Elixhauser outperformed Charlson comorbidity index in prognostic value after ACS: insights from a national registry

      Highlights

      • The first study to compare performance of the ECS and CCI comorbidity scores in predicting ACS outcomes.
      • ECS had superior prognostic value compared to the CCI in terms of statistical performance of models predicting all in-hospital outcomes.
      • ECS should be routinely adopted in clinical practice to inform the risk stratification and management of ACS.

      Abstract

      Objective

      To compare the performance of risk adjustment models using the Elixhauser and Charlson comorbidity scores in predicting in-hospital outcomes of ACS patients from a nationwide administrative database.

      Study Design and Setting

      All hospitalizations for ACS in the United States between 2004 and 2014 (n = 7,201,900) were retrospectively analyzed. We used ECS and CCI score based on ICD-9 codes to define comorbidity variables. Logistic regression models were fitted to three in-hospital outcomes, including mortality, Major Acute Cardiovascular & Cerebrovascular Events (MACCE) and bleeding. The prognostic values of ECS and CCI after adjusting for known confounders, were compared using the C-statistic, Akaike information criterion (AIC), and Bayesian information criterion (BIC).

      Results

      The statistical performance of models predicting all in-hospital outcomes demonstrated that the ECS had superior prognostic value compared to the CCI, with higher C-statistics and lower AIC and BIC values associated with the former.

      Conclusion

      This is the first study that compared the prognostic value of the ECS and CCI scores in predicting multiple ACS outcomes, based on their scoring systems. Better discrimination and goodness of fit was achieved with the Elixhauser method across all in-hospital outcomes studied.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic and Personal

      Subscribe:

      Subscribe to Journal of Clinical Epidemiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Virani SS
        • Alonso A
        • Benjamin EJ
        • Bittencourt MS
        • Callaway CW
        • Carson AP
        • et al.
        Heart disease and stroke statistics-2020 update: a report from the American Heart Association.
        Circulation. 2020; 141: e139-e596https://doi.org/10.1161/CIR.0000000000000757
        • Wolff JL
        • Starfield B
        • Anderson G
        Prevalence, expenditures, and complications of multiple chronic conditions in the elderly.
        Arch Intern Med. 2002; 162: 2269-2276
        • Sanchis J
        • Bonanad C
        • Ruiz V
        • Fernández J
        • García-Blas S
        • Mainar L
        • et al.
        Frailty and other geriatric conditions for risk stratification of older patients with acute coronary syndrome.
        Am Heart J. 2014; 168 (e2): 784-791
        • Graham MM
        • Galbraith PD
        • O'Neill D
        • Rolfson DB
        • Dando C
        • Norris CM
        Frailty and outcome in elderly patients with acute coronary syndrome.
        Can J Cardiol. 2013; 29: 1610-1615
        • Klykylo WM
        Comorbidity.
        in: Hersen M Sledge W Encyclopedia of psychotherapy. Academic Press, New York2002: 475-479
        • Potts J
        • Nagaraja V
        • Al Suwaidi J
        • Brugaletta S
        • Martinez SC
        • Alraies C
        • et al.
        The influence of Elixhauser comorbidity index on percutaneous coronary intervention outcomes.
        Catheterization and Cardiovascular Interventions. 2019; 94: 195-203
        • Zhang F
        • Bharadwaj A
        • Mohamed MO
        • Ensor J
        • Peat G
        • Mamas MA
        Impact of Charlson co-morbidity index score on management and outcomes after acute coronary syndrome.
        Am J Cardiol. 2020; 130: 15-23
        • Zhang F
        • MO Mohamed
        • Ensor J
        • Peat G
        • Mamas MA
        Temporal trends in comorbidity burden and impact on prognosis in patients with acute coronary syndrome using the Elixhauser comorbidity index score.
        Am J Cardiol. 2020; 125: 1603-1611https://doi.org/10.1016/j.amjcard.2020.02.044
        • Canivell S
        • Muller O
        • Gencer B
        • Heg D
        • Klingenberg R
        • Räber L
        • et al.
        Prognosis of cardiovascular and non-cardiovascular multimorbidity after acute coronary syndrome.
        PLoS One. 2018; 13e0195174
        • Chen H
        • Saczynski JS
        • McManus DD
        • Lessard D
        • Yarzebski J
        • Lapane KL
        • et al.
        The impact of cardiac and noncardiac comorbidities on the short-term outcomes of patients hospitalized with acute myocardial infarction: a population-based perspective.
        Clinical Epidemiology. 2013; 5: 439
        • Erickson SR
        • Cole E
        • Kline-Rogers E
        • Eagle KA
        The addition of the Charlson comorbidity index to the GRACE risk prediction index improves prediction of outcomes in acute coronary syndrome.
        Population Health Manage. 2014; 17: 54-59
        • Deyo RA
        • Cherkin DC
        • Ciol MA
        Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases.
        J Clin Epidemiol. 1992; 45: 613-619https://doi.org/10.1016/0895-4356(92)90133-8
        • Charlson ME
        • Pompei P
        • Ales KL
        • MacKenzie CR
        A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
        J Clin Epidemiol. 1987; 40: 373-383
        • van Walraven C
        • Austin PC
        • Jennings A
        • Quan H
        • Forster AJ
        A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.
        Med Care. 2009; : 626-633
        • Elixhauser A
        • Steiner C
        • Harris DR
        • Coffey RM
        Comorbidity measures for use with administrative data.
        Med Care. 1998; 36: 8-27https://doi.org/10.1097/00005650-199801000-00004
        • Gutacker N
        • Bloor K
        • Cookson R
        Comparing the performance of the Charlson/Deyo and Elixhauser comorbidity measures across five European countries and three conditions.
        Eur J Public Health. 2015; 25: 15-20
        • Southern DA
        • Quan H
        • Ghali WA
        Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data.
        Med Care. 2004; : 355-360
        • Chu Y
        • Ng Y
        • Wu S
        Comparison of different comorbidity measures for use with administrative data in predicting short-and long-term mortality.
        BMC Health Serv Res. 2010; 10: 1-7
        • HCUP National Inpatient Sample (NIS)
        Healthcare cost and utilization project (HCUP). 2004-2014. Agency for Healthcare Research and Quality, Rockville, MD2014
        • Azur MJ
        • Stuart EA
        • Frangakis C
        • Leaf PJ
        Multiple imputation by chained equations: what is it and how does it work?.
        Int J Methods Psychiatr Res. 2011; 20: 40-49
        • White IR
        • Royston P
        • Wood AM
        Multiple imputation using chained equations: issues and guidance for practice.
        Stat Med. 2011; 30: 377-399https://doi.org/10.1002/sim.4067
      1. Rubin DB. Multiple imputation for survey nonresponse. 1987.

        • Kontopantelis E
        • White IR
        • Sperrin M
        • Buchan I
        Outcome-sensitive multiple imputation: a simulation study.
        BMC Med Res Method. 2017; 17: 2https://doi.org/10.1186/s12874-016-0281-5
        • Zweig MH
        • Campbell G
        Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.
        Clin Chem. 1993; 39: 561-577
        • Akaike H.
        A new look at the statistical model identification.
        IEEE Trans Autom Control. 1974; 19: 716-723https://doi.org/10.1109/TAC.1974.1100705
        • Lumley T
        • Scott A
        AIC and BIC for modeling with complex survey data.
        J Survey Stat Methodol. 2015; 3: 1-18
        • Schwarz G
        Estimating the dimension of a model.
        Ann Stat. 1978; 6: 461-464
        • Bland JM
        • Peacock JL
        Interpreting statistics with confidence.
        Obstetrician Gynaecol. 2002; 4: 176-180https://doi.org/10.1576/toag.2002.4.3.176
      2. Frost J. Using confidence intervals to compare means.2019. https://statisticsbyjim.com/hypothesis-testing/confidence-intervals-compare-means/. Accessed Oct 4, 2020.

      3. Anderson D, Burnham K. AIC myths and misunderstandings. April, 2006. https://sites.warnercnr.colostate.edu/anderson/wp-content/uploads/sites/26/2016/11/AIC-Myths-and-Misunderstandings.pdf . Accessed Jan 30, 2021.

        • Kuha J
        • AIC and BIC
        AIC and BIC: Comparisons of assumptions and performance.
        Sociol Methods Res. 2004; 33: 188-229https://doi.org/10.1177/0049124103262065
        • Burnham KP
        • Anderson DR
        Multimodel inference: Understanding AIC and BIC in model selection.
        Sociol Methods Res. 2004; 33: 261-304
      4. Lumley T. Survey: Analysis of complex survey samples. R package version 4.0. 2020.

        • Stukenborg GJ
        • Wagner DP
        • Connors AFJ
        Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations.
        Med Care. 2001; 39: 727-739
        • Zhang Z
        Too much covariates in a multivariable model may cause the problem of overfitting.
        J Thorac Dis. 2014; 6: E196-E197https://doi.org/10.3978/j.issn.2072-1439.2014.08.33
        • Altman DG
        • Royston P
        The cost of dichotomising continuous variables.
        BMJ. 2006; 332: 1080https://doi.org/10.1136/bmj.332.7549.1080
        • Corrigendum to
        2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation [eur heart J 2020;doi: 10.1093/eurheartj/ehaa575].
        Eur Heart J. 2020; (doi:): ehaa895https://doi.org/10.1093/eurheartj/ehaa895
        • Tang EW
        • Wong CK
        • Herbison P
        Global registry of acute coronary events (GRACE) hospital discharge risk score accurately predicts long-term mortality post-acute coronary syndrome.
        Am Heart J. 2007; 153 (doi: S0002-8703(06)00896-9 [pii]): 29-35
        • van Walraven C
        • Austin P
        Administrative database research has unique characteristics that can risk biased results.
        J Clin Epidemiol. 2012; 65 (doi:[doi]): 126-131https://doi.org/10.1016/j.jclinepi.2011.08.002
        • Hosmer David W
        • Lemeshow Stanley
        • Sturdivant Rodney X.
        Applied Logistic Regression,. 2013;