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      Performance of Comprehensive Risk Adjustment for the Prediction of In-Hospital Events Using Administrative Healthcare Data: The Queralt Indices.

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          Abstract

          Accurate risk adjustment is crucial for healthcare management and benchmarking.

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          Most cited references15

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          Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work.

          Steven R Austin, Yu-Ning Wong, Robert Uzzo … (2015)
          Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate.
          Article 0 comments Cited 245 times – based on 0 reviews     Review now
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            Systematic review of comorbidity indices for administrative data.

            Paul Aylin, Mansour Sharabiani, Alex Bottle (2012)
            Adjustment for comorbidities is common in performance benchmarking and risk prediction. Despite the exponential upsurge in the number of articles citing or comparing Charlson, Elixhauser, and their variants, no systematic review has been conducted on studies comparing comorbidity measures in use with administrative data. We present a systematic review of these multiple comparison studies and introduce a new meta-analytical approach to identify the best performing comorbidity measures/indices for short-term (inpatient + ≤ 30 d) and long-term (outpatient+>30 d) mortality. Articles up to March 18, 2011 were searched based on our predefined terms. The bibliography of the chosen articles and the relevant reviews were also searched and reviewed. Multiple comparisons between comorbidity measures/indices were split into all possible pairs. We used the hypergeometric test and confidence intervals for proportions to identify the comparators with significantly superior/inferior performance for short-term and long-term mortality. In addition, useful information such as the influence of lookback periods was extracted and reported. Out of 1312 retrieved articles, 54 articles were eligible. The Deyo variant of Charlson was the most commonly referred comparator followed by the Elixhauser measure. Compared with baseline variables such as age and sex, comorbidity adjustment methods seem to better predict long-term than short-term mortality and Elixhauser seems to be the best predictor for this outcome. For short-term mortality, however, recalibration giving empirical weights seems more important than the choice of comorbidity measure. The performance of a given comorbidity measure depends on the patient group and outcome. In general, the Elixhauser index seems the best so far, particularly for mortality beyond 30 days, although several newer, more inclusive measures are promising.
            Article 0 comments Cited 165 times – based on 0 reviews     Review now
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              Choosing a future for epidemiology: I. Eras and paradigms.

              M Susser, E. Susser (1996)
              To inform choices about the future of epidemiology, the present condition of epidemiology is examined, in terms of its evolution through three eras, each demarcated by its own paradigm: (1) the era of sanitary statistics with its paradigm, miasma; (2) the era of infectious disease epidemiology with its paradigm, the germ theory; and (3) the era of chronic disease epidemiology with its paradigm, the black box. The historical context in which these eras arose is briefly described. In each era, the public health was at the center of the concerns of the founders and early protagonists of the prevailing paradigm. Around this intellectual development we weave a further theme. We argue that in the present era, the public health has become less central a concern. At the same time, in epidemiology today the dominant black box paradigm is of declining utility and is likely soon to be superseded.
              Article 0 comments Cited 131 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (iso-abbrev): Risk Manag Healthc Policy
                Title: Risk management and healthcare policy
                Publisher: Informa UK Limited
                ISSN: 1179-1594
                ISSN (Print): 1179-1594
                Publication date (Electronic): 2020
                Volume: 13
                Affiliations
                [1 ] Catalan Institute of Health, Barcelona, Spain.
                [2 ] Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
                [3 ] Bellvitge University Hospital, L'Hospitalet de Llobregat, Barcelona, Spain.
                [4 ] Vall d'Hebron Hospital, Barcelona, Spain.
                [5 ] Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
                [6 ] Catalan Health Service, Barcelona, Spain.
                [7 ] Catalan Health Department, Barcelona, Spain.
                [8 ] Catalan Institute of Oncology (ICO), Barcelona, Spain.
                [9 ] University of Barcelona, Barcelona, Spain.
                Article
                Publisher Item ID: 228415
                DOI: 10.2147/RMHP.S228415
                PMC ID: 7125405
                PubMed ID: 32280290
                SO-VID: 8196134b-c33a-4113-afe2-068a25bc157e
                Copyright © © 2020 Monterde et al.
                History

                Keywords: Queralt’s indices,benchmarking,case-mix,comorbidity,discrimination,multimorbidity,risk
                Data availability:
                Keywords: Queralt’s indices, benchmarking, case-mix, comorbidity, discrimination, multimorbidity, risk

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