Browse > Article

A Comparative Study on Comorbidity Measurements with Lookback Period using Health Insurance Database: Focused on Patients Who Underwent Percutaneous Coronary Intervention  

Kim, Kyoung-Hoon (Review & Assessment Policy Institute, Health Insurance Review & Assessment Service)
Ahn, Lee-Su (Review & Assessment Policy Institute, Health Insurance Review & Assessment Service)
Publication Information
Journal of Preventive Medicine and Public Health / v.42, no.4, 2009 , pp. 267-273 More about this Journal
Abstract
Objectives : To compare the performance of three comorbidity measurements (Charlson comorbidity index, Elixhauser s comorbidity and comorbidity selection) with the effect of different comorbidity lookback periods when predicting in-hospital mortality for patients who underwent percutaneous coronary intervention. Methods : This was a retrospective study on patients aged 40 years and older who underwent percutaneous coronary intervention. To distinguish comorbidity from complications, the records of diagnosis were drawn from the National Health Insurance Database excluding diagnosis that admitted to the hospital. C-statistic values were used as measures for in comparing the predictability of comorbidity measures with lookback period, and a bootstrapping procedure with 1,000 replications was done to determine approximate 95% confidence interval. Results : Of the 61,815 patients included in this study, the mean age was 63.3 years (standard deviation: ${\pm}$10.2) and 64.8% of the population was male. Among them, 1,598 2.6%) had died in hospital. While the predictive ability of the Elixhauser's comorbidity and comorbidity selection was better than that of the Charlson comorbidity index, there was no significant difference among the three comorbidity measurements. Although the prevalence of comorbidity increased in 3 years of lookback periods, there was no significant improvement compared to 1 year of a lookback period. Conclusions : In a health outcome study for patients who underwent percutaneous coronary intervention using National Health Insurance Database, the Charlson comorbidity index was easy to apply without significant difference in predictability compared to the other methods. The one year of observation period was adequate to adjust the comorbidity. Further work to select adequate comorbidity measurements and lookback periods on other diseases and procedures are needed.
Keywords
Claims data; Comorbidity index; Risk; adjustment; In-hospital mortality; Percutaneous coronary intervention;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
Times Cited By SCOPUS : 3
연도 인용수 순위
1 Kim HY. Relationship between Hospital Volume and Risk-Adjusted Mortality Rate Following Percutaneous Coronary Intervention [dissertation]. Seoul: Korea University; 2007. (Korean)
2 Lee KS, Lee SI. Does a higher coronary artery bypass graft surgery volume always have a low in-hospital mortality rate in Korea? J Prev Med Public Health 2006; 39(1): 13-20. (Korean)   과학기술학회마을   ScienceOn
3 Preen DB, Holman CD, Spilsbury K, Semmens JB, Brameld KJ. Length of comorbidity lookback period affected regression model performance of administrative health data. J Clin Epidemiol 2006; 59(9): 940-946   DOI   ScienceOn
4 Stukenborg GJ, Wagner DP, Connors AF Jr. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalization. Med Care 2001; 39(7): 727-739   DOI   ScienceOn
5 Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality. J Clin Epidemiol 2004; 57(12): 1288-1294   DOI   ScienceOn
6 Alidoosti M, Salarifar M, Zeinali AM, Kassaian SE, Dehkordi MR. Comparison of outcomes of percutaneous coronary intervetion on proximal versus non-proximal left anterior descending coronary artery, proximal left circumflex, and proximal right coronary artery: A corss-sectional study. BMC Cardiovacs Disord 2007; 7: 7   DOI   PUBMED
7 Zavascki AP, Fuchs SC. The need for reappraisal of AIDS score weight of Charlson comorbidity index. J Clin Epidemiol 2007; 60(9): 867-868   DOI   ScienceOn
8 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36(1): 8-27   DOI   ScienceOn
9 Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005; 43(11): 1130-1139   DOI   ScienceOn
10 Zhang JX, Iwashyna TJ, Christakis NA. The performance of different lookback periods and sources of information for Charlson comorbidity adjustment in medicare claims. Med Care 1999; 37(11): 1128-1139   DOI   ScienceOn
11 Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chronic Dis 1987; 40(5): 373-383   DOI   ScienceOn
12 Epstein AJ, Rathore SS, Krumholz HM, Volpp KG. Volume-based referral for cardiovascular procedures in the United States: A crosssectional regression analysis. BMC Health Serv Res 2005; 5: 42   DOI   PUBMED   ScienceOn
13 Li B, Evans D, Faris P, Dean S, Quan H. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res 2008; 8: 12   DOI   PUBMED   ScienceOn
14 Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol 2002; 55(6): 573-587   DOI   ScienceOn
15 Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in medicare populations. Health Serv Res 2003; 38(4): 1103-1120   DOI   ScienceOn
16 Birim O, Maat AP, Kappetein AP, van Meerbeeck JP, Damhuis RA, Bogers AJ. Validation of the Charlson comorbidity index in patients with operated primary non-small cell lung cancer. Eur J Cardiothorac Surg 2003; 23(1): 30-34   DOI   ScienceOn
17 Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/ Deyo methods of comorbidity measurement in administrative data. Med Care 2004; 42(4): 355-360   DOI   ScienceOn
18 Carey JS, Danielsen B, Gold JP, Rossiter SJ. Procedure rates and outcomes of coronary revascularization procedures in California and New York. J Thorac Cardiovasc Surg 2005; 129(6): 1276-1282   DOI   PUBMED   ScienceOn
19 Harjai KJ, Berman AD, Grines CL, Kahn J, Marsalese D, Mehta RH, et al. Impact of interventionalist volume, experience, and board certification on coronary angioplasty outcomes in the era of stenting. Am J Cardiol 2004; 94(4): 421-426   DOI   ScienceOn
20 Burton KR, Slack R, Oldroyd KG, Pell AC, Flapan AD, Starkey IR, et al. Hosptial volume of throughput and periprocedural and mediumterm adverse events after percutaneous coronary intervention: Retrospective cohort study of all 17,417 procedures undertaken in Scotland, 1997-2003. Heart 2006; 92(11): 1667-1672   DOI   PUBMED   ScienceOn
21 Lee DS, Donovan L, Austin PC, Gong Y, Liu PP, Rouleau JL, et al. Comparison of coding of heart failure and comorbidities in administrative and clinical data for use in outcomes research. Med Care 2005; 43(2): 182-188   DOI   ScienceOn
22 Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidity index using physician claims data. J Clin Epidemiol 2000; 53(12): 1258-1267   DOI   ScienceOn
23 Birim O, Kappetein AP, Bogers AJ. Charlson comorbidity index as a predictor of long-term outcome after surgery for nonsmall cell lung cancer. Eur J Cardiothorac Surg 2005; 28(5): 759-762   DOI   ScienceOn
24 Agency for Healthcare Research and Quality. AHRQ Quality Indicators-Guide to Inpatient Quality Indicators: Quality of Care in Hospitals-Volume, Mortality, and Utilization. Rockville: Agency for Healthcare Research and Quality; 2002. Report No.: AHRQ Pub. No. 02-RO204
25 Sundararajan V, Quan H, Halfon P, Fushimi K, Luthi J, Burnand B, et al. Cross-national comparative performance of three versions of the ICD-10 Charlson index. Med Care 2007; 45(12): 1210-1215   DOI   ScienceOn
26 Kim JY, Kim HY, Im JH. Development of Risk Adjustment and Prediction Methods for Care Episodes using National Health Insurance Database. Seoul: Health Insurance Review & Assessment Service; 2007. (Korean)
27 Cleves MA, Sanchez N, Draheim M. Evaluation of two competing methods for calculating Charlson s comorbidity index when analyzing short-term mortality using administrative data. J Clin Epidemiol 1997; 50(8): 903-908   DOI   ScienceOn