Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors

Medical Care

Volumn 43, Issue 5, 2005, Pages 480-485

  Birman Deych, Elena ^a   Waterman, Amy D ^a   Yan, Yan ^a   Nilasena, David S ^b   Radford, Martha J ^c   Gage, Brian F ^a  

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Centers for Medicare and Medicaid Services   (United States)

^c YALE NEW HAVEN HEALTH SYSTEM   (United States)

Author keywords

Administrative data; Ischemic heart disease; Medicare claims analysis; Risk adjustment; Risk factors

Indexed keywords

ADULT; AGED; ARTERY EMBOLISM; BRAIN HEMORRHAGE; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; CEREBROVASCULAR ACCIDENT; CLASSIFICATION; CORONARY ARTERY DISEASE; DEEP VEIN THROMBOSIS; DIABETES MELLITUS; DIAGNOSTIC ACCURACY; FEMALE; HEART ATRIUM FIBRILLATION; HEART FAILURE; HUMAN; HYPERTENSION; INTERNATIONAL CLASSIFICATION OF DISEASES; MAJOR CLINICAL STUDY; MALE; MEDICARE; PREDICTION AND FORECASTING; PREVALENCE; REGISTER; REVIEW; RISK ASSESSMENT; RISK FACTOR; SENSITIVITY AND SPECIFICITY; STROKE; TRANSIENT ISCHEMIC ATTACK; UNITED STATES; VALVULAR HEART DISEASE;

ADULT; AGED; AGED, 80 AND OVER; ATRIAL FIBRILLATION; CARDIOVASCULAR DISEASES; CEREBROVASCULAR ACCIDENT; CROSS-SECTIONAL STUDIES; FEMALE; HUMANS; INTERNATIONAL CLASSIFICATION OF DISEASES; MALE; MEDICARE PART A; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; REGISTRIES; RISK ASSESSMENT; RISK FACTORS; SENSITIVITY AND SPECIFICITY; UNITED STATES;

EID: 18844409393     PISSN: 00257079     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.mlr.0000160417.39497.a9     Document Type: Review

References (29)

1. DiSalvo TG, Normand SL, Hauptman PJ, et al. Pitfalls in assessing the quality of care for patients with cardiovascular disease. Am J Med. 2001;111:297-303.
2. Hsia DC, Krashat WM, Fagan AB, et al. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system. N Engl J Med. 1988;318:352-355.
3. Hsia DC, Ahern CA, Ritchie BP, et al. Medicare reimbursement accuracy under the prospective payment system, 1985 to 1988. JAMA. 1992;268:896-899.
4. Iezzoni LI, Foley SM, Daley J, et al. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality? JAMA. 1992;267:2197-2203.
5. Goldstein LB. Accuracy of ICD-9-CM-CM coding for the identification of patients with acute ischemic stroke: effect of modifier codes. Stroke. 1998;29:1602-1604.
6. Petersen LA, Wright S, Normand SL, et al. Positive predictive value of the diagnosis of acute myocardial infarction in an administrative database. J Gen Intern Med. 1999;14:555-558.
7. Humphries KH, Rankin JM, Carere RG, et al. Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review? J Clin Epidemiol. 2000;53:343-349.
8. Szeto HC, Coleman RK, Gholami P, et al. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manage Care. 2002;8:37-43.
9. Fisher ES, Whaley FS, Krushat WM, et al. The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992;82:243-248.
10. Deyo RA, Cherkin DC, Ciol M. Adapting a clinical comorbidity index for use with ICD-9-CM-CM administrative databases. J Clin Epidemiol. 1992;45:613-619.
11. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care. 1998;36:8-27.
12. Romano P, Roos L, Luft H, et al. A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. J Clin Epidemiol. 1994;47:249-260.
13. Ellekjaer H, Holmen J, Kruger O, Terent A. Identification of incident stroke in Norway: hospital discharge data compared with a population-based stroke register. Stroke. 1999;30:56-60.
14. Tanne D, Yaari S, Goldbourt U. Risk profile and prediction of long-term ischemic stroke mortality: a 21-year follow-up in the Israeli Ischemic Heart Disease (IIHD) Project. Circulation. 1998;98:1365-1371.
15. White RH, Zhou H, Romano PS. Length of hospital stay for treatment of deep venous thrombosis and the incidence of recurrent thromboembolism. Arch Intern Med. 1998;158:1005-1010.
16. Goff DC Jr, Pandey DK, Chan FA, et al. 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.
17. Bernstein CN, Blanchard JF, Houston DS, et al. The incidence of deep venous thrombosis and pulmonary embolism among patients with inflammatory bowel disease: a population-based cohort study. Thromb Haemost. 2001;85:430-434.
18. Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke. 2002;33:2465-2470.
19. Park H, Safdar N, Schmidt H. Decline in mortality of coronary heart disease among whites and blacks in Wisconsin 1979-1998. WMJ. 2002;101:23-27.
20. Clinical Classifications Software (ICD-9-CM-CM): Agency for Healthcare Research and Quality (AHRQ); 2003. Available at: http://www.h-cup-us.ahrq.gov/ toolssoftware/ccs/ccs.jsp. Accessed February 22, 2005.
21. Jaeschke R, Guyatt G, Sackett DL. Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? Evidence-Based Medicine Working Group. JAMA. 1994;271:389-391.
22. Woolson RF, Clarke WR. Statistical Methods for the Analysis of Biomedical Data. New York: John Wiley & Sons, 2002.
23. Fleiss J. Statistical Methods for Rates and Proportions. 2nd ed. New York: John Wiley & Sons; 1981.
24. Simel DL, Samsa GP, Matchar DB. Likelihood ratios with confidence: sample size estimation for diagnostic test studies. J Clin Epidemiol. 1991;44:763-770.
25. Efron B, Tibshirani R. An Introduction to the Bootstrap. 57 vol. New York: Chapman & Hall; 1993.
26. SAS Analytical Software. 9.0 ed. Cary, NC: SAS Institute Inc; 2002.
27. Rothman KJ, Greenland S. Modern Epidemiology. 2nd ed. Philadelphia, PA: Lippincott-Raven Publishers; 1998.
28. MacIntyre CR, Ackland MJ, Chandraraj EJ. Accuracy of injury coding in Victorian hospital morbidity data. Aust N Z J Public Health. 1997;21:779-783.
29. Sox HJ, Blatt M, Higgins M, Marton K. Medical Decision Making. Stoneham, MA: Butterworth; 1988.