Commentary: Prognostic models: clinically useful or quickly forgotten?

We are all familiar with using single items of patient data such as age or smoking history to help in making difficult clinical decisions.1 Prognostic models are more complex tools for helping decision making that combine two or more items of patient data to predict clinical outcomes.2 They are of potential value when doctors are making difficult clinical decisions (such as ordering invasive tests or selecting which patients should benefit from scarce resources),3 conducting comparative audit,4 or selecting uniform groups of patients for clinical trials.5 Another prognostic model appears in this week's BMJ6 to join the hundreds published every year.7 However, apart from exceptions such as the Glasgow coma scale8 and APACHE III,9 few of these models are routinely used to inform difficult clinical decisions. It might be argued that doctors never prognosticate, working always in the present, but studies of medical decision making show this is untrue.10 11 Some doctors might claim that they can foretell patient outcomes better than any statistical model, but again there is contrary evidence.9 12 A journal editor's view might be that most published models reflect preliminary work and need further research before clinical adoption.13 Finally, some models predict events that are of no clinical relevance or do not generate predictions in time to inform clinical decisions, suggesting that their developers wished merely to publish journal articles, not build clinically useful tools.14 We believe that the main reasons why doctors reject published prognostic models are lack of clinical credibility and lack of evidence that a prognostic model can support decisions about patient care (that is, evidence of accuracy, generality, and effectiveness). We examine each of these issues in …