Closely related and interlinked to prediction is the idea of prognosis. Literally meaning “foreseeing” in Greek, prognosis attempts to quantify the likely outcome over time for a particular patient in a particular clinical setting. For example, in terms of risk prediction, a CHADS2 score of 2 or more in a patient with AF tells us that he or she is at a large enough risk of stroke to warrant treatment with warfarin to thin the blood. 18.2% of patients with a maximum of CHADS2 score of 6 will have had a stroke at one year: this is prognostic information. Therefore, the same score can be used as both a predictive and a prognostic tool.

One of the most enduring and widely used prognostic scores in clinical practice, is the Apgar score, used to predict outcomes in newborns by a score of 1 to 10. As noted in Apgar’s seminal paper, scores of 0-3 indicate poor prognosis for newborn babies.

The prognostic information doctors and patients are most commonly interested in is survival, especially in the case of cancer. The challenge is individualising risk from epidemiologic data to a particular patient.

As in the previous 2 blogs about diagnosis and prognosis, scores are more relevant and more likely to be used in clinical practice when they are tied in a decision-making strategy, i.e. there is no point in prognostic information (other than telling the patient what their outlook is likely to be), unless the management of the patient is altered. This is nicely summarised in this 10-year old JAMA editorial:

“Medicine is an action-oriented profession in which clinicians want to relieve suffering, rather than just watch its course. Regrettably, most prognostic indices are not accompanied by decision thresholds that convert level of risk into degree of action.”