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The American Journal of Managed Care February 2014
Physician Financial Incentives and Care for the Underserved in the United States
Alyna T. Chien, MD, MS; Marshall H. Chin, MD, MPH; G. Caleb Alexander, MD, MS; Hui Tang, MS; and Monica E. Peek, MD, MPH
Connecting the Dots: Examining the Link Between Workforce Health and Business Performance
Bruce W. Sherman, MD; and Wendy D. Lynch, PhD
Patient Attitudes About Specialty Follow-up Care by Telephone
Jessica A. Eng, MD; Cecily J. Hunter, BA; Margaret A. Handley, PhD; Christy K. Boscardin, PhD; Ralph Gonzales, MD; and Sara L. Ackerman, PhD
The Impact of Patient Assistance Programs and the 340B Drug Pricing Program on Medication Cost
Yelba M. Castellon, MD; Shahrzad Bazargan-Hejazi, PhD; Miles Masatsugu, MD; and Roberto Contreras, MD
Complying With State and Federal Regulations on Essential Drug Benefits: Implementing the Affordable Care Act
Joshua P. Cohen, PhD; Abigail Felix, BA; and Magdalini Vasiadi, PhD
Trends in the Financial Burden of Medical Care for Nonelderly Adults with Diabetes, 2001 to 2009
Peter Cunningham, PhD; and Emily Carrier, MD
Formulary Restrictions on Atypical Antipsychotics: Impact on Costs for Patients With Schizophrenia and Bipolar Disorder in Medicaid
Seth A. Seabury, PhD; Dana P. Goldman, PhD; Iftekhar Kalsekar, PhD; John J. Sheehan, PhD; Kimberly Laubmeier, PhD; and Darius N. Lakdawalla, PhD
Impact of a Medicare MTM Program: Evaluating Clinical and Economic Outcomes
Rita L. Hui, PharmD, MS; Brian D. Yamada, PharmD; Michele M. Spence, PhD; Erwin W. Jeong, PharmD; and James Chan, PharmD, PhD
Dialing In: Effect of Telephonic Wellness Coaching on Weight Loss
Min Tao, PhD; Krishna Rangarajan, MS; Michael L. Paustian, PhD, MS; Elizabeth A. Wasilevich, PhD, MPH; and Darline K. El Reda, DrPH, MPH
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Positive Predictive Values of ICD-9 Codes to Identify Patients With Stroke or TIA
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

Positive Predictive Values of ICD-9 Codes to Identify Patients With Stroke or TIA

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
This study determined that ICD-9 codes 433.X1, 434.XX, and V12.54 had positive predictive values >90%, and may be used to focus care on stroke patients.
Objectives: To determine the positive predictive values of inpatient and outpatient ICD-9 codes and status code V12.54 for identifying confirmed history of stroke or transient ischemic attack (cerebral event) among patients within a managed care organization.

Study Design: Retrospective, cohort study.

Methods: Inpatient hospital claims and outpatient visit records were used to identify patients with ICD-9 codes (430.XX to 438.XX) or status code V12.54 in the primary or secondary position recorded between January 1, 2001, and December 31, 2009. A standardized chart abstraction tool was used by trained chart abstractors blinded to the coding to confirm the cerebral event and classify stroke type. Positive predictive values (PPVs) were calculated for each code based on care setting.

Results: A total of 4689 patients with 10,376 unique stroke codes recorded in the administrative data were reviewed. Of these, 2785 (59.4%) patients had a confirmed cerebral event. The codes with PPV less than 90% were 434.XX, 433 .X1, and V12.54 where codes were recorded in both the inpatient and outpatient settings. Overall, inpatient-only codes produced higher PPVs; however, relatively fewer events were captured in this setting.

Conclusions: Administrative ICD-9 codes 434.XX, 433.X1, and V12.54 had consistently high PPVs in identifying patients with a confirmed cerebral event. These codes could be used as part of a probabilistic approach to focus care activities on patients with the highest likelihood of a cerebral event.

Am J Manag Care. 2014;20(2):e27-e34
  •  An ideal cerebrovascular ICD-9 code would correctly identify a large number of patients with true cerebral events (ie, maintain a high positive predictive value [PPV]).

  • Our results found that several stroke codes (433.X1, 434, and V12.54) had relatively high PPVs. n Incorporating diagnoses and health status administrative codes from the outpatient setting may improve the ability to identify patients new to a health plan with a confirmed stroke or transient ischemic attack (TIA).

  • Using these codes to identify patients with true events may be a means to establish quality improvement programs and focus care activities on patients with prior strokes or TIAs.

  • Individuals should be cautioned that even the codes with high PPVs may still result in false positives in up to 11% of cases.
Stroke is the fourth-leading cause of death, the most common neurologic disorder, and a leading cause of long-term disability in the United States.1 Despite widely available national clinical practice treatment guidelines for ischemic stroke, studies demonstrate that a significant proportion of these patients have uncontrolled modifiable risk factors and remain undertreated.2-4 Barriers to implementing treatment guidelines include patient, provider, and healthcare system factors. Developing validated disease registries may allow better tracking and management of patients with stroke.

Administrative data, including International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes, can be a powerful tool for identifying eligible patients for disease state registries. Healthcare organizations have developed stroke registries to address treatment gaps and improve quality of care, and support surveillance, epidemiologic and economic research for patients with ischemic stroke.4-8 However, cerebrovascular-related ICD-9 diagnosis codes may not reliably identify patients with a confirmed stroke.4-7,9-17

Studies evaluating the accuracy of cerebrovascular ICD-9 codes have relied primarily on patients discharged following emergency department (ED) visits or hospitalizations.6,8,9-11,13-16 These data may not be available for patients transitioning to a new healthcare system. Patients new to a healthcare system and/or patients who were never treated in the ED or hospital could be seen in the outpatient setting where either self-report or clinical diagnosis of cerebrovascular disease is made. Thus, incorporating diagnoses and health status administrative codes from the outpatient setting may improve the ability to identify patients with a confirmed stroke or transient ischemic attack (TIA). The purpose of this study was to determine the positive predictive value (PPV) of cerebrovascular ICD-9 codes using data from both inpatient and outpatient care settings to capture patients, both incident and prevalent, with cerebral events including stroke or TIA. The information from this study may be useful for healthcare systems to develop and/or improve stroke registries.


Study Design and Setting

This retrospective study was conducted at Kaiser Permanente Colorado (KPCO), an integrated healthcare delivery system providing services to more than 500,000 members at 24 medical offices in the Denver-Boulder metropolitan area. At KPCO, all providers utilize an electronic medical record (EMR), in which all office visit, vital sign, imaging, laboratory, hospital discharge summaries, pharmacy, etc, data are housed. An EMR has been used at KPCO since 1998. In addition to the provider interface, the EMR has a data repository that can be queried electronically to extract relevant data. The majority of KPCO patients receive prescription medications from in-house pharmacies for a co-payment. This study was approved by the KPCO Institutional Review Board with a waiver of informed consent.

Patient Population

All patients at least 18 years of age with at least 1 inpatient stay or outpatient medical office visit with an ICD-9 diagnosis code between 430.xx and 438.xx or status code V12.54 (Personal History of Stroke) (Appendix) in the primary or secondary position recorded between January 1, 2001, and December 31, 2009, were identified administratively from inpatient hospital claim databases and outpatient visit records from the EMR data repository. Medical information (eg, provider visit notes, hospital discharge summaries) in the EMR was available from January 1, 1998, onward. In some cases, scanned medical information was available prior to 1998.

While there is some disagreement for using all diagnosis codes versus limiting to primary and secondary positions,12,17 we used codes from both positions to maximize the yield of patients with which to examine for true strokes. Patients were categorized by the first 3 digits of the identified diagnosis code (eg, a patient with a diagnosis of 433.10 would be categorized as 433) or the status code alone (ie, V12.54) and the setting type where the code was recorded (inpatient only, outpatient only, or the same code recorded in both care settings). All of a patient’s cerebrovascular codes identified in the administrative data during the study period were captured; thus, a patient could have had more than 1 code category assigned in the inpatient and/or outpatient settings. There were no prespecified exclusion criteria.


Information from each eligible patient’s EMR was considered the standard for determination of a confirmed stroke or TIA. An abstraction tool using elements from the Rochester Minnesota Stroke study form18 was developed with input from KPCO neurologists. The tool was used to identify patients with a confirmed incident or prevalent stroke or TIA (cerebral event), identify the cerebral event date, and, if a confirmed stroke was found, classify the stroke type (ischemic, hemorrhagic, or unknown). For purposes of this manuscript, all cerebral events (including TIAs) are referred to as a stroke unless specifically describing/discussing TIAs. Identifying a confirmed stroke required sufficient clinical and/or radiologic evidence of diagnosis (focal neurological symptoms and/or computed tomography [CT] scan/magnetic resonance imaging [MRI] verifying ischemia, infarct, or hemorrhage). Recognizing the difficulty in objectively diagnosing TIA, a confirmed TIA was defined as a coded diagnosis with written confirmation by physician(s) in hospital progress/discharge note(s) or medical office note(s).

Given that secondary prevention management of TIA is similar to that of ischemic stroke,2 TIA was included with the ischemic stroke classification. Intracerebral and subarachnoid bleeding was classified as hemorrhagic stroke. The “unknown” classification was used when chart review revealed clinical evidence of stroke, but insufficient clinical or radiologic evidence was available to establish stroke type. Trained clinical pharmacy specialist reviewers used the abstraction tool and all available information in the EMR to determine whether patients had confirmed stroke and to classify stroke type. Reviewers were blinded to administrative ICD-9 code(s). A kappa score was calculated for a random sample of 20 patients with the result of 0.8 indicating a high degree of inter-rater reliability for stroke confirmation.17 Additionally, the study investigators met with the reviewers to review the discrepancies and agree on how to code certain cases moving forward.

Prescriptions for antiplatelet medications (ie, ticlopidine [Ticlid], clopidogrel [Plavix], dipyridamole [Persantine], and dipyridamole/ ASA [Aggrenox]), use of imaging (CT and/or MRI scans), and outpatient neurology department visits within 180 days of initial cerebrovascular ICD-9 code(s) were identified using queries of KPCO-integrated electronic databases. These stroke-related health service use indicators were used in conjunction with the study ICD-9 codes to determine if the PPVs of administrative data to identify strokes could be improved.

Data Analysis

Positive predictive values for each ICD-9 code were determined by dividing the number of confirmed stroke events by the total number of events recorded in the administrative data for the specified code. The primary study outcome was the PPV with 95% confidence intervals (CIs) for correctly identifying confirmed cerebral events.19 PPVs were calculated for each ICD-9 code in the setting where the code was recorded (ie, inpatient setting only, outpatient setting only, or the same code recorded in both settings). Secondary outcomes included assessments of PPV for identifying confirmed hemorrhagic and ischemic strokes, respectively for each hemorrhagic (430, 431, 432) and ischemic (433, 434, 435, 436, 437, 438, V12.54) code. Positive predictive values for these outcomes were determined by dividing the number of specific confirmed events (ischemic or hemorrhagic) by the total number of events recorded in the administrative data for the specified code. Subanalyses were performed to determine the robustness of PPV estimates under various scenarios.

The impact of using study ICD-9 codes in conjunction with health service indicators was determined by calculating PPVs for each ICD-9 code individually and in combination with various health service indicators. To accomplish this, only patients who were exposed to the health service indicator and had the ICD-9 code diagnosis were used to calculate the PPV. To assess the impact of ICD-9 code position (primary or secondary), secondary position inpatient codes and patients with only a secondary position inpatient code were removed and PPVs recalculated. To assess the impact of including TIA, all patients with confirmed TIA were removed and PPVs recalculated. To assess the impact of the ICD-9 code 436 not being inclusive of stroke, because the code excludes “cerebrovascular accident (CVA) NOS, Stroke” as of October 1, 2004, the PPVs of code 436 recorded before and on/after this date were recalculated. To assess the impact of the use of codes with specific “infarction” terms, codes 433.XX and 434.XX were categorized individually by codes that contain the infarction term (ie, 433.X1 and 434.X1) or not and the PPVs were recalculated. Positive predictive values, were determined with SAS version 9.1.3 (SAS Institute Inc, Cary, North Carolina) using Proc Freq with weighting by the count of patients having a specific diagnosis and the exact binomial function to determine 95% CIs.

Patient characteristics were reported as means with standard deviations for interval-level characteristics. These characteristics were assessed for distribution normality and appropriate tests (eg, t test, rank-sum test) were used to assess differences between groups. To assess differences in proportions between groups on dichotomous characteristics, Pearson’s x2 test of association was utilized. A 2-sided alpha level was set at <.05.


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