title: Pathways Clinical Decision Support for Appropriate Use of Key Biomarkers.
abstract: Breast cancer diagnostics have the ability to predict disease recurrence and the benefit of chemotherapy. This study measures the use of a diagnostic assay, Oncotype DX, when embedded in a breast cancer decision support algorithm and, on the basis of the assay results, the use of chemotherapy in the adjuvant setting.
  
  
  
  UPMC CancerCenter retrospectively reviewed patients with estrogen receptor-positive, human epidermal growth factor receptor 2 (HER2)Neu-negative disease with zero to three positive nodes navigated in the Via Pathways decision support portal during a 12-month period. The breast algorithm prompted input of the assay recurrence score (RS) and then recommended hormonal therapy alone (HT) for low RS, or chemotherapy followed by HT for high RS. The patient's RS was correlated with the treatment decision.
  
  
  
  During this time period, 643 patients had ER-positive, HER2Neu-negative disease with zero to three positive nodes. Of those, 596 (92.7%) had diagnostic testing to determine chemotherapy plus HT versus HT alone, and 47 had chemotherapy followed by HT without an RS. For node-negative patients classified with low or high RS, pathway treatment adherence rates were 99.7% and 96.6%, respectively; node-positive patients had 95.7% and 87.5% adherence rates, respectively.
  
  
  
  This analysis demonstrates the use of a clinical pathway to measure the adoption of a diagnostic test, the Oncotype DX breast assay, and the use of the appropriate therapy on the basis of the RS. As more diagnostics are established to aid in the personalized treatment of diseases, pathways may be important in maintaining clinician awareness of the appropriate disease presentations where these tests should be used, measuring usage of these tests, and tracking the treatment decisions on the basis of test results.
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Peter G
authors.initials: PG
authors.lastname: Ellis
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Adam M
authors.initials: AM
authors.lastname: Brufsky
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Sushil
authors.initials: S
authors.lastname: Beriwal
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Kathleen G
authors.initials: KG
authors.lastname: Lokay
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Hans O
authors.initials: HO
authors.lastname: Benson
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA.
authors.forename: Stephanie B
authors.initials: SB
authors.lastname: McCutcheon
authors.affiliation: UPMC CancerCenter; University of Pittsburgh Cancer Center; and Via Oncology, Pittsburgh, PA melinda.krebs@viaoncology.com.
authors.forename: Melinda
authors.initials: M
authors.lastname: Krebs
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000378:metabolism; D017024:Chemotherapy, Adjuvant; D020000:Decision Support Systems, Clinical; D005260:Female; D006801:Humans; D008875:Middle Aged; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor