title: Implementation of a mortality prediction rule for real-time decision making: feasibility and validity.
abstract: BACKGROUND
  A previously published, retrospectively derived prediction rule for death within 30 days of hospital admission has the potential to launch parallel interdisciplinary team activities. Whether or not patient care improves will depend on the validity of prospectively generated predictions, and the feasibility of generating them on demand for a critical proportion of inpatients.
  
  
  OBJECTIVE
  To determine the feasibility of generating mortality predictions on admission and to validate their accuracy using the scoring weights of the retrospective rule.
  
  
  METHODS
  Prospective, sequential cohort.
  
  
  METHODS
  Large, tertiary care, community hospital in the Midwestern United States
  
  
  METHODS
  Adult patients admitted from the emergency department or scheduled for elective surgery
  
  
  RESULTS
  Mortality predictions were generated on demand at the beginning of the hospitalization for 9312 (92.9%) out of a possible 10,027 cases. The area under the receiver operating curve for 30-day mortality was 0.850 (95% confidence interval: 0.833-0.866), indicating very good to excellent discrimination. The prospectively generated 30-day mortality risk had a strong association with the receipt of palliative care by hospital discharge, in-hospital mortality, and 180-day mortality, a fair association with the risk for 30-day readmissions and unplanned transfers to intensive care, and weak associations with receipt of intensive unit care ever within the hospitalization or the development of a new diagnosis that was not present on admission (ie, complication).
  
  
  CONCLUSIONS
  Important prognostic information is feasible to obtain in a real-time, single-assessment process for a sizeable proportion of hospitalized patients.
authors.affiliation: Department of Medicine, St. Joseph Mercy Hospital, Ann Arbor, Michigan; Quality Institute, St. Joseph Mercy Hospital, Ann Arbor, Michigan.
authors.forename: Mark E
authors.initials: ME
authors.lastname: Cowen
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Czerwinski
authors.forename: Patricia J
authors.initials: PJ
authors.lastname: Posa
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Van Hoek
authors.forename: James
authors.initials: J
authors.lastname: Mattimore
authors.forename: Lakshmi K
authors.initials: LK
authors.lastname: Halasyamani
authors.forename: Robert L
authors.initials: RL
authors.lastname: Strawderman
mesh_terms: D000368:Aged; D019540:Area Under Curve; D003657:Decision Making; D017558:Elective Surgical Procedures / Q000401:mortality*; D004636:Emergency Service, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data*; D005240:Feasibility Studies; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D008297:Male; D015146:Midwestern United States / Q000453:epidemiology; D010343:Patient Admission / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D018570:Risk Assessment / Q000379:methods; D062606:Tertiary Care Centers / Q000458:organization & administration / Q000706:statistics & numerical data*