期刊论文详细信息
BMC Nephrology
Impact of a modified data capture period on Liu comorbidity index scores in Medicare enrollees initiating chronic dialysis
Theresa I Shireman3  Edward F Ellerbeck3  Lei Dong2  Jonathan D Mahnken2  James B Wetmore4  Sally K Rigler1 
[1] Office of Scholarly, Academic & Research Mentoring (OSARM), University of Kansas Medical Center, 3901 Rainbow Blvd., Mail Stop 1037, Kansas City, KS, 66160, USA;Department of Biostatistics, University of Kansas School of Medicine, 3901 Rainbow Boulevard, MS 1026, Kansas City, KS, 66160, USA;Department of Preventive Medicine and Public Health, University of Kansas School of Medicine, 3901 Rainbow Boulevard, MS 1008, Kansas City, KS, 66160, USA;Department of Nephrology and Hypertension, University of Kansas School of Medicine, 3901 Rainbow Boulevard, MS 3002, Kansas City, KS, 66160, USA
关键词: Epidemiologic research design;    Renal dialysis;    Chronic;    Kidney failure;    Comorbidity;   
Others  :  1082987
DOI  :  10.1186/1471-2369-14-51
 received in 2012-12-17, accepted in 2013-02-21,  发布年份 2013
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【 摘 要 】

Background

The Liu Comorbidity Index uses the United States Renal Data System (USRDS) to quantify comorbidity in chronic dialysis patients, capturing baseline comorbidities from days 91 through 270 after dialysis initiation. The 270 day survival requirement results in sample size reductions and potential survivor bias. An earlier and shorter time-frame for data capture could be beneficial, if sufficiently similar comorbidity information could be ascertained.

Methods

USRDS data were used in a retrospective observational study of 70,114 Medicare- and Medicaid-eligible persons who initiated chronic dialysis during the years 2000–2005. The Liu index was modified by changing the baseline comorbidity capture period to days 1–90 after dialysis initiation for persons continuously enrolled in Medicare. The scores resulting from the original and the modified comorbidity indices were compared, and the impact on sample size was calculated.

Results

The original Liu comorbidity index could be calculated for 75% of the sample, but the remaining 25% did not survive to 270 days. Among 52,937 individuals for whom both scores could be calculated, the mean scores for the original and the modified index were 7.4 ± 4.0 and 6.4 ± 3.6 points, respectively, on a 24-point scale. The most commonly calculated difference between scores was zero, occurring in 44% of patients. Greater comorbidity was found in those who died before 270 days.

Conclusions

A modified version of the Liu comorbidity index captures the majority of comorbidity in persons who are Medicare-enrolled at the time of chronic dialysis initiation. This modification reduces sample size losses and facilitates inclusion of a sicker portion of the population in whom early mortality is common.

【 授权许可】

   
2013 Rigler et al; licensee BioMed Central Ltd.

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【 参考文献 】
  • [1]Concato J: Observational versus experimental studies: what's the evidence for a hierarchy? NeuroRx: J Am Soc Exp Neurother 2004, 1:341-347.
  • [2]Concato J: Is it time for medicine-based evidence? JAMA 2012, 307:1641-1643.
  • [3]Concato J, Lawler EV, Lew RA, Gaziano JM, Aslan M, Huang GD: Observational methods in comparative effectiveness research. Am J Med 2010, 123(12 Suppl 1):e16-e23.
  • [4]Strippoli GF, Craig JC, Schena FP: The number, quality, and coverage of randomized controlled trials in nephrology. J Am Soc Nephrol 2004, 15:411-419.
  • [5]Charytan D, Kuntz RE: The exclusion of patients with chronic kidney disease from clinical trials in coronary artery disease. Kidney Int 2006, 70:2021-2030.
  • [6]Uhlig K, Macleod A, Craig J: Grading evidence and recommendations for clinical practice guidelines in nephrology. A position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int 2006, 70:2058-2065.
  • [7]Uhlig K, Balk EM, Lau J: Grading evidence-based guidelines–what are the issues? Am J Kidney Dis 2008, 52:211-215.
  • [8]Coca SG, Krumholz HM, Garg AX, Parikh CR: Underrepresentation of renal disease in randomized controlled trials of cardiovascular disease. JAMA 2006, 296:1377-1384.
  • [9]Austin PC, Platt RW: Survivor treatment bias, treatment selection bias, and propensity scores in observational research. J Clin Epidemiol 2010, 63:136-138.
  • [10]Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ: Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 2001, 154:854-864.
  • [11]Schneeweiss S, Maclure M: Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol 2000, 29:891-898.
  • [12]Roberts MA, Polkinghorne KR, McDonald SP, Ierino FL: Secular trends in cardiovascular mortality rates of patients receiving dialysis compared with the general population. Am J Kidney Dis 2011, 58:64-72.
  • [13]Stenvinkel P, Barany P: Dialysis in 2011: Can cardiovascular risk in dialysis patients be decreased? Nature Rev Nephrol 2012, 8:72-74.
  • [14]van Manen JG, van Dijk PC, Stel VS: Confounding effect of comorbidity in survival studies in patients on renal replacement therapy. Nephrol Dial Transplant 2007, 22:187-195.
  • [15]Seliger SL: Comorbidity and confounding in end-stage renal disease. Kidney Int 2010, 77:83-85.
  • [16]U.S. Renal Data System 2010 Annual Data Report: Atlas of Chronic Kidney Disease and End-Stage Renal Disease in the United States, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. Bethesda, MD; 2011. http://www.usrds.org/adr.aspx webcite
  • [17]Liu J, Huang Z, Gilbertson DT, Foley RN, Collins AJ: An improved comorbidity index for outcome analyses among dialysis patients. Kidney Int 2010, 77:141-151.
  • [18]Plantinga LC, Fink NE, Levin NW: Early, intermediate, and long-term risk factors for mortality in incident dialysis patients: the Choices for Healthy Outcomes in Caring for ESRD (CHOICE) Study. Am J Kidney Dis 2007, 49:831-840.
  • [19]Wetmore JB, Mahnken JD, Rigler SK: Association of race with cumulative exposure to statins in dialysis. Am J Nephrol 2012, 36:90-96.
  • [20]Cassella G, Berger RL: Statistical Inference. 2nd edition. Pacific Grove, CA: Duxbury Press; 2002.
  • [21]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, 40:373-383.
  • [22]Hemmelgarn BR, Manns BJ, Quan H, Ghali WA: Adapting the Charlson Comorbidity Index for use in patients with ESRD. Am J Kidney Dis 2003, 42:125-132.
  • [23]van Manen JG, Korevaar JC, Dekker FW, Boeschoten EW, Bossuyt PM, Krediet RT: How to adjust for comorbidity in survival studies in ESRD patients: a comparison of different indices. Am J Kidney Dis 2002, 40:82-89.
  • [24]Van Manen JG, Korevaar JC, Dekker FW, Boeschoten EW, Bossuyt PM, Krediet RT: Adjustment for comorbidity in studies on health status in ESRD patients: which comorbidity index to use? J Am Soc Nephrol 2003, 14:478-485.
  • [25]Miskulin DC, Meyer KB, Martin AA: Comorbidity and its change predict survival in incident dialysis patients. Am J Kidney Dis 2003, 41:149-161.
  • [26]Wetmore JB, Rigler SK, Mahnken JD, Mukhopadhyay P, Shireman TI: Considering health insurance: how do dialysis initiates with Medicaid coverage differ from persons without Medicaid coverage? Nephrol Dialysis Transplant 2010, 25:198-205.
  • [27]Baldwin LM, Klabunde CN, Green P, Barlow W, Wright G: In search of the perfect comorbidity measure for use with administrative claims data: does it exist? Med Care 2006, 44:745-753.
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