Systematic review of comorbidity indices for administrative data

Medical Care

Volumn 50, Issue 12, 2012, Pages 1109-1118

  Sharabiani, Mansour T A ^a   Aylin, Paul ^a   Bottle, Alex ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

administrative data; casemix; comorbidity; meta analysis; mortality; systematic review

Indexed keywords

ARTICLE; COMORBIDITY; CONFIDENCE INTERVAL; GROUPS BY AGE AND SEX; HUMAN; LONG TERM SURVIVAL; MORTALITY; PERFORMANCE MEASUREMENT SYSTEM; PRIORITY JOURNAL; SHORT TERM SURVIVAL; SURVIVAL PREDICTION; SYSTEMATIC REVIEW;

COMORBIDITY; HUMANS; RISK ADJUSTMENT;

EID: 84870061056     PISSN: 00257079     EISSN: 15371948     Source Type: Journal    
DOI: 10.1097/MLR.0b013e31825f64d0     Document Type: Article

References (73)

1. Aylin P, Bottle A, Majeed A. Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models. BMJ. 2007;334:1044.
2. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373-383. (Pubitemid 17055592)
3. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8-27. (Pubitemid 128456396)
4. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613-619.
5. D'Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol. 1996;49:1429-1433. (Pubitemid 27028932)
6. D'Hoore W, Sicotte C, Tilquin C. Risk adjustment in outcome assessment: the Charlson comorbidity index. Methods Inf Med. 1993;32: 382-387. (Pubitemid 24001924)
7. 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;46:1075-1079; discussion 1081-1090.
8. Khan NF, Perera R, Harper S, et al. Adaptation and validation of the Charlson Index for Read/OXMIS coded databases. BMC Fam Pract. 2010;11:1.
9. de Groot V, Beckerman H, Lankhorst GJ, et al. How to measure comorbidity: a critical review of available methods. J Clin Epidemiol. 2003;56:221-229. (Pubitemid 36513483)
10. Extermann M. Measuring comorbidity in older cancer patients. Eur J Cancer. 2000;36:453-471. (Pubitemid 30136223)
11. Hall WH, Jani AB, Ryu JK, et al. The impact of age and comorbidity on survival outcomes and treatment patterns in prostate cancer. Prostate Cancer Prostatic Dis. 2005;8:22-30.
12. Leal JR, Laupland KB. Validity of ascertainment of co-morbid illness using administrative databases: a systematic review. Clin Microbiol Infect. 2010;16:715-721.
13. Needham DM, Scales DC, Laupacis A, et al. A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk adjustment in critical care research. J Crit Care. 2005;20:12-19. (Pubitemid 40602219)
14. Gagne JJ, Glynn RJ, Avorn J, et al. A combined comorbidity score predicted mortality in elderly patients better than existing scores. J Clin Epidemiol. 2011;64:749-759.
15. van Walraven C, Escobar GJ, Greene JD, et al. The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J Clin Epidemiol. 2010;63:798-803.
16. Moore L, Lavoie A, Sage NL, et al. Using information on preexisting conditions to predict mortality from traumatic injury. Ann Emerg Med. 2008;52:356e2-364e2.
17. Schneeweiss S, Seeger JD, Maclure M, et al. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol. 2001;154:854-864. (Pubitemid 33000813)
18. Desai MM, Bogardus ST Jr., Williams CS, et al. Development and validation of a risk-adjustment index for older patients: the high-risk diagnoses for the elderly scale. J Am Geriatr Soc. 2002;50:474-481. (Pubitemid 34229403)
19. Reid BC, Alberg AJ, Klassen AC, et al. A comparison of three comorbidity indexes in a head and neck cancer population. Oral Oncol. 2002;38:187-194. (Pubitemid 34164570)
20. Schneeweiss S, Wang PS, Avorn J, et al. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38:1103-1120. (Pubitemid 37048302)
21. Schneeweiss S, Wang PS, Avorn J, et al. Consistency of performance ranking of comorbidity adjustment scores in Canadian and U.S. utilization data. J Gen Intern Med. 2004;19(pt 1):444-450. (Pubitemid 38638905)
22. Grunau GL, Sheps S, Goldner EM, et al. Specific comorbidity risk adjustment was a better predictor of 5-year acute myocardial infarction mortality than general methods. J Clin Epidemiol. 2006;59:274-280. (Pubitemid 43243653)
23. Klabunde CN, Legler JM, Warren JL, et al. A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients. Ann Epidemiol. 2007;17:584-590. (Pubitemid 47096709)
24. Radley DC, Gottlieb DJ, Fisher ES, et al. Comorbidity risk-adjustment strategies are comparable among persons with hip fracture. J Clin Epidemiol. 2008;61:580-587.
25. Quan H, Li B, Couris CM, et al. Updating and validating the charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries. Am J Epidemiol. 2011;173: 676-682.
26. 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;50:903-908. (Pubitemid 27423520)
27. Berlowitz DR, Hoenig H, Cowper DC, et al. Impact of comorbidities on stroke rehabilitation outcomes: does the method matter? Arch Phys Med Rehabil. 2008;89:1903-1906.
28. Li P, Kim MM, Doshi JA. Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality. BMC Health Serv Res. 2010;10:245.
29. Normand SL, Morris CN, Fung KS, et al. Development and validation of a claims based index for adjusting for risk of mortality: the case of acute myocardial infarction. J Clin Epidemiol. 1995;48:229-243.
30. Klabunde CN, Potosky AL, Legler JM, et al. Development of a comorbidity index using physician claims data. J Clin Epidemiol. 2000;53:1258-1267. (Pubitemid 32007842)
31. Baldwin L-M, Klabunde CN, Green P, et al. In search of the perfect comorbidity measure for use with administrative claims data: does it exist? Med Care. 2006;44:745-753. (Pubitemid 44284265)
32. Rochon PA, Katz JN, Morrow LA, et al. Comorbid illness is associated with survival and length of hospital stay in patients with chronic disability. A prospective comparison of three comorbidity indices. Med Care. 1996;34:1093-1101. (Pubitemid 126433208)
33. Jassal SV, Schaubel DE, Fenton SSA. Baseline comorbidity in kidney transplant recipients: a comparison of comorbidity indices. Am J Kidney Dis. 2005;46:136-142. (Pubitemid 40848442)
34. Liu J, Huang Z, Gilbertson DT, et al. An improved comorbidity index for outcome analyses among dialysis patients. Kidney Int. 2010;77: 141-151.
35. Machnicki G, Pinsky B, Takemoto S, et al. Predictive ability of pretransplant comorbidities to predict long-term graft loss and death. Am J Transplant. 2009;9:494-505.
36. O'Connell RL, Lim LL. Utility of the Charlson comorbidity index computed from routinely collected hospital discharge diagnosis codes. Methods Inf Med. 2000;39:7-11. (Pubitemid 30196270)
37. Yan Y, Birman-Deych E, Radford MJ, et al. Comorbidity indices to predict mortality from Medicare data: results from the national registry of atrial fibrillation. Med Care. 2005;43:1073-1077. (Pubitemid 41587592)
38. MelfiC, Holleman E, Arthur D, et al. Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data. J Clin Epidemiol. 1995;48:917-926.
39. Holman CDAJ, Preen DB, Baynham NJ, et al. A multipurpose comorbidity scoring system performed better than the Charlson index. J Clin Epidemiol. 2005;58:1006-1014. (Pubitemid 41316443)
40. Nuttall M, van der Meulen J, Emberton M. Charlson scores based on ICD-10 administrative data were valid in assessing comorbidity in patients undergoing urological cancer surgery. J Clin Epidemiol. 2006;59:265-273. (Pubitemid 43243652)
41. Myers RP, Quan H, Hubbard JN, et al. Predicting in-hospital mortality in patients with cirrhosis: results differ across risk adjustment methods. Hepatology. 2009;49:568-577.
42. van Walraven C, Austin PC, Jennings A, et al. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009;47:626-633.
43. Armitage JN, van der Meulen JH, Royal G, College of Surgeons Comorbidity Consensus. Identifying co-morbidity in surgical patients using administrative data with the Royal College of Surgeons Charlson Score. Br J Surg. 2010;97:772-781.
44. Lix LM, Quail J, Teare G, et al. Performance of comorbidity measures for predicting outcomes in population-based osteoporosis cohorts. Osteoporosis Int. 2011;22:2633-2643.
45. 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;37:1128-1139.
46. Stukenborg GJ, Wagner DP, Connors AF Jr. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care. 2001;39:727-739. (Pubitemid 33610097)
47. Preen DB, Holman CD, Spilsbury K, et al. Length of comorbidity lookback period affected regression model performance of administrative health data. J Clin Epidemiol. 2006;59:940-946. (Pubitemid 44163421)
48. Fleming ST, Pearce KA, McDavid K, et al. The development and validation of a comorbidity index for prostate cancer among Black men. J Clin Epidemiol. 2003;56:1064-1075. (Pubitemid 37386333)
49. Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe populations and predict costs. Health Care Financ Rev. 2000;21:7-28.
50. Thombs BD, Singh VA, Halonen J, et al. The effects of preexisting medical comorbidities on mortality and length of hospital stay in acute burn injury: evidence from a national sample of 31,338 adult patients. Ann Surg. 2007;245:629-634. (Pubitemid 46555668)
51. Sharabiani MTA, Vermeulen R, Scoccianti C, et al. Immunologic profile of excessive body weight. Biomarkers. 2011;16:243-251.
52. Syed Z, Rubinfeld I, Patton JH, et al. Using procedural codes to supplement risk adjustment: a nonparametric learning approach. J Am Coll Surg. 2011;212:1086-U198.
53. Ghali WA, Hall RE, Rosen AK, et al. Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data. J Clin Epidemiol. 1996;49:273-278. (Pubitemid 26149646)
54. Polanczyk CA, Rohde LE, Philbin EA, et al. A new casemix adjustment index for hospital mortality among patients with congestive heart failure. Med Care. 1998;36:1489-1499. (Pubitemid 128456368)
55. Ward MM. Development and testing of a systemic lupus-specific risk adjustment index for in-hospital mortality. J Rheumatol. 2000;27: 1408-1413. (Pubitemid 30354036)
56. Wang PS, Walker A, Tsuang M, et al. Strategies for improving comorbidity measures based on Medicare and Medicaid claims data. J Clin Epidemiol. 2000;53:571-578. (Pubitemid 30394506)
57. Tu JV, Austin PC, Walld R, et al. Development and validation of the Ontario acute myocardial infarction mortality prediction rules. J Am Coll Cardiol. 2001;37:992-997. (Pubitemid 32217595)
58. Johnston JA, Wagner DP, Timmons S, et al. Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients. Med Care. 2002;40:929-940.
59. Ash AS, Posner MA, Speckman J, et al. Using claims data to examine mortality trends following hospitalization for heart attack in Medicare. Health Serv Res. 2003;38:1253-1262. (Pubitemid 37288496)
60. Atherly A, Fink AS, Campbell DC, et al. Evaluating alternative riskadjustment strategies for surgery. Am J Surg. 2004;188:566-570. (Pubitemid 39491106)
61. Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42:355-360.
62. Quan H, Fink AS, Campbell DC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130-1139. (Pubitemid 41587600)
63. Martins M, Blais R. Evaluation of comorbidity indices for inpatient mortality prediction models. J Clin Epidemiol. 2006;59:665-669. (Pubitemid 43841851)
64. So L, Evans D, Quan H. ICD-10 coding algorithms for defining comorbidities of acute myocardial infarction. BMC Health Serv Res. 2006;6:161.
65. Ho KM, Finn J, Knuiman M, et al. Combining multiple comorbidities with Acute Physiology Score to predict hospital mortality of critically ill patients: a linked data cohort study. Anaesthesia. 2007;62:1095-1100. (Pubitemid 47556427)
66. Kurichi JE, Stineman MG, Kwong PL, et al. Assessing and using comorbidity measures in elderly veterans with lower extremity amputations. Gerontology. 2007;53:255-259. (Pubitemid 47294448)
67. Sundararajan V, Quan H, Halfon P, et al. Cross-national comparative performance of three versions of the ICD-10 Charlson index. Med Care. 2007;45:1210-1215.
68. Li B, Evans D, Faris P, et al. Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res. 2008;8:12.
69. Tang J, Wan JY, Bailey JE. Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive Medicaid population. Stroke. 2008;39:1938-1944.
70. Zhu H, Hill MD. Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality. Neurology. 2008;71:283-287.
71. Furlan JC, Kattail D, Fehlings MG. The impact of co-morbidities on age-related differences in mortality after acute traumatic spinal cord injury. J Neurotrauma. 2009;26:1361-1367.
72. Chu Y-T, Ng Y-Y, Wu S-C. Comparison of different comorbidity measures for use with administrative data in predicting short- and longterm mortality. BMC Health Serv Res. 2010;10:140.
73. Martins M. Use of comorbidity measures to predict the risk of death in Brazilian in-patients. Rev Saude Publica. 2010;44:448-456.