title: Estimating long-term survival of critically ill patients: the PREDICT model.
abstract: BACKGROUND
  Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients.
  
  
  RESULTS
  This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable.
  
  
  CONCLUSIONS
  Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients.
authors.affiliation: School of Population Health, University of Western Australia and Royal Perth Hospital, Perth, Western Australia, Australia. kwok.ho@health.wa.gov.au
authors.forename: Kwok M
authors.initials: KM
authors.lastname: Ho
authors.forename: Matthew
authors.initials: M
authors.lastname: Knuiman
authors.forename: Judith
authors.initials: J
authors.lastname: Finn
authors.forename: Steven A
authors.initials: SA
authors.lastname: Webb
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D015897:Comorbidity; D003422:Critical Care; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011379:Prognosis; D016016:Proportional Hazards Models; D012189:Retrospective Studies; D016896:Treatment Outcome
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