title: A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record.
abstract: BACKGROUND
  This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record.
  
  
  METHODS
  Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index.
  
  
  RESULTS
  The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P < .0001), assistance with activities of daily living (ADLs; P = .022), admission type (elective/emergency) (P = .059), oxygen use (P < .0001), and vital signs abnormalities including pulse oximetry (P = .0004), temperature (P = .017), and heart rate (P = .0002). A logistic regression model was developed to predict death within 30 days: Score = 18.2897 + 0.6013*(admit type) + 0.4518*(ADL) + 0.0325*(admit age) - 0.1458*(temperature) + 0.019*(heart rate) - 0.0983*(pulse oximetry) - 0.0123 (systolic blood pressure) + 0.8615*(O2 use). The largest sum of sensitivity (63%) and specificity (78%) was at -2.09 (area under the curve = -0.789). A total of 25.32% (100 of 395) of patients with a score above -2.09 died, whereas 4.31% (49 of 1136) of patients below -2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably.
  
  
  CONCLUSIONS
  Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life.
authors.affiliation: Department of Medicine, Division of General Medical Disciplines and Division of Oncology, Stanford University, Stanford, CA 94305, USA. kavitha@stanford.edu
authors.forename: Kavitha J
authors.initials: KJ
authors.lastname: Ramchandran
authors.forename: Joseph W
authors.initials: JW
authors.lastname: Shega
authors.forename: Jamie
authors.initials: J
authors.lastname: Von Roenn
authors.forename: Mark
authors.initials: M
authors.lastname: Schumacher
authors.forename: Eytan
authors.initials: E
authors.lastname: Szmuilowicz
authors.forename: Alfred
authors.initials: A
authors.lastname: Rademaker
authors.forename: Bing Bing
authors.initials: BB
authors.lastname: Weitner
authors.forename: Pooja D
authors.initials: PD
authors.lastname: Loftus
authors.forename: Isabella M
authors.initials: IM
authors.lastname: Chu
authors.forename: Sigmund
authors.initials: S
authors.lastname: Weitzman
mesh_terms: D000368:Aged; D015331:Cohort Studies; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality*; D010343:Patient Admission / Q000706:statistics & numerical data*; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors