title: Development and validation of an algorithm for identifying urinary retention in a cohort of patients with epilepsy in a large US administrative claims database.
abstract: OBJECTIVE
  The aim of this study was to develop and validate an insurance claims-based algorithm for identifying urinary retention (UR) in epilepsy patients receiving antiepileptic drugs to facilitate safety monitoring.
  
  
  METHODS
  Data from the HealthCore Integrated Research Database(SM) in 2008-2011 (retrospective) and 2012-2013 (prospective) were used to identify epilepsy patients with UR. During the retrospective phase, three algorithms identified potential UR: (i) UR diagnosis code with a catheterization procedure code; (ii) UR diagnosis code alone; or (iii) diagnosis with UR-related symptoms. Medical records for 50 randomly selected patients satisfying ≥1 algorithm were reviewed by urologists to ascertain UR status. Positive predictive value (PPV) and 95% confidence intervals (CI) were calculated for the three component algorithms and the overall algorithm (defined as satisfying ≥1 component algorithms). Algorithms were refined using urologist review notes. In the prospective phase, the UR algorithm was refined using medical records for an additional 150 cases.
  
  
  RESULTS
  In the retrospective phase, the PPV of the overall algorithm was 72.0% (95%CI: 57.5-83.8%). Algorithm 3 performed poorly and was dropped. Algorithm 1 was unchanged; urinary incontinence and cystitis were added as exclusionary diagnoses to Algorithm 2. The PPV for the modified overall algorithm was 89.2% (74.6-97.0%). In the prospective phase, the PPV for the modified overall algorithm was 76.0% (68.4-82.6%). Upon adding overactive bladder, nocturia and urinary frequency as exclusionary diagnoses, the PPV for the final overall algorithm was 81.9% (73.7-88.4%).
  
  
  CONCLUSIONS
  The current UR algorithm yielded a PPV > 80% and could be used for more accurate identification of UR among epilepsy patients in a large claims database.
authors.affiliation: HealthCore, Inc., Wilmington, DE, USA.
authors.forename: Scott C
authors.initials: SC
authors.lastname: Quinlan
authors.affiliation: Analysis Group, Inc., Boston, MA, USA.
authors.forename: Wendy Y
authors.initials: WY
authors.lastname: Cheng
authors.affiliation: GlaxoSmithKline plc, Uxbridge, Middlesex, UK.
authors.forename: Lianna
authors.initials: L
authors.lastname: Ishihara
authors.affiliation: GlaxoSmithKline plc, Research Triangle Park, NC, USA.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Irizarry
authors.affiliation: HealthCore, Inc., Wilmington, DE, USA.
authors.forename: Crystal N
authors.initials: CN
authors.lastname: Holick
authors.affiliation: Analysis Group, Inc., Boston, MA, USA.
authors.forename: Mei Sheng
authors.initials: MS
authors.lastname: Duh
mesh_terms: D000465:Algorithms; D000927:Anticonvulsants / Q000009:adverse effects / Q000627:therapeutic use; D016208:Databases, Factual / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000188:drug therapy*; D006801:Humans; D008499:Medical Records; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012189:Retrospective Studies; D014481:United States; D016055:Urinary Retention / Q000175:diagnosis* / Q000453:epidemiology / Q000209:etiology
chemical_list: D000927:Anticonvulsants
keywords: algorithm; epilepsy; pharmacoepidemiology; urinary retention