Positive Predictive Values of ICD-9 Codes to Identify Patients With Stroke or TIA | Page 3
Published Online: February 26, 2014
Kari L. Olson, BSc(Pharm), PharmD; Michele D. Wood, PharmD; Thomas Delate, PhD; Lisa J. Lash, PharmD; Jon Rasmussen, PharmD; Anne M. Denham, PharmD; and John A. Merenich, MD
We assessed ICD-9 code 435 (TIA) because treatment guidelines are similar for patients who experienced an ischemic stroke and TIA is an important risk factor for stroke, with 90-day risks reported as high as 17%.2 Interestingly, we found that code 435 was one of the most prevalent codes, capturing 1329 patients who had their cerebral event confirmed, and its PPVs were relatively high across settings (>84%) indicating potential utility for this code if identification of patients with probable TIA is desirable.
Historically, ICD-9 codes for hemorrhagic stroke have reported higher PPVs than ischemic stroke codes.14,15 However, we found that, overall, hemorrhagic stroke codes had lower PPVs than ischemic stroke codes, perhaps because there were fewer patients with hemorrhagic stroke codes. We did find that inpatient hemorrhagic codes had higher PPVs than outpatient hemorrhagic codes suggesting that inpatient hemorrhagic events are coded more accurately.
It is not surprising that ICD-9 codes 432 “other and nonspecified intracranial hemorrhage” and 437 “other and illdefined cerebrovascular disease” performed poorly based on their definitions. For example 437 includes a variety of conditions under the umbrella of “ill defined,” including cerebral atherosclerosis, chronic cerebral ischemia, hypertensive encephalopathy, non-ruptured cerebral aneurysm, moyamoya disease, nonpyogenic thrombosis of intracranial venous sinus, and transient global amnesia.10
Overall, the inclusion of health service indicators such as diagnostic imaging and neurology visits did not change appreciably the PPV estimates, nor did evaluating code 434.X1 with specific infarction terms. Code 433.X1 with specific mention of infarction did increase the PPV appreciably but identified only a small number of patients. Inclusion of prescription antiplatelet exposure slightly improved PPVs. We identified no other studies that employed health service indicators to assess their effects on PPVs.
The ideal cerebrovascular ICD-9 code would capture a large number of patients and maintain a high PPV. We found that codes 434 and V12.54 came closest to ideal when recorded in the outpatient setting. Code 434.XX is likely highly predictive because the operating definition is specific to thrombosis or embolism of cerebral arteries.10 The high predictability of V12.54 is interesting because it is a patient self-reported code. To our knowledge, this is the first study to evaluate the accuracy of ICD-9 status code V12.54, which proved to be highly effective capturing many patients and moderately predictive across the inpatient and outpatient settings. We recommend that health plans wishing to identify administratively patients with a history of stroke or TIA utilize ICD-9 codes 433.X1, 434.XX, 436.XX, and V12.54. These codes demonstrated reasonable predictive abilities across care settings. Conversely, we caution plans from utilizing administratively codes with a low PPV as this may result in a large number of false positives.
Our study was not without limitations. We recognize that PPVs are subject to disease prevalence, however, we were unable to determine sensitivities and specificities as we did not identify or review patients who did not have the specified stroke codes. Given the large number of patients with stroke codes reviewed for this study, the number of non-cases we would have had to review to provide meaningful data would have been too resource intensive. We used a manual chart review process that relied on the accuracy of care and health services use of documentation in the EMR. There may also have been temporal changes in coding practices. The study was conducted at only 1 health plan and coding practices and accuracy may vary across institutions and health plans; thus, our findings may not be applicable at all institutions/ health plans. Similarly, coding and charting practices may have differed between individual physicians, whereby some physicians were more accurate and/or comprehensive.
While we have no evidence of differential coding and charting, we enhanced our chart review process by using a standardized chart abstraction tool to improve consistency and trained chart reviewers who had an inter-rater reliability consistent with a high level of agreement. Additionally, the duration of a member’s enrollment in KPCO may have impacted our ability to confirm strokes. Patients with longer enrollments would have had a greater opportunity to be diagnosed in multiple care settings and/or had medical information to confirm stroke.
The ideal cerebrovascular ICD-9 code would capture a large number of patients and maintain a high PPV. Unfortunately, the majority of cerebrovascular ICD-9 codes lack this ability, making it difficult to accurately identify stroke patients without manual medical chart review. Our results indicate that several stroke codes (433.X1, 434, and V12.54 in particular) had relatively high PPVs across care settings. However, caution should be used with all codes, and they may be best suited to identify patients with a higher likelihood of having had a true stroke. This could provide a means to minimize the number of confirmatory chart reviews that would be needed to accurately identify patients with a true stroke.
Author Affiliations: Pharmacy Department, Kaiser Permanente Colorado, Aurora, CO (KLO, MDW, LJL, AMD, JR); University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, CO (KLO, TD, JR, AMD); Department of Endocrinology, Colorado Permanente Medical Group and the University of Colorado Health Sciences Center Denver, CO (JAM).
Funding Source: Kaiser Permanente Colorado.
Author Disclosures: The authors (KLO, MDW, TD, LJL, JR, AMD, JAM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (TD, LJL, JR, AMD); acquisition of data (MDW, TD, JR); analysis and interpretation of data (MDW, TD, LJL, JR, AMD); drafting of the manuscript (MDW, LJL, JR); critical revision of the manuscript for important intellectual content (LJL, JR, AMD); statistical analysis (TD); supervision (JR).
Address correspondence to: Thomas Delate, PhD, 16601 E Centretech Parkway, Aurora, CO 80011. E-mail: email@example.com.
1. Roger VL, Go AS, Lloyd-Jones D, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012;125(1):e2-e220.
2. Furie KL, Kasner SE, Adams RJ, et al. Guidelines for the prevention of stroke in patients with stroke or transient ischemic attack: a guideline for healthcare professionals from the American Heart Association/ American Stroke Association. Stroke. 2011;42(1):227-276.
3. Saposnik G, Goodman SG, Leiter LA, et al. Applying the evidence: do patients with stroke, coronary artery disease, or both achieve similar treatment goals? Stroke. 2009;40(4):1417-1424.
4. Asberg S, Henriksson KM, Farahmand B, et al. Ischemic stroke and secondary prevention in clinical practice: a cohort study of 14 529 patients in the Swedish stroke register. Stroke. 2010;41(7):1338-1342.
5. Leibson CL, Naessens JM, Brown RD, Whisnant JP. Accuracy of hospital discharge abstracts for identifying stroke. Stroke. 1994;25(12): 2348-2355.
6. Ellekjaer H, Holmen, Krüger O, Terent, A. Identification of incident stroke in Norway: hospital discharge data compared with a population based stroke register. Stroke. 1999(1);30:56-60.
7. Krarup LH, Boysen G, Janjua H, Prescott E, Truelsen T. Validity of stroke diagnosis in a national registry of patients. Neuroepidemiology. 2007;28(3):150-154.
8. Stegmayr B, Asplund K. Measuring stroke in the population: quality of routine statistics in comparison with a population-based stroke registry. Neuroepidemiology. 1992;11(4-6):204-213.
9. Derby CA, Lapane KL, Feldman HA, Carleton RA. Possible effect of DRGs on the classification of stroke: implications for epidemiological surveillance. Stroke. 2001;32(7):1487-1491.
10. Benesch C, Witter DM Jr, Wilder AL, Duncan PW, Samsa GP, Matchar DB. Inaccuracy of the international classification of diseases (ICD-9) in identifying the diagnosis of ischemic cerebrovascular disease. Neurology. 1997;49(3):660-664.
11. Goldstein LB. Accuracy of ICD-9-CM coding for identification of patients with acute ischemic stroke: effect of modifier codes. Stroke. 1998;29(8):1602-1604.
12. Stineman MG, Granger CV, Hamilton BB, Melvin JL, Fiedler IG. Specificity of ICD-9 CM coding practices for stroke rehabilitation. Am J Phys Med Rehabil. 1993;72(5):318-324.
13. Golomb MR, Garg BP, Saha C, Williams LS. Accuracy and yield of ICD-9 codes for identifying children with ischemic stroke. Neurology. 2006;67(11):2053-2055.
14. Roumie CL, Mitchel E, Gideon PS, Varas-Lorenzo C, Castellsague J, Griffin MR. Validation of ICD-9 codes with a high positive predictive value for incident strokes resulting in hospitalization using Medicaid health data. Pharmacoepidemiol Drug Saf. 2008;17(1):20-26.
15. Tirschwell DL, Longstreth WT Jr. Validating administrative data in stroke research. Stroke. 2002;33(10):2465-2470.
16. Johnsen SP, Overvad K, Sørensen HT, Tjønneland A, Husted SE: Predictive value of stroke and transient ischemic attack discharge diagnoses in The Danish National Registry of Patients. J Clin Epidemiol. 2002;55(6):602-607.
17. Kramer MS, Feinstein AR. Clinical biostatistics: LIV: the biostatistics of concordance. Clin Pharmacol Ther. 1981;29(1):111-123.
18. Petty GW, Brown RD Jr, Whisnant JP, Sicks JD, O’Fallon WM, Wiebers DO. Survival and recurrence after first cerebral infarction: a population-based study in Rochester, Minnesota, 1975 through 1989. Neurology. 1998;50(1):208-216.
19. Fleiss JL, Levin B, Paik MC. Statistical Methods for Rates and Proportions. 3rd ed. Hoboken, NJ: John Wiley & Sons, Inc; 2003