title: Mortality predictions on admission as a context for organizing care activities.
abstract: BACKGROUND
  Favorable health outcomes are more likely to occur when the clinical team recognizes patients at risk and intervenes in consort. Prediction rules can identify high-risk subsets, but the availability of multiple rules for various conditions present implementation and assimilation challenges.
  
  
  METHODS
  A prediction rule for 30-day mortality at the beginning of the hospitalization was derived in a retrospective cohort of adult inpatients from a community hospital in the Midwestern United States from 2008 to 2009, using clinical laboratory values, past medical history, and diagnoses present on admission. It was validated using 2010 data from the same and from a different hospital. The calculated mortality risk was then used to predict unplanned transfers to intensive care units, resuscitation attempts for cardiopulmonary arrests, a condition not present on admission (complications), intensive care unit utilization, palliative care status, in-hospital death, rehospitalizations within 30 days, and 180-day mortality.
  
  
  RESULTS
  The predictions of 30-day mortality for the derivation and validation datasets had areas under the receiver operating characteristic curve of 0.88. The 30-day mortality risk was in turn a strong predictor for in-hospital death, palliative care status, 180-day mortality; a modest predictor for unplanned transfers and cardiopulmonary arrests; and a weaker predictor for the other events of interest.
  
  
  CONCLUSIONS
  The probability of 30-day mortality provides health systems with an array of prognostic information that may provide a common reference point for organizing the clinical activities of the many health professionals involved in the care of the patient.
authors.affiliation: Department of Medicine, St. Joseph Mercy Hospital, Ann Arbor, MI 48197, USA. cowenm@trinity-health.org
authors.forename: Mark E
authors.initials: ME
authors.lastname: Cowen
authors.forename: Robert L
authors.initials: RL
authors.lastname: Strawderman
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Czerwinski
authors.forename: Mary Jo
authors.initials: MJ
authors.lastname: Smith
authors.forename: Lakshmi K
authors.initials: LK
authors.lastname: Halasyamani
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006764:Hospitals, Community / Q000639:trends*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000639:trends*; D005791:Patient Care / Q000379:methods / Q000401:mortality* / Q000639:trends*; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012307:Risk Factors; D055815:Young Adult