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The prognosis is a prediction of the course of a disease following its onset. It refers to the possible outcomes of a disease (e.g. death, chance of recovery, recurrence) and the frequency with which these outcomes can be expected to occur.
Sometimes the characteristics of a particular patient can be used to more accurately predict that patient's eventual outcome. These characteristics are called prognostic factors, and they can be used to predict outcome. Prognostic factors need not necessarily cause the outcomes, just be associated with them strongly enough to predict their development.1
Prognostic factors can be any of several types, including:
- Demographic (e.g. age)
- Behavioural (e.g. alcohol consumption, smoking)
- Disease-specific (e.g. tumour stage)
- Co-morbid (e.g. other conditions accompanying the disease in question)
Risk factors vs. prognostic factors
In a prognostic study patients with a particular illness are identified, followed forward in time, and their outcomes measured. Conditions associated with the outcome are identified; these are known as prognostic factors.
Prognostic factors are similar to risk factors in conventional cohort studies, but they may occur at a different stage on the disease spectrum: risk factors are present before the development of a disease, whereas prognostic factors may either have been present before the onset (e.g. sex, smoking behaviour) of the disease under investigation, or have developed afterwards (e.g. tumour size, high white cell count).
There are several other important differences between prognostic factors and risk factors:2
- Study patients are different – in prognostic studies, they have already developed the disease of interest
- Risk and prognosis describe different outcomes – the onset of disease versus a range of disease consequences
- Variables associated with an increased risk of developing a disease are not necessarily the same as those that indicate a worse prognosis or outcome.
Elements of a prognostic study
Prognostic studies should begin at a defined point of time in the disease course, follow up patients for an adequate period of time, and measure all relevant outcomes. Other features include:2
- To ensure an unbiased sample, the study population should include all those with a disease in a defined population, for example all those on a disease register
- Patients should all be followed up from the same defined point in the disease course to ensure a precise estimate of prognosis
- Patients must be followed up for long enough so that most important outcomes have occurred
- Prognosis estimates should include all aspects of a disease that are important to patients, including pain and disability, not just death or recovery.
The best design for a prognostic study is a cohort study. It would usually be impossible or unethical to randomise patients to different prognostic factors. This type of study is described in more detail elsewhere, but would normally follow up one or more groups (cohorts) of individuals who have a disease but have not yet suffered an adverse event, and monitor the number of outcome events over time.
It may also be possible to perform a case-control study of prognosis, comparing ‘cases’ of individuals who have already suffered the outcome event to ‘controls’ who have not, to estimate the proportion of individuals in each group with a particular prognostic factor. However, this study design is prone to bias and cannot provide information about absolute risk.1
Appraising a prognostic study
Laupacis and colleagues1 provide a helpful guide to reviewing prognostic studies, including a critical appraisal framework, which is well worth reading. They suggest that readers should ask a series of questions to determine whether the results are valid, how they should be interpreted, and whether the information will benefit patients. The questions include:
- Was there a representative and well-defined sample of patients at a similar point in the course of the disease?
- Was follow-up sufficiently long and complete?
- Were objective and unbiased outcome criteria used?
- How large is the likelihood of the outcome events occurring in a specified period of time?
- Were the study patients similar to my own?
- Are the results useful for reassuring or counselling patients?
Advantages of prognostic studies3
- The results can facilitate clinical decision-making, for example, by providing the information necessary to select appropriate treatment
- A more accurate prediction of disease outcomes facilitates patient education and counseling
- Prognostic studies may also allow subgroups of patients to be defined who are at particular risk of specific disease outcomes, leading to improved study designs and analysis of clinical trials through risk stratification.
Disadvantages of prognostic studies
- There is considerable variation in the quality of prognostic studies published in the literature.
- The results may not be generalisable to local settings, limiting the validity of the study
- Laupacis A, Wells G, Richardson S, et al. Users guides to the medical literature. V. How to use an article about prognosis. JAMA 1994; 272:234–237
- Fletcher R, Fletcher S. Clinical epidemiology: the essentials (5th ed.), Lippincott Williams & Wilkins, 2013.
- Mak K, Kum CK. How to Appraise a Prognostic Study. World J Surg 2005; 29: 567–569
© Helen Barratt 2009, Saran Shantikumar 2018