Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
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Prognostic Impact of Charlson Comorbidity Index Obtained from Medical Records and Claims Data on 1-year Mortality and Length of Stay in Gastric Cancer Patients

위암환자에서 의무기록과 행정자료를 활용한 Charlson Comorbidity Index의 1년 이내 사망 및 재원일수 예측력 연구

  • Kyung, Min-Ho (Department of Preventive Medicine, College of Medicine, Korea University) ;
  • Yoon, Seok-Jun (Department of Preventive Medicine, College of Medicine, Korea University) ;
  • Ahn, Hyeong-Sik (Department of Preventive Medicine, College of Medicine, Korea University) ;
  • Hwang, Se-Min (Department of Preventive Medicine, College of Medicine, Korea University) ;
  • Seo, Hyun-Ju (Department of Preventive Medicine, College of Medicine, Korea University) ;
  • Kim, Kyoung-Hoon (Insurance Review and Assessment Service) ;
  • Park, Hyeung-Keun (Department of Health Policy and Management, School of Medicine, Cheju National University)
  • 경민호 (고려대학교 의과대학 예방의학교실) ;
  • 윤석준 (고려대학교 의과대학 예방의학교실) ;
  • 안형식 (고려대학교 의과대학 예방의학교실) ;
  • 황세민 (고려대학교 의과대학 예방의학교실) ;
  • 서현주 (고려대학교 의과대학 예방의학교실) ;
  • 김경훈 (건강보험심사평가원) ;
  • 박형근 (제주대학교 의과대학 의료관리학교실)
  • Published : 2009.03.31
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Abstract

Objectives : We tried to evaluate the agreement of the Charlson comorbidity index values(CCI) obtained from different sources(medical records and National Health Insurance claims data) for gastric cancer patients. We also attempted to assess the prognostic value of these data for predicting 1-year mortality and length of the hospital stay(length of stay). Methods : Medical records of 284 gastric cancer patients were reviewed, and their National Health Insurance claims data and death certificates were also investigated. To evaluate agreement, the kappa coefficient was tested. Multiple logistic regression analysis and multiple linear regression analysis were performed to evaluate and compare the prognostic power for predicting 1 year mortality and length of stay. Results : The CCI values for each comorbid condition obtained from 2 different data sources appeared to poorly agree(kappa: 0.00-0.59). It was appeared that the CCI values based on both sources were not valid prognostic indicators of 1-year mortality. Only medical record-based CCI was a valid prognostic indicator of length of stay, even after adjustment of covariables($\beta$ = 0.112, 95% CI = [0.017-1.267]). Conclusions : There was a discrepancy between the data sources with regard to the value of CCI both for the prognostic power and its direction. Therefore, assuming that medical records are the gold standard for the source for CCI measurement, claims data is not an appropriate source for determining the CCI, at least for gastric cancer.

Keywords

References

  1. Greenfield S, Blanco DM, Elashoff RM, Ganz PA. Patterns of care related to age of breast cancer patients. JAMA 1987; 257(20): 2766-2770 https://doi.org/10.1001/jama.257.20.2766
  2. 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
  3. 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
  4. Hall SF, Rochon PA, Streiner DL, Paszat LF, Groome PA, Rohland SL. Measuring comorbidity in patients with head and neck cancer. Laryngoscope 2002; 112(11): 1988-1996 https://doi.org/10.1097/00005537-200211000-00015
  5. Hall WH, Jani AB, Ryu JK, Narayan S, Vijayakumar S. The impact of age andcomorbidity on survival outcomes and treatment patterns in prostate cancer. Prostate Cancer Prostatic Dis 2005; 8(1): 22-30 https://doi.org/10.1038/sj.pcan.4500772
  6. Satariano WA, Ragland DR. The effect of comorbidity on 3-year survival of women with primary breast cancer. Ann Intern Med 1994; 120(2): 104-110 https://doi.org/10.7326/0003-4819-120-2-199401150-00002
  7. Soares M, Salluh JI, Ferreira CG, Luiz RR, Spector N, Rocco JR. Impact of two different comorbidity measures on the 6-month mortality of critically ill cancer patients. Intensive Care Med 2005; 31(3): 408-415 https://doi.org/10.1007/s00134-005-2554-z
  8. Sabin SL, Rosenfeld RM, Sundaram K, Har-el G, Lucente FE. The impact of comorbidity and age on survival with laryngeal cancer. Ear Nose Throat J 1999; 78(8): 578,581-574
  9. Ogle KS, Swanson GM, Woods N, Azzouz F. Cancer and comorbidity. Cancer 2000; 88(3): 653-663 https://doi.org/10.1002/(SICI)1097-0142(20000201)88:3<653::AID-CNCR24>3.0.CO;2-1
  10. 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
  11. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992; 45(6): 613-619 https://doi.org/10.1016/0895-4356(92)90133-8
  12. Klabunde CN, Potosky AL, Legler JM, Warren JL. Development of a comorbidityindex using physician claims data. J Clin Epidemiol 2000; 53(12): 1258-1267 https://doi.org/10.1016/S0895-4356(00)00256-0
  13. Klabunde CN, Warren JL, Legler JM. Assessing comorbidity using claims data: An overview. Med Care 2002; 40(8 Suppl): IV-26-35 https://doi.org/10.1097/00005650-200201000-00005
  14. 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
  15. 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
  16. Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-cm administrative data: Differing perspectives. J Clin Epidemiol 1993; 46(10): 1075-1079 https://doi.org/10.1016/0895-4356(93)90103-8
  17. 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
  18. Newschaffer CJ, Bush TL, Penberthy LT. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. J Clin Epidemiol 1997; 50(6): 725-733 https://doi.org/10.1016/S0895-4356(97)00050-4
  19. Lee JH, Hyung WJ, Noh SH. Randomomized prosective trial of drain use after gastric resections for gastric cancer patients. J Korean Sur Soc 2002; 63(2): 123-128. (Korean)
  20. Lee TG, Noh SM, Lee TY. Assessment of peritoneal irrigation and drainage following elective gastric cancer surgery. J Korean Sur Soc 2002; 63(4): 292-297. (Korean)
  21. Suh HS, Lee KH, Kim HC, Yu CS, Kim JC. The postoperative impact of co-morbidity in colorectal cancer surgery. J Korean Soc Coloproctol 2003; 19(5): 299-306. (Korean)
  22. Lee JW, Seo IY, Rim JS. Surgical results of laparoscopic radical nephrectomy according to tumor size in renal cell carcinomas. Korean J Urol 2008;49(3): 203-207. (Korean) https://doi.org/10.4111/kju.2008.49.3.203
  23. Lee EK. A study on predict of health outcomes in patients undergoing operation for gastric cancer by Charlson comorbidity index [dissertation]. Seoul: Korea University; 2008. (Korean)
  24. Klabunde CN, Harlan LC, Warren JL. Data sources for measuring comorbidity: Acomparison of hospital records and medicare claims for cancer patients. Med Care 2006; 44(10): 921-928 https://doi.org/10.1097/01.mlr.0000223480.52713.b9
  25. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33(1): 159-174 https://doi.org/10.2307/2529310
  26. Humphries KH, Rankin JM, Carere RG, Buller CE, Kiely FM, Spinelli JJ. Co-morbidity data in outcomes research are clinical data derived from administrative databases a reliable alternative to chart review? J Clin Epidemiol 2000; 53(4): 343-349 https://doi.org/10.1016/S0895-4356(99)00188-2
  27. Malenka DJ, McLerran D, Roos N, Fisher ES, Wennberg EJ. Using administrative data to describe casemix: A comparison with the medical record. J Clin Epidemiol 1994; 47(9): 1027-1032 https://doi.org/10.1016/0895-4356(94)90118-X
  28. Luthi JC, Troillet N, Eisenring MC, Sax H, Burnand B, Quan H, et al. Administrative data outperformed single-day chart review for comorbidity measure. Int J Qual Health Care 2007; 19(4): 225-231 https://doi.org/10.1093/intqhc/mzm017
  29. Librero J, Peiro S,Ordinana R. Chronic comorbidity and outcomes of hospital care: Length of stay, mortality, and readmission at 30 and 365 days. J Clin Epidemiol 1999; 52(3): 171-179 https://doi.org/10.1016/S0895-4356(98)00160-7
  30. Matsui K, Goldman L, Johnson P, Kuntz K, Cook E, Lee T. Comorbidity as a correlate of length of stay for hospitalized patients with acute chest pain. J Gen Intern Med 1996; 11(5): 262-268 https://doi.org/10.1007/BF02598265
  31. Lubke T, Monig SP, Schneider PM, Holscher AH, Bollschweiler E. Does Charlsoncomorbidity index correlate with short-term outcome in patients with gastric cancer? Zentralbl Chir 2003; 128(11): 970-976. (German) https://doi.org/10.1055/s-2003-44805

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