Understanding clinical risk scores in 4 days. Day 3: Prognosis
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.”
Understanding clinical risk scores in 4 days. Day 2: Prediction
As well as diagnosing disease, it may be important to predict which patients need more investigation or more treatment. The term, “clinical prediction rule”, is often used to describe the best combination of medical signs, symptoms, and other findings in predicting the probability of a specific disease or outcome. Therefore, they can be used in diagnosis as in the last article, or they can estimate the risk of a specific outcome, and therefore help in decision-making.
To form prediction rules, researchers look at a group of patients suspected of having a specific disease or outcome. They then compare the value of clinical findings available to the clinician versus the results of more intensive testing or the results of delayed clinical follow up.
Previous studies have shown that giving clinicians information in the form of risk scores in isolation may not influence clinical practice. GPs routinely underuse risk scores and have suggested that computerized risk prediction for multiple diseases simultaneously and the integration of lifestyle recommendations may improve their uptake.
Clinical prediction rules are most effective when implemented as part of a pathway within hospital policies and guidelines, as shown in the case of pneumonia and the more rigorously the rules are implemented, the more successful they are at improving outcomes.
The CHADS(2) score is an example of a clinical risk score to help in deciding whether to start warfarin in patients with atrial fibrillation to prevent stroke. Patients are classified by their clinical history, getting 1 point each for Congestive cardiac failure, Hypertension, Age>75 years, Diabetes and 2 points for Stroke. Patients with scores of 2 or more should get warfarin, as long as they have no contraindications. A recent improvement on this score, the CHA(2)DS(2)-VASc has added 3 additional factors (history of vascular disase, sex and age 65-74) to improve the prediction of risk so that only people who are really at negligible risk of stroke do not get warfarin.
Other examples of risk scores used in this way are Wells scores for deep vein thrombosis and pulmonary embolism and CURB-65 in pneumonia.