title: Why try to predict ICU outcomes?
abstract: OBJECTIVE
  To describe why the prediction of ICU outcomes is essential to underpin critical care quality improvement programmes.
  
  
  RESULTS
  Recent literature demonstrates that risk-adjusted mortality is a widely used and well-accepted quality indicator for benchmarking ICU performance. Ongoing research continues to address the best ways to present the results of benchmarking through either direct comparison among institutions (e.g., by funnel plots) or indirect comparison against the risk predictions from a risk model (e.g., by process control charts). There is also ongoing research and debate regarding event-based outcomes (e.g., hospital mortality) versus time-based outcomes (e.g., 30-day mortality). Beyond benchmarking, ICU outcome prediction models have a role in risk adjustment and risk stratification in randomized controlled trials, and adjusting for confounding in nonrandomized, observational research. Recent examples include comparing risk-adjusted outcomes according to 'capacity strain' on the ICU and extending propensity matching methods to evaluate outcomes of patients managed with a pulmonary artery catheter, among others. Risk models may have a role in communicating risk, but their utility for individual patient decision-making is limited.
  
  
  CONCLUSIONS
  Risk-adjusted mortality has strong support from the critical care community as a quality indicator for benchmarking ICU performance but is dependent on up-to-date, accurate risk models. ICU outcome prediction can also contribute to both randomized and nonrandomized research and potentially contribute to individual patient management, although generic risk models should not be used to guide individual treatment decisions.
authors.affiliation: Intensive Care National Audit & Research Centre (ICNARC), London, United Kingdom.
authors.forename: G Sarah
authors.initials: GS
authors.lastname: Power
authors.forename: David A
authors.initials: DA
authors.lastname: Harrison
mesh_terms: D019985:Benchmarking; D003422:Critical Care / Q000592:standards / Q000639:trends; D016638:Critical Illness; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007362:Intensive Care Units / Q000592:standards / Q000639:trends; D007902:Length of Stay / Q000706:statistics & numerical data* / Q000639:trends; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D011379:Prognosis; D058996:Quality Improvement; D019984:Quality Indicators, Health Care / Q000639:trends; D016032:Randomized Controlled Trials as Topic; D020379:Risk Adjustment; D012720:Severity of Illness Index