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      Prediction Ability of Charlson, Elixhauser, and Rx-Risk Comorbidity Indices for Mortality in Patients with Hip Fracture. A Danish Population-Based Cohort Study from 2014 – 2018

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          Abstract

          Objective

          Comorbidity has an important role in risk prediction and risk adjustment modelling in observational studies. However, it is unknown which comorbidity index is most accurate to predict mortality in hip fracture patients. We aimed to evaluate the prediction ability, including discrimination and calibration of Charlson comorbidity index (CCI), Elixhauser comorbidity index (ECI) and Rx-risk index for 30 day- and 1 year mortality in a population-based cohort of hip fracture surgery patients.

          Methods

          Using the Danish Multidisciplinary Hip Fracture Registry in the period 2014–2018, 31,443 patients were included. CCI and ECI were based on discharge diagnoses, while Rx-Risk index was based on pharmacy dispensings. We used logistic regression to assess discrimination of the different indices, individually and in combinations, by calculating c-statistics and the contrast in c-statistic to a base model including only age and gender with 95% confidence intervals (CI).

          Results

          The study cohort were primarily female (69%) and older than 85 years (42%). The 30-day mortality was 10.1% and the 1-year mortality was 26.6%. Age and gender alone had a good discrimination ability for 30-day and 1-year mortality (c-statistic=0.70, CI: 0.69–0.71 and c-statistic=0.68, CI: 0.67 −0.69, respectively). By adding indices individually to the base model, Rx-risk index had the best 30-day and 1-year mortality discrimination ability (c-statistic=0.73, CI: 0.72–0.74 and 0.71 CI: 0.71–0.72, respectively). By adding combination of indices to the base model, a combination of CCI and the Rx-risk index had a 30-day and 1-year mortality discrimination ability of c-statistic=0.74, CI: 0.73–0.75 and c-statistic=0.73, CI: 0.73–0.74, respectively. Calibration of indices was similar.

          Conclusion

          The highest discrimination ability was achieved by combining CCI and Rx-risk index in addition to age and gender. However, age and gender alone had a fair mortality discrimination ability.

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          Most cited references35

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              The Danish National Patient Registry: a review of content, data quality, and research potential

              Background The Danish National Patient Registry (DNPR) is one of the world’s oldest nationwide hospital registries and is used extensively for research. Many studies have validated algorithms for identifying health events in the DNPR, but the reports are fragmented and no overview exists. Objectives To review the content, data quality, and research potential of the DNPR. Methods We examined the setting, history, aims, content, and classification systems of the DNPR. We searched PubMed and the Danish Medical Journal to create a bibliography of validation studies. We included also studies that were referenced in retrieved papers or known to us beforehand. Methodological considerations related to DNPR data were reviewed. Results During 1977–2012, the DNPR registered 8,085,603 persons, accounting for 7,268,857 inpatient, 5,953,405 outpatient, and 5,097,300 emergency department contacts. The DNPR provides nationwide longitudinal registration of detailed administrative and clinical data. It has recorded information on all patients discharged from Danish nonpsychiatric hospitals since 1977 and on psychiatric inpatients and emergency department and outpatient specialty clinic contacts since 1995. For each patient contact, one primary and optional secondary diagnoses are recorded according to the International Classification of Diseases. The DNPR provides a data source to identify diseases, examinations, certain in-hospital medical treatments, and surgical procedures. Long-term temporal trends in hospitalization and treatment rates can be studied. The positive predictive values of diseases and treatments vary widely (<15%–100%). The DNPR data are linkable at the patient level with data from other Danish administrative registries, clinical registries, randomized controlled trials, population surveys, and epidemiologic field studies – enabling researchers to reconstruct individual life and health trajectories for an entire population. Conclusion The DNPR is a valuable tool for epidemiological research. However, both its strengths and limitations must be considered when interpreting research results, and continuous validation of its clinical data is essential.
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                Author and article information

                Journal
                Clin Epidemiol
                Clin Epidemiol
                clep
                Clinical Epidemiology
                Dove
                1179-1349
                08 March 2022
                2022
                : 14
                : 275-287
                Affiliations
                [1 ]Department of Clinical Epidemiology, Aarhus University Hospital and Department of Clinical Medicine, Aarhus University , Aarhus, Denmark
                Author notes
                Correspondence: Alma Becic Pedersen, Tel +45 87167212, Fax +45 87167215, Email abp@clin.au.dk
                Author information
                http://orcid.org/0000-0003-4917-7918
                http://orcid.org/0000-0002-6036-8240
                http://orcid.org/0000-0003-0112-5801
                http://orcid.org/0000-0001-5473-9386
                http://orcid.org/0000-0002-3288-9401
                Article
                346745
                10.2147/CLEP.S346745
                8922332
                35299726
                16d68e82-ee16-442c-b600-71b209690447
                © 2022 Vesterager et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 15 November 2021
                : 22 February 2022
                Page count
                Figures: 2, Tables: 5, References: 35, Pages: 13
                Categories
                Original Research

                Public health
                charlson comorbidity index,elixhauser index,rx-risk index,multimorbidity,discrimination,calibration

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