Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
권호 표지

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

건강보험 청구자료에서 동반질환 보정방법과 관찰기관 비교 연구: 경피적 관상동맥 중재술을 받은 환자를 대상으로

  • Kim, Kyoung-Hoon (Review & Assessment Policy Institute, Health Insurance Review & Assessment Service) ;
  • Ahn, Lee-Su (Review & Assessment Policy Institute, Health Insurance Review & Assessment Service)
  • 김경훈 (건강보험심사평가원 심사평가정책연구소) ;
  • 안이수 (건강보험심사평가원 심사평가정책연구소)
  • Published : 2009.07.31
Download PDF
⟨ Previous Next ⟩

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

References

  1. 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 https://doi.org/10.1016/S0895-4356(97)00091-7
  2. Kim HY. Relationship between Hospital Volume and Risk-Adjusted Mortality Rate Following Percutaneous Coronary Intervention [dissertation]. Seoul: Korea University; 2007. (Korean)
  3. 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)
  4. 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 https://doi.org/10.1016/0021-9681(87)90171-8
  5. 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 https://doi.org/10.1111/1475-6773.00165
  6. 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 https://doi.org/10.1016/j.ejcts.2005.06.046
  7. 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 https://doi.org/10.1016/S1010-7940(02)00721-2
  8. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36(1): 8-27 https://doi.org/10.1097/00005650-199801000-00004
  9. 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 https://doi.org/10.1097/00005650-200107000-00009
  10. 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 https://doi.org/10.1097/01.mlr.0000118861.56848.ee
  11. 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 https://doi.org/10.1016/j.jclinepi.2005.12.013
  12. 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 https://doi.org/10.1097/00005650-200502000-00012
  13. 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
  14. 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 https://doi.org/10.1186/1471-2261-7-7
  15. 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 https://doi.org/10.1136/hrt.2005.086736
  16. 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 https://doi.org/10.1016/j.jtcvs.2004.12.043
  17. 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 https://doi.org/10.1186/1472-6963-5-42
  18. 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 https://doi.org/10.1016/j.amjcard.2004.04.056
  19. 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 https://doi.org/10.1016/S0895-4356(01)00521-2
  20. 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 https://doi.org/10.1016/j.jclinepi.2004.03.012
  21. 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 https://doi.org/10.1097/MLR.0b013e3181484347
  22. 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 https://doi.org/10.1097/01.mlr.0000182534.19832.83
  23. 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 https://doi.org/10.1016/S0895-4356(00)00256-0
  24. 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 https://doi.org/10.1186/1472-6963-8-12
  25. Zavascki AP, Fuchs SC. The need for reappraisal of AIDS score weight of Charlson comorbidity index. J Clin Epidemiol 2007; 60(9): 867-868 https://doi.org/10.1016/j.jclinepi.2006.11.004
  26. 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 https://doi.org/10.1097/00005650-199911000-00005
  27. 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)