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      Feasibility of coding-based Charlson comorbidity index for hospitalized patients in China, a representative developing country

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          Abstract

          Background

          The Charlson Comorbidity Index (CCI) can be automatically calculated from the International Classification of Disease (ICD) code. However, the feasibility of this transformation has not been acknowledged, particularly in hospitals without a qualified ICD coding system. Here, we investigated the utility of coding-based CCI in China.

          Methods

          A multi-center, population-based, retrospective observational study was conducted, using a dataset incorporating 2,464,395 adult subjects from 15 hospitals. CCI was calculated using both ICD-10-based and diagnosis-based method, according to the transformation rule reported previously and to the literal description from discharge diagnosis, respectively. A κ coefficient of variation was used as a measure of agreement between the above two methods for each hospital. The discriminative abilities of the two methods were compared using the receiver-of-operating characteristic curve (ROC) for prediction of in-hospital mortality.

          Results

          Total agreement between the ICD-based and diagnosis-based CCI for each index ranged from 86.1 to 100%, with κ coefficients from 0.210 [95% confidence interval (CI) 0.208–0.212] to 0.932 (95% CI 0.924–0.940). None of the 19 indices of CCI had a κ coefficient > 0.75 in all the hospitals included for study. The area under the curve of ROC for in-hospital mortality of all 15 hospitals was significantly lower for ICD-based than diagnosis-based CCI [0.735 (0.732, 0.739) vs 0.760 (0.757, 0.764)], indicative of more limited discriminative ability of the ICD-based calculation.

          Conclusions

          CCI calculated using ICD-10 coding did not agree with diagnosis-based CCI. ICD-based CCI displayed diminished discrimination performance in terms of in-hospital mortality, indicating that this method is not promising for CCI scoring in China under the present circumstances.

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          Most cited references12

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          Measuring diagnoses: ICD code accuracy.

          To examine potential sources of errors at each step of the described inpatient International Classification of Diseases (ICD) coding process. The use of disease codes from the ICD has expanded from classifying morbidity and mortality information for statistical purposes to diverse sets of applications in research, health care policy, and health care finance. By describing a brief history of ICD coding, detailing the process for assigning codes, identifying where errors can be introduced into the process, and reviewing methods for examining code accuracy, we help code users more systematically evaluate code accuracy for their particular applications. We summarize the inpatient ICD diagnostic coding process from patient admission to diagnostic code assignment. We examine potential sources of errors at each step and offer code users a tool for systematically evaluating code accuracy. Main error sources along the "patient trajectory" include amount and quality of information at admission, communication among patients and providers, the clinician's knowledge and experience with the illness, and the clinician's attention to detail. Main error sources along the "paper trail" include variance in the electronic and written records, coder training and experience, facility quality-control efforts, and unintentional and intentional coder errors, such as misspecification, unbundling, and upcoding. By clearly specifying the code assignment process and heightening their awareness of potential error sources, code users can better evaluate the applicability and limitations of codes for their particular situations. ICD codes can then be used in the most appropriate ways.
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            The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality.

            The most used score to measure comorbidity is the Charlson index. Its application to a health care administrative database including International Classification of Diseases, 10th edition (ICD-10) codes, medical procedures, and medication required studying its properties on survival. Our objectives were to adapt the Charlson comorbidity index to the French National Health Insurance database to predict 1-year mortality of discharged patients and to compare discrimination and calibration of different versions of the Charlson index.
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              Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data

              Background Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges. Methods Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities. Results For the 17 the Charlson co-morbidities, the sensitivity - median (min-max) - was 36.5% (17.4-64.1) in 1999, 42.5% (22.2-64.6) in 2001 and 42.8% (8.4-75.6) in 2003. For the 29 Elixhauser co-morbidities, the sensitivity was 34.2% (1.9-64.1) in 1999, 38.6% (10.5-66.5) in 2001 and 41.6% (5.1-76.5) in 2003. Between 1999 and 2003, sensitivity estimates increased for 30 co-morbidities and decreased for 6 co-morbidities. The increase in sensitivities was statistically significant for six conditions and the decrease significant for one. Kappa values were increased for 29 co-morbidities and decreased for seven. Conclusions Accuracy of administrative data in recording clinical conditions improved slightly between 1999 and 2003. These findings are of relevance to all jurisdictions introducing new coding systems, because they demonstrate a phenomenon of improved administrative data accuracy that may relate to a coding 'learning curve' with the new coding system.
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                Author and article information

                Contributors
                molly2leo@126.com
                xiezhen6289@126.com
                liuguohui5@126.com
                strong_he@163.com
                wedding2@163.com
                sunny4666@163.com
                codein2004@126.com
                dingfeng@sjtu.edu.cn
                78743099@qq.com
                haoliqilin@163.com
                luchen706@163.com
                sunjing1165@163.com
                nmgxulibin@sina.com
                13826638251@139.com
                493022054@qq.com
                friendphw@163.com
                429386084@qq.com
                swlfxjl@163.com
                chenyuanhan@gdph.org.cn
                xinlingliang_ggh@163.com
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                18 May 2020
                18 May 2020
                2020
                : 20
                : 432
                Affiliations
                [1 ]Division of Nephrology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No.106 Zhongshan Road 2, Guangzhou, 510080 Guangdong China
                [2 ]GRID grid.440180.9, ISNI 0000 0004 7480 2233, Department of Nephrology, , Dongguan People’s Hospital, ; Dongguan, 523018 China
                [3 ]GRID grid.410646.1, ISNI 0000 0004 1808 0950, Department of Dermatology, , Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, ; Chengdu, 610072 China
                [4 ]GRID grid.417401.7, ISNI 0000 0004 1798 6507, Department of Nephrology, , Zhejiang Provincial People’s Hospital (People’s Hospital of Hangzhou Medical College), ; Hangzhou, 310014 China
                [5 ]GRID grid.16821.3c, ISNI 0000 0004 0368 8293, Division of Nephrology, , Shanghai Ninth People’s Hospital, School of Medicine, Shanghai Jiaotong University, ; Shanghai, 200030 China
                [6 ]Department of Nephrology, Chongqing Ninth People’s Hospital, Chongqing, 400700 China
                [7 ]GRID grid.452696.a, Department of Nephrology, , Second Hospital of Anhui Medical University, ; Hefei, 230601 China
                [8 ]GRID grid.410644.3, Department of Nephrology, , People’s Hospital of Xinjiang Uygur Autonomous Region, ; Urumqi, 830001 China
                [9 ]GRID grid.452829.0, Department of Nephrology, , Second Hospital of Jilin University, ; Changchun, 130022 China
                [10 ]GRID grid.440229.9, ISNI 0000 0004 1757 7789, Department of Nephrology, , Inner Mongolia People’s Hospital, ; Hohhot, 010017 China
                [11 ]Second Division of Internal Medicine, Wuhua People’s Hospital, Meizhou, 514400 China
                [12 ]Department of Nephrology, First People’s Hospital of Kashgar, Kashgar, 844000 China
                [13 ]People’s Hospital of Wanning & The First Affiliated Hospital of Chongqing Medical Univesity, Wanning, 571500 China
                [14 ]Department of Nephrology, Chongzuo People’s Hospital, Chongzuo, 844000 China
                [15 ]Division of Nephrology, Guangdong Lufeng People’s Hospital, Lufeng, 516500 China
                Article
                5273
                10.1186/s12913-020-05273-8
                7236530
                32423399
                d00095e5-3dfe-454f-9eeb-0c7aa858c35c
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 16 November 2019
                : 29 April 2020
                Funding
                Funded by: Guangdong Science and Technology Project
                Award ID: 2017A070709008
                Award Recipient :
                Funded by: Guangzhou Science and Technology Project
                Award ID: 201604020037
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Health & Social care
                icd-10,charlson comorbidity index,diagnosis,agreement,discrimination
                Health & Social care
                icd-10, charlson comorbidity index, diagnosis, agreement, discrimination

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