


      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.


      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.


      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)


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