title: Incorporating initial treatments improves performance of a mortality prediction model for patients with sepsis.
abstract: OBJECTIVE
  Mortality prediction models can be used to adjust for presenting severity of illness in observational studies of treatment effectiveness. We aimed to determine the incremental benefit of adding information about critical care services to a sepsis mortality prediction model.
  
  
  METHODS
  In a retrospective cohort of 166 931 eligible sepsis patients at 309 hospitals, we developed nested logistic regression models to predict mortality at the patient level. Our initial model included only demographic information. We then added progressively more detailed information such as comorbidities and initial treatments. We calculated each model's area under the receiver operating characteristic curve (AUROC) and also used a sheaf coefficient analysis to determine the relative effect of each additional group of variables.
  
  
  RESULTS
  Model discrimination increased as more detailed patient information was added. With demographics alone, the AUROC was 0.59; adding comorbidities increased the AUROC to 0.67. The final model, which took into account mixed (hierarchical) effects at the hospital level as well as initial treatments administered within the first two hospital days, resulted in an AUROC of 0.78. The standardized sheaf coefficient for the initial treatments was approximately 30% greater than that for demographics or infection source.
  
  
  CONCLUSIONS
  A sepsis disease risk score that incorporates information about the use of mechanical ventilation and vasopressors is superior to models that rely only on demographic information and comorbidities. Until administrative datasets include clinical information (such as vital signs and laboratory results), models such as this one could allow researchers to conduct observational studies of treatment effectiveness in sepsis patients.
authors.affiliation: Center for Quality of Care Research, Baystate Medical Center, Springfield, MA, USA. lagutc@gmail.com
authors.forename: Tara
authors.initials: T
authors.lastname: Lagu
authors.forename: Michael B
authors.initials: MB
authors.lastname: Rothberg
authors.forename: Brian H
authors.initials: BH
authors.lastname: Nathanson
authors.forename: Jay S
authors.initials: JS
authors.lastname: Steingrub
authors.forename: Peter K
authors.initials: PK
authors.lastname: Lindenauer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D016015:Logistic Models; D008297:Male; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D008875:Middle Aged; D011379:Prognosis; D012189:Retrospective Studies; D018805:Sepsis / Q000175:diagnosis / Q000401:mortality / Q000628:therapy; D012720:Severity of Illness Index