| Object:Establish a quantitative mathematical model to accurately predict the risk of intracranial aneurysm rupture.Quantitative analysis model can simulate various physical and dynamic phenomena and provide insight into why the aneurysms grow or rupture.Previous studies demonstrated that regression model can find which parameters could have important effect on it's rupture.In this research we have got an improved binary logistic regression model and validated it in a prospective evaluation of another patients group to assess it's stability.Methods:All the aneurismal patients underwent an examination of cerebral DSA test followed by a three-dimensional image reconstruction.Then we got the aneurysm's neck size,height and length as well as its parent vessel's mean diameters.Also,we calculated other two values:AR(height/neck size) and SR(length/mean diameter of parent vessel).All the above morphological parameters combined with the locations of aneurysm and the patient's basic information were used in the derivation of the backward binary regression model,which was applied to another 19 aneurysms in 19 patients(not included in the original derivation of model) to determine its logit value of rupture risk.Results:There were total 37 aneurysms(24 ruptured and 13 unruptured) observed in 37 patients.Four of the five morphological parameters were significantly larger(each with p＜0.05) in the ruptured aneurysms than in the unruptured aneurysms.The binary logistic regression model applied to the prospective research demonstrated its stability with a sensitivity and accuracy of 84.6%and 78.9%respectively.Conclusion:This binary logistic regression model of aneurysm rupture identified the status of an aneurysm with a good accuracy.The use of this technique and its validation suggests that biomorphometric data and their relationships may be valuable in determining the status of an aneurysm. |
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