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The Geneva score is a clinical prediction rule used in determining the pre-test probability of pulmonary embolism (PE) based on a patient's risk factors and clinical findings. [1] It has been shown to be as accurate as the Wells Score, and is less reliant on the experience of the doctor applying the rule. [2]
The Wells score is a clinical prediction rule used to classify patients suspected of having pulmonary embolism (PE) into risk groups by quantifying the pre-test probability. It is different than Wells score for DVT (deep vein thrombosis).
The Geneva prediction rules and Wells criteria are used to calculate the pre-test probability of patients to predict who has a pulmonary embolism. These scores are tools to be used with clinical judgment in deciding diagnostic testing and types of therapy. [ 107 ]
The pre-test probability of an individual can be chosen as one of the following: The prevalence of the disease, which may have to be chosen if no other characteristic is known for the individual, or it can be chosen for ease of calculation even if other characteristics are known although such omission may cause inaccurate results
A pulmonary embolism (PE) occurs when a blood clot from a deep vein (a DVT) detaches from a vein , travels through the right side of the heart, and becomes lodged as an embolus in a pulmonary artery that supplies deoxygenated blood to the lungs for oxygenation. [28] Up to one-fourth of PE cases are thought to result in sudden death. [12]
Alternatively, post-test probability can be calculated directly from the pre-test probability and the likelihood ratio using the equation: P' = P0 × LR/(1 − P0 + P0×LR), where P0 is the pre-test probability, P' is the post-test probability, and LR is the likelihood ratio. This formula can be calculated algebraically by combining the steps ...
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The investigators then obtain a standard set of clinical observations on each patient and a test or clinical follow-up to define the true state of the patient. They then use statistical methods to identify the best clinical predictors of the patient's true state. The probability of disease will depend on the patient's key clinical predictors.
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