title: External validation of the In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT) multivariable prediction rule.
abstract: OBJECTIVE
  To validate the In-hospital Mortality for PulmonAry embolism using Claims daTa (IMPACT) multivariable prediction rule using admission claims data.
  
  
  METHODS
  Retrospective claims database analysis.
  
  
  METHODS
  This analysis was performed using Humana admission claims data from January 2007 to March 2014. We included adult patients admitted for their first PE during this period (International Classification of Diseases, ninth edition, Clinical Modification code of 415.1x in in the primary position or secondary position when accompanied by a primary code for a PE complication). The IMPACT rule, consisting of age plus 11 comorbidities, was used to estimate patients' probability of in-hospital mortality and classify risk. Low risk was defined as in-hospital mortality ≤ 1.5%. IMPACT was evaluated by evaluating prognostic test characteristic values and 95% confidence intervals (CIs).
  
  
  RESULTS
  A total of 23,858 patients admitted for PE were included, and 3.3% died in-hospital. The IMPACT prediction rule classified 2371 (9.9%) as low-risk; with a sensitivity of 97.6%, 95% CI: 96.1-98.5, specificity of 10.2%, 95% CI: 9.8-10.6, negative and positive predictive values of 99.2% (95% CI: 98.7-99.5) and 3.5% (95% CI: 3.3-3.8) and c-statistic of 0.70, 95% CI: 0.0.68-0.72, for in-hospital mortality. IMPACT classified 42.7% of patients < 65 years old as low-risk; with a sensitivity, specificity and c-statistic of 85.0%, 95% CI: 77.4-90.5, 43.3%, 95% CI: 42.0-44.7 and 0.74, 95% CI: 0.69-0.78, respectively.
  
  
  CONCLUSIONS
  The IMPACT prediction rule was valid when implemented in a database consisting largely of Medicare claims. Following further external validation and direct comparison to commonly used clinical prediction rules, IMPACT may become a valuable tool for payers and hospitals wishing to retrospectively assess whether their PE patients are being kept hospitalized for the optimal period of time.
authors.affiliation: Department of Pharmacy Practice and Administration, University of Saint Joseph School of Pharmacy, Hartford, CT, USA.|Evidence-based Practice Center, UCONN/Hartford Hospital, Hartford, CT, USA.
authors.forename: C G
authors.initials: CG
authors.lastname: Kohn
authors.affiliation: Department of Emergency Medicine, Baylor College of Medicine, Houston, TX, USA.
authors.forename: W F
authors.initials: WF
authors.lastname: Peacock
authors.affiliation: Department of Emergency Medicine, University of Cincinnati, Cincinnati, OH, USA.
authors.forename: G J
authors.initials: GJ
authors.lastname: Fermann
authors.affiliation: Program Evaluation & Pharmacy Analytics, Aetna, Hartford, CT, USA.
authors.forename: T J
authors.initials: TJ
authors.lastname: Bunz
authors.affiliation: Janssen Scientific Affairs, LLC, Raritan, NJ, USA.
authors.forename: C
authors.initials: C
authors.lastname: Crivera
authors.affiliation: Janssen Scientific Affairs, LLC, Raritan, NJ, USA.
authors.forename: J R
authors.initials: JR
authors.lastname: Schein
authors.affiliation: Department of Pharmacy Practice, University of Connecticut School of Pharmacy, Storrs, CT, USA.
authors.forename: C I
authors.initials: CI
authors.lastname: Coleman
mesh_terms: D000067575:Administrative Claims, Healthcare / Q000706:statistics & numerical data; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D003661:Decision Support Techniques; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011655:Pulmonary Embolism / Q000401:mortality*; D012189:Retrospective Studies