A modified Elixhauser score for predicting in-hospital mortality in internal medicine admissions

Published:February 08, 2017DOI:


      • Multimorbid older adults are frequently admitted.
      • Data on in-hospital mortality are heterogeneous.
      • A specific score for internal medicine wars could better classify cases.
      • Evaluation by a score could help in the identification of high risk patients.
      • A score could help communication between physicians and patients' family.



      In-hospital mortality (IHM) is an indicator of the quality of care provided. The two most widely used scores for predicting IHM by International Classification of Diseases (ICD) codes are the Elixhauser (EI) and the Charlson Comorbidity indexes. Our aim was to obtain new measures based on internal medicine ICD codes for the original EI, to detect risk for IHM.

      Material and methods

      This single-center retrospective study included hospital admissions for any cause in the department of internal medicine between January 1, 2000, and December 31, 2013, recorded in the hospital database. The EI was calculated for evaluation of comorbidity, then we added age, gender and diagnosis of ischemic heart disease. IHM was our outcome. Only predictors positively associated with IHM were taken into consideration and the Sullivan's method was applied in order to modify the parameter estimates of the regression model into an index.


      We analyzed 75,586 admissions (53.4% females) and mean age was 72.7 ± 16.3 years. IHM was 7.9% and mean score was 12.1 ± 7.6. The points assigned to each condition ranged from 0 to 16, and the possible range of the score varied between 0 and 89. In our population the score ranged from 0 to 54, and it was higher in the deceased group. Receiver operating characteristic curve of the new score was 0.721 (95% CI 0.714–0.727, p < 0.001).


      In order to make prognostic assessment, the use of a score could be of help in targeting interventions in older adults, identifying subjects at high risk for IHM.


      ADL (activities of daily living), CGA (comprehensive geriatric assessment), CI (confidence interval), CIRS (Cumulative Illness Rating Scale), EI (Elixhauser index), ED (emergency department), HDR (hospital discharge records), HIV (Human Immunodeficiency Virus), IHM (in-hospital mortality), ICD-9-CM (International Classification of Diseases, 9th Revision, Clinical Modification), IHD (ischemic heart disease), MPI (Multidimensional Prognostic Index), NEWS (National Early Warning Score), PCPs (primary care physicians), ROC (receiver operating characteristic), SD (Standard deviation)


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        • Goodacre S.
        • Campbell M.
        • Carter A.
        What do hospital mortality rates tell us about quality of care?.
        Emerg Med J. 2015; 32: 244-247
        • Wolff J.L.
        • Starfield B.
        • Anderson G.
        Prevalence, expenditures, and complications of multiple chronic conditions in the elderly.
        Arch Intern Med. 2002; 162: 2269-2276
        • Fabbian F.
        • De Giorgi A.
        • Manfredini F.
        • Lamberti N.
        • Forcellini S.
        • Storari A.
        • et al.
        Impact of comorbidity on outcome in kidney transplant recipients: a retrospective study in Italy.
        Intern Emerg Med. 2016; 11: 825-832
        • Fabbian F.
        • De Giorgi A.
        • Manfredini F.
        • Lamberti N.
        • Forcellini S.
        • Storari A.
        • et al.
        Impact of renal dysfunction on in-hospital mortality of patients with severe chronic obstructive pulmonary disease: a single-center Italian study.
        Int Urol Nephrol. 2016; 48: 1121-1127
        • Fabbian F.
        • Gallerani M.
        • Pala M.
        • De Giorgi A.
        • Salmi R.
        • Dentali F.
        • et al.
        Association between in-hospital mortality and renal dysfunction in 186,219 patients hospitalized for acute stroke in the Emilia-Romagna region of Italy.
        Angiology. 2014; 65: 906-910
        • Fabbian F.
        • Gallerani M.
        • Pala M.
        • De Giorgi A.
        • Salmi R.
        • Manfredini F.
        • et al.
        In-hospital mortality for pulmonary embolism: relationship with chronic kidney disease and end-stage renal disease. The hospital admission and discharge database of the Emilia Romagna region of Italy.
        Intern Emerg Med. 2013; 8: 735-740
        • Fabbian F.
        • Pala M.
        • De Giorgi A.
        • Manfredini F.
        • Mallozzi Menegatti A.
        • Salmi R.
        • et al.
        In-hospital mortality in patients with renal dysfunction admitted for myocardial infarction: the Emilia-Romagna region of Italy database of hospital admissions.
        Int Urol Nephrol. 2013; 45: 769-775
        • Valderas J.M.
        • Starfield B.
        • Sibbald B.
        • Salisbury C.
        • Roland M.
        Defining comorbidity: implications for understanding health and health services.
        Ann Fam Med. 2009; 7: 357-363
        • Elixhauser A.
        • Steiner C.
        • Harris D.R.
        • Coffey R.M.
        Comorbidity measures for use with administrative data.
        Med Care. 1998; 36: 8-27
        • Charlson M.E.
        • Pompei P.
        • Ales K.L.
        • MacKenzie C.R.
        A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
        J Chronic Dis. 1987; 40: 373-383
        • Sharabiani M.T.
        • Aylin P.
        • Bottle A.
        Systematic review of comorbidity indices for administrative data.
        Med Care. 2012; 50: 1109-1118
        • van Walraven C.
        • Austin P.C.
        • Jennings A.
        • Quan H.
        • Forster A.J.
        A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.
        Med Care. 2009; 47: 626-633
        • Quan H.
        • Sundararajan V.
        • Halfon P.
        • et al.
        Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.
        Med Care. 2005; 43: 1130-1139
        • Diederichs C.P.
        • Wellmann J.
        • Bartels D.B.
        • Ellert U.
        • Hoffmann W.
        • Berger K.
        How to weight chronic diseases in multimorbidity indices? Development of a new method on the basis of individual data from five population-based studies.
        J Clin Epidemiol. 2012; 65: 679-685
        • Fabbian F.
        • Boccafogli A.
        • De Giorgi A.
        • Pala M.
        • Salmi R.
        • Melandri R.
        • et al.
        The crucial factor of hospital readmissions: a retrospective cohort study of patients evaluated in the emergency department and admitted to the department of medicine of a general hospital in Italy.
        Eur J Med Res. 2015; 20: 6
        • Sullivan L.M.
        • Massaro J.M.
        • D'Agostino Sr., R.B.
        Presentation of multivariate data for clinical use: the Framingham Study risk score functions.
        Stat Med. 2004; 23: 1631-1660
        • Hosmer D.W.
        • Hosmer T.
        • Le Cessie S.
        • Lemeshow S.
        A comparison of goodness-of-fit tests for the logistic regression model.
        Stat Med. 1997; 16: 965-980
        • Yurkovich M.
        • Avina-Zubieta J.A.
        • Thomas J.
        • Gorenchtein M.
        • Lacaille D.
        A systematic review identifies valid comorbidity indices derived from administrative health data.
        J Clin Epidemiol. 2015; 68: 3-14
        • Yancik R.
        • Ershler W.
        • Satariano W.
        • Hazzard W.
        • Cohen H.J.
        • Ferrucci L.
        Report of the National Institute on Aging Task Force on Comorbidity.
        J Gerontol A Biol Sci Med Sci. 2007; 62: 275-280
        • Diederichs C.
        • Berger K.
        • Bartels D.B.
        The measurement of multiple chronic diseases: a systematic review on existing multimorbidity indices.
        J Gerontol A Biol Sci Med Sci. 2011; 66: 301-311
        • Buck H.G.
        • Meghani S.
        • Bettger J.P.
        • Byun E.
        • Fachko M.J.
        • O'Connor M.
        • et al.
        The use of comorbidities among adults experiencing care transitions: a systematic review and evolutionary analysis of empirical literature.
        Chronic Illn. 2012; 8: 278-295
        • Barnett K.
        • Mercer S.W.
        • Norbury M.
        • Watt G.
        • Wyke S.
        • Guthrie B.
        Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.
        Lancet. 2012; 380: 37-43
        • Walter L.C.
        • Brand R.J.
        • Counsell S.R.
        • Palmer R.M.
        • Landefeld C.S.
        • Fortinsky R.H.
        • et al.
        Development and validation of a prognostic index for 1-year mortality in older adults after hospitalization.
        JAMA. 2001; 285: 2987-2994
        • Cei M.
        • Mumoli N.
        • Vitale J.
        • Dentali F.
        A prognostic index for 1-year mortality can also predict in-hospital mortality of elderly medical patients.
        Intern Emerg Med. 2015; 10: 575-579
        • de Gelder J.
        • Lucke J.A.
        • Heim N.
        • de Craen A.J.
        • Lourens S.D.
        • Steyerberg E.W.
        • et al.
        Predicting mortality in acutely hospitalized older patients: a retrospective cohort study.
        Intern Emerg Med. 2016; 11: 587-594
        • Escobar G.J.
        • Greene J.D.
        • Scheirer P.
        • Gardner M.N.
        • Draper D.
        • Kipnis P.
        Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases.
        Med Care. 2008; 46: 232-239
        • Abbott T.E.
        • Torrance H.D.
        • Cron N.
        • Vaid N.
        • Emmanuel J.
        A single-centre cohort study of National Early Warning Score (NEWS) and near patient testing in acute medical admissions.
        Eur J Intern Med. 2016; ([Epub ahead of print])
        • Bernabeu-Wittel M.
        • Moreno-Gaviño L.
        • Ollero-Baturone M.
        • Barón-Franco B.
        • Díez-Manglano J.
        • Rivas-Cobas C.
        • et al.
        Validation of PROFUND prognostic index over a four-year follow-up period.
        Eur J Intern Med. 2016; ([Epub ahead of print])
        • Di Bari M.
        • Balzi D.
        • Roberts A.T.
        • Barchielli A.
        • Fumagalli S.
        • Ungar A.
        • et al.
        Prognostic stratification of older persons based on simple administrative data: development and validation of the “silver code,” to be used in emergency department triage.
        J Gerontol A Biol Sci Med Sci. 2010; 65: 159-164
        • Mazzaglia G.
        • Roti L.
        • Corsini G.
        • Colombini A.
        • Maciocco G.
        • Marchionni N.
        • et al.
        Screening of older community-dwelling people at risk for death and hospitalization: the Assistenza Socio-Sanitaria in Italia project.
        J Am Geriatr Soc. 2007; 55: 1955-1960
        • Pilotto A.
        • Ferrucci L.
        • Franceschi M.
        • D'Ambrosio L.P.
        • Scarcelli C.
        • Cascavilla L.
        • et al.
        Development and validation of a multidimensional prognostic index for one-year mortality from comprehensive geriatric assessment in hospitalized older patients.
        Rejuvenation Res. 2008; 11: 151-161
        • Sancarlo D.
        • D'Onofrio G.
        • Franceschi M.
        • Scarcelli C.
        • Niro V.
        • Addante F.
        • et al.
        Validation of a Modified-Multidimensional Prognostic Index (m-MPI) including the Mini Nutritional Assessment Short-Form (MNA-SF) for the prediction of one-year mortality in hospitalized elderly patients.
        J Nutr Health Aging. 2011; 15: 169-173
        • Rozzini R.
        • Sabatini T.
        • Trabucchi M.
        Prediction of 6-month mortality among older hospitalized adults.
        JAMA. 2001; 286: 1315-1316
        • Thompson N.R.
        • Fan Y.
        • Dalton J.E.
        • Jehi L.
        • Rosenbaum B.P.
        • Vadera S.
        • et al.
        A new Elixhauser-based comorbidity summary measure to predict in-hospital mortality.
        Med Care. 2015; 53: 374-379
        • Justice A.C.
        • Covinsky K.E.
        • Berlin J.A.
        Assessing the generalizability of prognostic information.
        Ann Intern Med. 1999; 130: 515-524
        • Hsia D.C.
        • Krushat W.M.
        • Fagan A.B.
        • Tebbutt J.A.
        • Kusserow R.P.
        Accuracy of diagnostic coding for Medicare patients under the prospective-payment system.
        N Engl J Med. 1988; 318: 352-355
        • Mazzali C.
        • Duca P.
        Use of administrative data in healthcare research.
        Intern Emerg Med. 2015; 10: 517-524