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Efficiency of International Classification of Diseases, Ninth Revision, Billing Code Searches to Identify Emergency Department Visits for Blood or Body Fluid Exposures through a Statewide Multicenter Database

Published online by Cambridge University Press:  02 January 2015

Lisa M. Rosen ,
Tao Liu  and
Roland C. Merchant
Lisa M. Rosen
Affiliation:
Department of Community Health, Alpert Medical School of Brown University, Providence, Rhode Island Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island
Tao Liu
Affiliation:
Department of Community Health, Alpert Medical School of Brown University, Providence, Rhode Island
Roland C. Merchant*
Affiliation:
Department of Community Health, Alpert Medical School of Brown University, Providence, Rhode Island Department of Emergency Medicine, Alpert Medical School of Brown University, Providence, Rhode Island
*
Department of Emergency Medicine, Rhode Island Hospital, 593 Eddy Street, Claverick Building, Providence, RI 02903 ([email protected])
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Abstract

Background.

Blood and body fluid exposures are frequently evaluated in emergency departments (EDs). However, efficient and effective methods for estimating their incidence are not yet established.

Objective.

Evaluate the efficiency and accuracy of estimating statewide ED visits for blood or body fluid exposures using International Classification of Diseases, Ninth Revision (ICD-9), code searches.

Design.

Secondary analysis of a database of ED visits for blood or body fluid exposure.

Setting.

EDs of 11 civilian hospitals throughout Rhode Island from January 1, 1995, through June 30, 2001.

Patients.

Patients presenting to the ED for possible blood or body fluid exposure were included, as determined by prespecified ICD-9 codes.

Methods.

Positive predictive values (PPVs) were estimated to determine the ability of 10 ICD-9 codes to distinguish ED visits for blood or body fluid exposure from ED visits that were not for blood or body fluid exposure. Recursive partitioning was used to identify an optimal subset of ICD-9 codes for this purpose. Random-effects logistic regression modeling was used to examine variations in ICD-9 coding practices and styles across hospitals. Cluster analysis was used to assess whether the choice of ICD-9 codes was similar across hospitals.

Results.

The PPV for the original 10 ICD-9 codes was 74.4% (95% confidence interval [CI], 73.2%–75.7%), whereas the recursive partitioning analysis identified a subset of 5 ICD-9 codes with a PPV of 89.9% (95% CI, 88.9%–90.8%) and a misclassification rate of 10.1%. The ability, efficiency, and use of the ICD-9 codes to distinguish types of ED visits varied across hospitals.

Conclusions.

Although an accurate subset of ICD-9 codes could be identified, variations across hospitals related to hospital coding style, efficiency, and accuracy greatly affected estimates of the number of ED visits for blood or body fluid exposure.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 33 , Issue 6 , June 2012 , pp. 581 - 588
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012 

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References

1. Varas-Lorenzo, C, Castellsague, J, Stang, MR, Tomas, L, Aguado, J, Perez-Gutthann, S. Positive predictive value of ICD-9 codes 410 and 411 in the identification of cases of acute coronary syndromes in the Saskatchewan Hospital Automated Database. Pharmacoepidemiol Drug Saf 2008; 17:842–852.Google Scholar
2. White, RH, Garcia, M, Sadeghi, B, et al. Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States. Thromb Res 2010; 126(1):61–67.Google Scholar
3. Semins, MJ, Trock, BJ, Matlaga, BR. Validity of administrative coding in identifying patients with upper urinary tract calculi. J Urol 2010;184(1):190–192.Google Scholar
4. Chibnik, LB, Massarotti, EM, Costenbader, KH. Identification and validation of lupus nephritis cases using administrative data. Lupus 2010;19(6):741–743.Google Scholar
5. Clark, S, Gaeta, TD, Kamarthi, GS, Camargo, CA. ICD-9 CM coding of emergency department visits for food and insect sting allergy. Ann Epidemiol 2006;16(9):696–700.Google Scholar
6. Leibson, CL, Naessens, JM, Brown, RD, Whisnant, JP. Accuracy of hospital discharge abstracts for identifying stroke. Stroke 1994; 25(12):2348–2355.Google Scholar
7. Chewning, SJ, Nussman, DS, Griffo, ML, Kiebzak, GM. Health care information processing: how accurate are the data? J South Orthop Assoc 1997;6(1):8–16.Google Scholar
8. O'Malley, KJ, Cook, KF, Price, MD, Wildes, KR, Hurdle, JF, Ashton, CM. Measuring diagnoses: ICD code accuracy. Health Serv Res 2005;40(5):1620–1639.Google Scholar
9. Farzandipour, M, Sheikhtaheri, A. Evaluation of factors influencing accuracy of principal procedure coding based on ICD-9-CM: an Iranian study. Perspect Health Info Manag 2009;6:5.Google Scholar
10. Patkar, NM, Curtis, JR, Teng, GG, et al. Administrative codes combined with medical records based criteria accurately identified bacterial infections among rheumatoid arthritis patients. J Clin Epidemiol 2009;62:321–327.Google Scholar
11. Merchant, RC, Becker, BM, Mayer, KH, Fuerch, J, Schreck, B. Emergency department blood or body fluid exposure evaluations and HIV postexposure prophylaxis usage. Acad Emerg Med 2003;10(12):1345–1353.Google Scholar
12. Merchant, RC, Mayer, KH, Becker, BM, Delong, AK, Hogan, JW. Predictors of the initiation of HIV postexposure prophylaxis in Rhode Island emergency departments. AIDS Patient Care STDS 2008;22(1):41–52.Google Scholar
13. Merchant, RC, Katzen, JB, Mayer, KH, Becker, BM. Emergency department evaluations of non-percutaneous blood or body fluid exposures during cardiopulmonary resuscitation. Prehosp Disaster Med 2007;22(4):331–334.Google Scholar
14. Merchant, RC, TC, Lau, Liu, T, Mayer, KH, Becker, BM. Adult sexual assault evaluations at Rhode Island emergency departments, 1995-2001. J Urban Health 2008;86(1):43–53.Google Scholar
15. Merchant, RC, Nettieton, JE, Mayer, KH, Becker, BM. Blood or body fluid exposures and HIV postexposure prophylaxis utilization among first responders. Prehosp Emerg Care 2009;13(1): 6–13.Google Scholar
16. Merchant, RC, Chee, KJ, Liu, T, Mayer, KH. Incidence of visits for health care worker blood or body fluid exposures and HIV postexposure prophylaxis provision at Rhode Island emergency departments. J Acquir Immune Defic Syndr 2008;47(3):358–368.Google Scholar
17. Merchant, RC, Kelly, ET, Mayer, KH, Becker, BM, Duffy, SJ, Pugatch, DL. Compliance in Rhode Island emergency departments with American Academy of Pediatrics recommendations for adolescent sexual assaults. Pediatrics 2008; 121 (6): 1660–1667.Google Scholar
18. Merchant, RC, Nettieton, JE, Mayer, KH, Becker, BM. HIV postexposure prophylaxis among police and corrections officers. Oc-cup Med 2008;58(7):502–505.Google Scholar
19. Breiman, L, Friedman, JH, Olshen, RA, Stone, CJ. Classification and Regression Trees. Belmont, CA: Wadsworth, 1984.Google Scholar
20. U.S. Census Bureau. Metropolitan and micropolitan statistical areas. November 2008. http://www.census.gov/population/metro.html.Google Scholar
21. Stram, DO, Lee, JW. Variance components testing in the longitudinal mixed effects model. Biometrics 1994;50(4):1171–1177.Google Scholar
22. Johnson, RA, Wichern, DW. Applied Multivariate Statistical Analysis. 6th ed. New York: Prentice Hall, 2007.Google Scholar
23. Dingemans, PM. ICD-9-CM classification coding in psychiatry. J Clin Psychol 1990;46(2):161–168.Google Scholar
24. Throckmorton, TW, Dunn, W, Holmes, T, Kuhn, JT. Intraobserver and interobserver agreement of International Classification of Diseases, Ninth Revision codes in classifying shoulder instability. J Shoulder Elbow Surg 2009;18(2):199–203.Google Scholar