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  1. Illness severity adjustment for outcomes analysis : Validation of the ICISS methodology in all 821,455 patients hospitalized in North Carolina in 1996. Discussion.

    Article, Communication - En anglais

    Annual Meeting of the Society of University Surgeons. Milwaukee, WI, USA, 1998/02/12.


    Previous work has demonstrated that the International Classification of Diseases 9th Revision (ICD-9) Based Illnes Severity Score (ICISS) methodology developed by Rutledge and Osler can perform well in this role as a severity adjustment tool in trauma patients.

    The purpose of the present study was to extend this previous work to determine the ability of ICISS to predict outcomes in all types of hospitalized patients.


    The ICISS methodology was used to derive predictions of survival, length of hospital stay and hospital charges in the entire study population.


    A total of 821,455 hospitalized patients in North Carolina in 1996 had complete data available for analysis.

    The overall hospital mortality rate was 2.9%. ICISS was an accurate predictor of hospital survival in all hospitalized patients (accuracy 95.9%, sensitivity 97.2%, and specificity 52.7% .) The area of the receiver operator characteristic curve was 0.93.

    By adding age to the model, the area under the receiver operator characteristic curve increased to 0.95.

    ICISS also explained a large amount of the variance in hospital stay and charges (R2=0.38 and 0.56, respectively P<. 0001).


    This study extends previous work suggesting that ICISS may be an important improvement over other presently available severity adjustment models.

    If these findings are confirmed in comparison with other predictive tools, ICISS may find an important place in assessing illness severity.

    Mots-clés Pascal : Maladie, Evaluation, Indice gravité, Echelle évaluation, Evaluation performance, Facteur prédictif, Evolution, Actualisation, Homme

    Mots-clés Pascal anglais : Disease, Evaluation, Severity score, Evaluation scale, Performance evaluation, Predictive factor, Evolution, Discounting, Human

    Logo du centre Notice produite par :
    Inist-CNRS - Institut de l'Information Scientifique et Technique

    Cote : 98-0491855

    Code Inist : 002B30A01C. Création : 19/02/1999.