Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

What is fundamental prognosis research? Before carrying out research into novel prognostic factors, prognostic models, or stratified medicine it is necessary to carry out research describing and explaining future outcomes in people with a disease or health condition in relation to current diagnostic and treatment practices. There is a close relation between the questions “What is the prognosis of people with this condition?” and “What are the outcomes of the care which people receive for this condition?” In order to improve the quality of healthcare, evidence is required on how the specific patterns of care received (such as investigation, treatment, support), and their variations (such as underuse, overuse, misuse) have an impact on future endpoints.13 Such research has a broad remit. It spans, for example, investigations into societal influences (inequitable variations in care and outcome among older people, women, the socially disadvantaged, and ethnic minorities), patient safety,14 15 unanticipated harms and benefits from treatments, and screening research. Prognosis in the absence of care—which is sometimes termed natural history—is an important parameter for judging the potential impact of screening for asymptomatic disease (such as mammography for breast cancer), as well as for case detection of symptomatic undiagnosed or unpresented conditions such as back pain or angina.16 17 These relations may be expressed as an absolute risk (or rate) of one or more type of endpoint among groups of people who share demographic and clinical characteristics; some refer to this as an average prognosis in a particular group of interest, or as a baseline risk. Here the research provides initial answers to the question “What is the prognosis of people with a given disease?” For example, on average about 15% of people aged 65 years or older, admitted in 2006 in the US died within 30 days of admission to hospital with a heart attack compared with an average of 19% in 1995.18 Such a change in the average mortality rate is illustrated in figure 2⇓. This shows the decreasing prognostic burden of heart attack and motivates inquiry into new approaches to understand and reduce this risk further. This clinical scenario also exemplifies that “the prognosis” of a disease or condition is a somewhat misleading expression: what is observed is prognosis of people in particular clinical contexts, defined by current clinical approaches in diagnosing, characterising, and managing patients with a symptom or disease. View larger version: In this window In a new window Fig 2 Example of use of fundamental prognosis research to examine variations in outcomes from medical care: inter-hospital variation in mortality per 100 population within 30 days of admission with acute myocardial infarction (created using fictional data for illustration purposes, but based on the findings of Krumholz et al18) Such prognosis research is also concerned with describing and understanding the variations around the average course.19 20 These variations may occur between individual patients or between patients clustered, for example, within surgeons, hospitals, or regions. The acute myocardial infarction example above demonstrates striking variations between hospitals in prognosis, and similar variations are seen in traumatic brain injury and other conditions.18 21 Indeed, for most hospitals the national average is a poor guide to the mortality of their patients (fig 2⇑). Stephen J Gould, the evolutionary biologist, having survived 20 years after being told the median survival of his abdominal mesothelioma was eight months, famously remarked, “the median isn’t the message.”22 Describing and explaining the sources of variability in prognosis is a theme throughout our PROGRESS framework.3 4 5 Fundamental prognosis research may help explain Gould’s long survival in terms of the demographic and clinical context (for example, his high educational status and the quality of care received), whereas research into emerging prognostic factors may examine psychological, behavioural, or biomarker factors associated with improved outcome (see paper 2 in our series3), or the extent to which his survival was predictable from statistical models of individual risk prediction (paper 3 in our series4), or whether particular treatments had a larger beneficial effect for him than for others (paper 4 in our series5).