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

  • C.P. Launay
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • H. Rivière
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • A. Kabeshova
    Affiliations
    Department of Neuroscience, Division of Geriatric Medicine, UPRES EA 4638, UNAM, Angers University Hospital, Angers, France
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  • O. Beauchet
    Correspondence
    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.
    Affiliations
    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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      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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      References

        • Aminzadeh F.
        • Dalziel W.B.
        Older adults in the emergency department: a systematic review of patterns of use, adverse outcomes, and effectiveness of interventions.
        Ann Emerg Med. 2002; 393: 238-247
        • Xu K.T.
        • Nelson B.K.
        • Berk S.
        The changing profile of patients who used emergency department services in the United States: 1996 to 2005.
        Ann Emerg Med. 2009; 546: 805-810
        • McCusker J.
        • Verdon J.
        • Tousignant P.
        • de Courval L.P.
        • Dendukuri N.
        • Belzile E.
        Rapid emergency department intervention for older people reduces risk of functional decline: results of a multicenter randomized trial.
        J Am Geriatr Soc. 2001; 49: 1272-1281
        • Hoogerduijn J.G.
        • Buurman B.M.
        • Korevaar J.C.
        • Grobbee D.E.
        • de Rooij S.E.
        • Schuurmans M.J.
        The prediction of functional decline in older hospitalised patients.
        Age Ageing. 2012; 41: 381-387
        • Launay C.P.
        • de Decker L.
        • Kabeshova A.
        • Annweiler C.
        • Beauchet O.
        Screening for older emergency department inpatients at risk of prolonged hospital stay: the brief geriatric assessment tool.
        Plos One. 2014; 9 ([e110135])
        • Beauchet O.
        • Launay C.P.
        • Fantino B.
        • Lerolle N.
        • Maunoury F.
        • Annweiler C.
        Screening for elderly patients admitted to the emergency department requiring specialized geriatric care.
        J Emerg Med. 2013; 45: 739-745
        • Beauchet O.
        • Launay C.P.
        • Maunoury F.
        • de Decker L.
        • Fantino B.
        • Annweiler C.
        Association between vitamin D deficiency and long hospital stay in geriatric acute care unit: results from a pilot cohort study.
        Aging Clin Exp Res. 2013; 25: 107-109
        • Reuben D.B.
        Medical care for the final years of life: “when you're 83, it's not going to be 20 years”.
        JAMA. 2009; 302: 2686-2694
        • Reuben D.B.
        Better care for older people with chronic diseases: an emerging vision.
        JAMA. 2007; 298: 2673-2674
        • Launay C.
        • Haubois G.
        • Hureaux-Huynh R.
        • Gautier J.
        • Annweiler C.
        • Beauchet O.
        Older adults and emergency department: who is at risk of hospitalization?.
        Geriatr Psychol Neuropsychiatr Vieil. 2014; 12: 43-49
        • Baxt W.G.
        Application of artificial neural networks to clinical medicine.
        Lancet. 1995; 346: 1135-1138
        • Baxt W.G.
        • Skora J.
        Prospective validation of artificial neural network trained to identify acute myocardial infarction.
        Lancet. 1996; 347: 12-15
        • Patel J.L.
        • Goyal R.K.
        Applications of artificial neural networks in medical science.
        Curr Clin Pharmacol. 2007; 2: 217-226
        • Lisboa P.J.
        A review of evidence of health benefit from artificial neural networks in medical intervention.
        Neural Netw. 2002; 15: 11-39
        • Lang P.O.
        • Zekry D.
        • Michel J.P.
        • Drame M.
        • Novella J.L.
        • Jolly D.
        • et al.
        Early markers of prolonged hospital stay in demented inpatients: a multicentre and prospective study.
        J Nutr Health Aging. 2010; 14: 141-147
        • Moons P.
        • De Ridder K.
        • Geyskens K.
        • Sabbe M.
        • Braes T.
        • Flamaing J.
        • et al.
        Screening for risk of readmission of patients aged 65 years and above after discharge from the emergency department: predictive value of four instruments.
        Eur J Emerg Med. 2007; 14: 315-323
        • Lim S.C.
        • Doshi V.
        • Castasus B.
        • Lim J.K.
        • Mamun K.
        Factors causing delay in discharge of elderly patients in an acute care hospital.
        Ann Acad Med Singapore. 2006; 351: 27-32
        • Byrne D.G.
        • Chung S.L.
        • Bennett K.
        Age and outcome in acute emergency medical admissions.
        Age Ageing. 2010; 396: 694-698
        • Hastings S.N.
        • Whitson E.
        • Purser J.L.
        • et al.
        Emergency department discharge diagnosis and adverse health outcomes in older adults.
        J Am Geriatr Soc. 2009; 5710: 1856-1861
        • Drame M.
        • Jovenin N.
        • Novella J.L.
        • Lang P.O.
        • Somme D.
        • Laniece I.
        • et al.
        Predicting early mortality among elderly patients hospitalised in medical wards via emergency department: the SAFES cohort study.
        J Nutr Health Aging. 2008; 12: 599-604
        • Stanley K.O.
        • Miikkulainen R.
        Evolving neural networks through augmenting topologies.
        Evol Comput. 2002; 10: 99-127
      1. Fritsch S, Guenther F, Suling, following earlier work by M. neuralnet: Training of neural networks, 2012.

        • Kabeshova A.
        • Launay C.P.
        • Gromov V.A.
        • Annweiler C.
        • Fantino B.
        • Beauchet O.
        Artificial neural network and falls in community-dwellers: a new approach to identify the risk of recurrent falling?.
        J Am Med Dir Assoc. 2014; 16: 277-281
        • McCusker J.
        • Bellavance F.
        • Cardin S.
        • Trépanier S.
        • Verdon J.
        • Ardman O.
        Detection of older people at increased risk of adverse health outcomes after an emergency visit: the ISAR screening tool.
        J Am Geriatr Soc. 1999; 47: 1229-1237
        • Meldon S.W.
        • Mion L.C.
        • Palmer R.M.
        • Drew B.L.
        • Connor J.T.
        • Lewicki L.J.
        • et al.
        A brief risk-stratification tool to predict repeat emergency department visits and hospitalizations in older patients discharged from the emergency department.
        Acad Emerg Med. 2003; 10: 224-232
        • de Decker L.
        • Launay C.
        • Annweiler C.
        • Kabeshova A.
        • Beauchet O.
        Number of drug classes taken per day may be used to assess morbidity burden in older inpatients: a pilot cross-sectional study.
        J Am Geriatr Soc. 2013; 61: 1224-1225
        • Sternberg S.A.
        • Wershof Schwartz A.
        • Karunananthan S.
        • Bergman H.
        • Mark Clarfield A.
        The identification of frailty: a systematic literature review.
        J Am Geriatr Soc. 2011; 59: 2129-2138
        • Clegg A.
        • Young J.
        • Iliffe S.
        • Rikkert M.O.
        • Rockwood K.
        Frailty in elderly people.
        Lancet. 2013; 381: 752-762
        • Andrew M.K.
        • Mitnitski A.
        • Kirkland S.A.
        • Rockwood K.
        The impact of social vulnerability on the survival of the fittest older adults.
        Age Ageing. 2012; 41: 161-165
        • Sourial N.
        • Bergman H.
        • Karunananthan S.
        • Wolfson C.
        • Payette H.
        • Gutierrez-Robledo L.M.
        • et al.
        Implementing frailty into clinical practice: a cautionary tale.
        J Gerontol A Biol Sci Med Sci. 2013; 68: 1505-1511