Performance of comorbidity measures to predict stroke and death in a community-dwelling, hypertensive medicaid population

Stroke

Volumn 39, Issue 7, 2008, Pages 1938-1944

  Tang, Jun ^a   Wan, Jim Y ^a   Bailey, James E ^a,b  

^a UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^b NONE   (United States)

Author keywords

Hypertension; Mortality; Outcome; Outcome assessment; Stroke

Indexed keywords

ANTIHYPERTENSIVE AGENT;

ADULT; AGED; ARTICLE; CALCULATION; CARDIOVASCULAR RISK; CEREBROVASCULAR ACCIDENT; COHORT ANALYSIS; COMORBIDITY; CONTROLLED STUDY; DEATH; DRUG UTILIZATION; FEMALE; FOLLOW UP; HEALTH CARE UTILIZATION; HUMAN; HYPERTENSION; MAJOR CLINICAL STUDY; MALE; MEDICAID; OUTCOME ASSESSMENT; PREDICTION; PRIORITY JOURNAL; PROPORTIONAL HAZARDS MODEL; RETROSPECTIVE STUDY; RISK ASSESSMENT; SCORING SYSTEM; STROKE; VALIDATION STUDY; ADOLESCENT; MIDDLE AGED; MORTALITY; PATHOLOGY; RISK FACTOR; UNITED STATES;

ADOLESCENT; ADULT; AGED; ANTIHYPERTENSIVE AGENTS; COMORBIDITY; FEMALE; HUMANS; HYPERTENSION; MALE; MEDICAID; MIDDLE AGED; RETROSPECTIVE STUDIES; RISK FACTORS; STROKE; UNITED STATES;

EID: 47649119672     PISSN: 00392499     EISSN: None     Source Type: Journal    
DOI: 10.1161/STROKEAHA.107.504688     Document Type: Article

References (46)

1. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36:8-27.
2. Byles JE, D'Este C, Parkinson L, O'Connell R, Treloar C. Single index of multimorbidity did not predict multiple outcomes. J Clin Epidemiol. 2005;58:997-1005.
3. Holman CD, Preen DB, Baynham NJ, Finn JC, Semmens JB. A multipurpose comorbidity scoring system performed better than the Charlson Index. J Clin Epidemiol. 2005;58:1006-1014.
4. Schneeweiss S, Seeger JD, Maclure M, Wang PS, Avorn J, Glynn RJ. Performance of comorbidity scores to control for confounding in epide-miologic studies using claims data. Am J Epidemiol. 2001;154:854-864.
5. 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.
6. Needham DM, Scales DC, Laupacis A, Pronovost PJ. 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.
7. Schneeweiss S, Maclure M. Use of comorbidity scores for control of confounding in studies using administrative databases. Int J Epidemiol. 2000;29:891-898.
8. 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.
9. 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.
10. Selim AJ, Berlowitz DR, Fincke G, Rosen AK, Ren XS, Christiansen CL, Cong Z, Lee A, Kazis L. Risk-adjusted mortality rates as a potential outcome indicator for outpatient quality assessments. Med Care. 2002; 40:237-245.
11. 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.
12. 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.
13. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130-1139.
14. Feigin VL, Carter K. Editorial comment - stroke incidence studies one step closer to the elusive gold standard? Stroke. 2004;35:2045-2047.
15. Bullano MF, Kamat S, Willey VJ, Barlas S, Watson DJ, Brenneman SK. Agreement between administrative claims and the medical record in identifying patients with a diagnosis of hypertension. Med Care. 2006; 44:486-490.
16. Robinson JR, Young TK, Roos LL, Gelskey DE. Estimating the burden of disease. Comparing administrative data and self-reports. Med Care. 1997;35:932-947.
17. Rosamond WD, Folsom AR, Chambless LE, Wang CH, McGovern PG, Howard G, Copper LS, Shahar E. Stroke incidence and survival among middle-aged adults: 9-year follow-up of the Atherosclerosis Risk in Communities (ARIC) cohort. Stroke. 1999;30:736-743.
18. Benesch C, Witter DM Jr, Wilder AL, Duncan PW, Samsa GP, Matchar DB. Inaccuracy of the International Classification of Diseases (ICD-9-CM) in identifying the diagnosis of ischemic cerebrovascular disease. Neurology. 1997;49:660-664.
19. Leibson CL, Naessens JM, Brown RD, Whisnant JP. Accuracy of hospital discharge abstracts for identifying stroke. Stroke. 1994;25:2348 -2355.
20. Federspiel CF, Ray WA, Schaffner W. Medicaid records as a valid data source: the Tennessee experience. Med Care. 1976;14:166-172.
21. Reker DM, Hamilton BB, Duncan PW, Yeh SC, Rosen A. Stroke: who's counting what? J Rehabil Res Dev. 2001;38:281-289.
22. Williams GR, Jiang JG, Matchar DB, Samsa GP. Incidence and occurrence of total (first-ever and recurrent) stroke. Stroke. 1999;30: 2523-2528.
23. Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke. 2002;33:2465-2470.
24. Schneeweiss S, Wang PS, Avorn J, Glynn RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res. 2003;38:1103-1120.
25. Goldstein LB, Samsa GP, Matchar DB, Horner RD. Charlson Index comorbidity adjustment for ischemic stroke outcome studies. Stroke. 2004;35:1941-1945.
26. Major cardiovascular events in hypertensive patients randomized to dox-azosin vs chlorthalidone: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT). ALLHAT Collaborative Research Group. JAMA. 2000;283:1967-1975.
27. Prevention of stroke by antihypertensive drug treatment in older persons with isolated systolic hypertension. Final results of the Systolic Hypertension in the Elderly Program (SHEP). SHEP Cooperative Research Group. JAMA. 1991;265:3255-3264.
28. Curb JD, Pressel SL, Cutler JA, Savage PJ, Applegate WB, Black H, Camel G, Davis BR, Frost PH, Gonzalez N, Guthrie G, Oberman A, Rutan GH, Stamler J. Effect of diuretic-based antihypertensive treatment on cardiovascular disease risk in older diabetic patients with isolated systolic hypertension. Systolic Hypertension in the Elderly Program Cooperative Research Group. JAMA. 1996;276:1886-1892.
29. Greenland P, Knoll MD, Stamler J, Neaton JD, Dyer AR, Garside DB, Wilson PW. Major risk factors as antecedents of fatal and nonfatal coronary heart disease events. JAMA. 2003;290:891-897.
30. Quan H, Parsons GA, Ghali WA. Validity of information on comorbidity derived rom ICD-9-CCM administrative data. Med Care. 2002;40: 675-685.
31. Goff DC Jr, Pandey DK, Chan FA, Ortiz C, Nichaman MZ. Congestive heart failure in the United States: is there more than meets the I(CD code)? The Corpus Christi Heart Project. Arch Intern Med. 2000;160: 197-202.
32. Go AS, Hylek EM, Borowsky LH, Phillips KA, Selby JV, Singer DE. Warfarin use among ambulatory patients with nonvalvular atrial fibrillation: the anticoagulation and risk factors in atrial fibrillation (ATRIA) study. Ann Intern Med. 1999;131:927-934.
33. Hill MD, Yiannakoulias N, Jeerakathil T, Tu JV, Svenson LW, Schopflocher DP. The high risk of stroke immediately after transient ischemic attack: a population-based study. Neurology. 2004;62:2015-2020.
34. Marini C, Baldassarre M, Russo T, De Santis F, Sacco S, Ciancarelli I, Carolei A. Burden of first-ever ischemic stroke in the oldest old: evidence from a population-based study. Neurology. 2004;62:77-81.
35. Gil-Nunez AC, Vivancos-Mora J. Blood pressure as a risk factor for stroke and the impact of antihypertensive treatment. Cerebrovasc Dis. 2005;20(suppl 2):40-52.
36. Hanon O, Seux ML, Lenoir H, Rigaud AS, Forette F. Prevention of dementia and cerebroprotection with antihypertensive drugs. Curr Hypertens Rep. 2004;6:201-207.
37. Cox D. Regression models and life tables. J R Stat Soc. 1972;B34: 187-220.
38. Bozdogan H. Model selection and Akaike's information criterion: the general theory and its analytical extensions. Psychometrika. 1987;52: 345-370.
39. de Leeuw J. Introduction to Akaike (1973) information theory and an extension of the maximum likelihood principle. In: Kotz S, Johnson N, eds. Breakthroughs in Statistics, vol 1. London: Springer-Verlag; 1992: 599-609.
40. Anderson D, Burnham K. General strategies for the analysis of ringing data. Bird Study. 1999;46(suppl):S261-270.
41. Statistical Analysis System (SAS) [computer program]. Version 9.1. Cary, NC: SAS Institute; 2003.
42. van Doorn C, Bogardus ST, Williams CS, Concato J, Towle VR, Inouye SK. Risk adjustment for older hospitalized persons: a comparison of two methods of data collection for the Charlson index. J Clin Epidemiol. 2001;54:694-701.
43. Melfi C, Holleman E, Arthur D, Katz B. Selecting a patient characteristics index for the prediction of medical outcomes using administrative claims data. J Clin Epidemiol. 1995;48:917-926.
44. Wang PS, Walker A, Tsuang M, Orav EJ, Levin R, Avorn J. Strategies for improving comorbidity measures based on Medicare and Medicaid claims data. J Clin Epidemiol. 2000;53:571-578.
45. Burnham K, Anderson D. Multimodel inference: understanding AIC and BIA in model selection. Amsterdam Workshop on Model Selection; 2004.
46. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61:92-105.