title: Laboratory tests as short-term correlates of stroke.
abstract: BACKGROUND
  The widespread adoption of electronic health records provides new opportunities to better predict which patients are likely to suffer a stroke. Using electronic health records, we assessed the correlation of different laboratory tests to future occurrences of a stroke.
  
  
  METHODS
  We examined the electronic health records of 2.4 million people over a two year time span. These records contained 26,964 diagnoses of stroke. Using Cox regression analysis, we measured whether any one of 1796 different laboratory tests were effectively correlated with a future diagnosis of stroke.
  
  
  RESULTS
  We identified 38 different laboratory tests that had significant short-term (two year) prognostic value for a future diagnosis of stroke. For each of the 38 laboratory tests we also compiled the Kaplan-Meier survival curve, and relative risk ratio that the test confers.
  
  
  CONCLUSIONS
  Several dozen laboratory tests are effective short-term correlates of stroke.
authors.affiliation: Kaiser Permanente Southern California, San Diego, CA, USA.|Department of Biomedical Engineering, University of California-Irvine, Irvine, CA, 92697-2715, USA.
authors.forename: Trevor
authors.initials: T
authors.lastname: Sughrue
authors.affiliation: Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Swiernik
authors.affiliation: Kaiser Permanente Southern California, San Diego, CA, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Department of Biomedical Engineering, University of California-Irvine, Irvine, CA, 92697-2715, USA. jpbrody@uci.edu.|Department of Biomedical Engineering, Henry Samueli School of Engineering, University of California, Irvine, CA, 92603, USA. jpbrody@uci.edu.
authors.forename: James P
authors.initials: JP
authors.lastname: Brody
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001772:Blood Cell Count / Q000706:statistics & numerical data; D001774:Blood Chemical Analysis / Q000706:statistics & numerical data; D002140:California / Q000453:epidemiology; D002784:Cholesterol / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data*; D015331:Cohort Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006339:Heart Rate; D006801:Humans; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D012189:Retrospective Studies; D020521:Stroke / Q000453:epidemiology*; D015996:Survival Rate; D055815:Young Adult
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D002784:Cholesterol