title: Timing of onset and burden of persistent critical illness in Australia and New Zealand: a retrospective, population-based, observational study.
abstract: Critical care physicians recognise persistent critical illness as a specific syndrome, yet few data exist for the timing of the transition from acute to persistent critical illness. Defining the onset of persistent critical illness as the time at which diagnosis and illness severity at intensive care unit (ICU) arrival no longer predict outcome better than do simple pre-ICU patient characteristics, we measured the timing of this onset at a population level in Australia and New Zealand, and the variation therein, and assessed the characteristics, burden of care, and hospital outcomes of patients with persistent critical illness.
  
  
  
  In this retrospective, population-based, observational study, we used data for ICU admission in Australia and New Zealand from the Australian and New Zealand Intensive Care Society Adult Patient Database. We included all patients older than 16 years of age admitted to a participating ICU. We excluded patients transferred from another hospital and those admitted to an ICU for palliative care or awaiting organ donation. The primary outcome was in-hospital mortality. Using statistical methods in evenly split development and validation samples for risk score development, we examined the ability of characteristics to predict in-hospital mortality.
  
  
  
  Between Jan, 2000, and Dec, 2014, we studied 1 028 235 critically ill patients from 182 ICUs across Australia and New Zealand. Among patients still in an ICU, admission diagnosis and physiological derangements, which accurately predicted outcome on admission (area under the receiver operating characteristics curve 0·898 [95% CI 0·897-0·899] in the validation cohort), progressively lost their predictive ability and no longer predicted outcome more accurately than did simple antecedent patient characteristics (eg, age, sex, or chronic health status) after 10 days in the ICU, thus empirically defining the onset of persistent critical illness. This transition occurred between day 7 and day 22 across diagnosis-based subgroups and between day 6 and day 15 across risk-of-death-based subgroups. Cases of persistent critical illness accounted for only 51 509 (5·0%) of the 1 028 235 patients admitted to an ICU, but for 1 029 345 (32·8%) of 3 138 432 ICU bed-days and 2 197 108 (14·7%) of 14 961 693 hospital bed-days. Overall, 12 625 (24·5%) of 51 509 patients with persistent critical illness died and only 23 968 (46·5%) of 51 509 were discharged home.
  
  
  
  Onset of persistent critical illness can be empirically measured at a population level. Patients with this condition consume vast resources, have high mortality, have much less chance of returning home than do typical ICU patients, and require dedicated future research. ICU clinicians should be aware that the risk of in-hospital mortality can change quickly over the first 2 weeks of an ICU course and be sure to incorporate such changes in their decision making and prognostication.
  
  
  
  None.
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Center for Clinical Management Research, Veterans Affairs Ann Arbor Health System, Ann Arbor, MI, USA. Electronic address: tiwashyn@umich.edu.
authors.forename: Theodore J
authors.initials: TJ
authors.lastname: Iwashyna
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Physiotherapy, Alfred Hospital, Melbourne, VIC, Australia.
authors.forename: Carol L
authors.initials: CL
authors.lastname: Hodgson
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Intensive Care, Alfred Hospital, Melbourne, VIC, Australia; Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Melbourne, VIC, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Pilcher
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.
authors.forename: Michael
authors.initials: M
authors.lastname: Bailey
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Melbourne, VIC, Australia.
authors.forename: Allison
authors.initials: A
authors.lastname: van Lint
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Australian and New Zealand Intensive Care Society Centre for Outcome and Resource Evaluation, Melbourne, VIC, Australia.
authors.forename: Shaila
authors.initials: S
authors.lastname: Chavan
authors.affiliation: Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia; Department of Intensive Care Unit, University of Melbourne, Austin Health, Heidelberg, VIC, Australia.
authors.forename: Rinaldo
authors.initials: R
authors.lastname: Bellomo
mesh_terms: D000368:Aged; D001315:Australia / Q000453:epidemiology; D017281:Cost of Illness; D003422:Critical Care / Q000706:statistics & numerical data; D016638:Critical Illness / Q000401:mortality*; D016208:Databases, Factual; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D009520:New Zealand / Q000453:epidemiology; D017063:Outcome Assessment, Health Care; D010343:Patient Admission / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D012372:ROC Curve; D012189:Retrospective Studies; D013997:Time Factors