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 Jan;17(1):20-6. doi: 10.1002/pds.1518.


Purpose: To validate ICD 9 codes with a high positive predictive value (PPV) for incident strokes. The study population consisted of Tennessee Medicaid enrollees aged from 50 to 84 years.

Methods: We identified all patients who were hospitalized with a discharge diagnosis of stroke between 1999 and 2003 using highly specific codes (ischemic stroke ICD 9-CM codes 433.x1, 434 [excluding 434.x0], or 436; intracerebral hemorrhage [431]; and subarachnoid hemorrhage [430]). We reviewed medical records of a systematic sample of 250 cohort members. We randomly selected 10-30 eligible records for review from hospitals with at least 10 stroke hospitalizations.

Results: We reviewed 231 charts (93% of total sampled), and 205 (89%) met study criteria for new outpatient stroke. Of the 205 confirmed new outpatient strokes, 196 had stroke listed as the primary discharge diagnosis (PPV = 96%). However, 46 (23%) of the 196 patients identified by the primary diagnosis also had a remote stroke history (recurrent stroke not incident). Thus the PPV of the primary discharge diagnosis for identifying incident stroke decreased to 74%. When we applied an algorithm that restricted our population to those with stroke as the primary diagnosis and excluded patients with any prior outpatient diagnosis of stroke, we identified incident stroke events with more precision (PPV = 80%).

Conclusion: The PPV of incident strokes was 80% using our strategy of primary discharge diagnosis and excluding prior outpatient diagnoses of stroke. Although an unknown percentage of incident strokes are missed, this group of proven incident stroke patients can be used for etiologic studies of medication exposures.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Algorithms
  • Cohort Studies
  • Female
  • Humans
  • International Classification of Diseases*
  • Male
  • Medicaid*
  • Medical Records / statistics & numerical data*
  • Middle Aged
  • Patient Discharge / statistics & numerical data
  • Prognosis
  • Reproducibility of Results
  • Retrospective Studies
  • Stroke / classification
  • Stroke / diagnosis*
  • Stroke / epidemiology*
  • Tennessee / epidemiology