[Principal component analysis and integral methods of cerebral vascular hemodynamic parameters].
OBJECTIVE
To establish a predicting model for stroke according to cerebral vascular hemodynamic indexes and major risk factors of stroke.


METHODS
Participants selected from a stroke cohort with 25,355 population in China. The first step was to carry out principal component analysis using CVHI. Logistic regression with principal component and main risk factors of stroke were then served as independent variables and stroke come on as dependent variables. The predictive model was established according to coefficient of regression and probability of each participant was also estimated. Finally, ROC curve was protracted and predictive efficacy was measured.


RESULTS
The accumulative contribution rates of four principal components were 58.1%, 79.4%, 88.4% and 94.6% respectively. Seven variables were being selected into the equation with the first to fourth principal component as history of hypertension, age and sex. Area under ROC curve was 0.855 and optimal cut-off point was probability over 0.05. Sensitivity, specificity and accuracy of stroke prediction were 80.7%, 78.5% and 78.5% respectively.


CONCLUSIONS
The model established by principal component and regression could effectively predict the incidence of stroke coming on.
Cao
Yi-feng
YF
Shanghai Institute of Cerebral Vascular Disease Prevention and Cure, Shanghai 200433, China.
Wang
Gui-qing
GQ
Huang
Jiu-yi
JY
Guo
Xiu-e
XE
Guo
Zuo
Z
Yang
Yong-ju
YJ
Feng
Chun-hong
CH
D001921:Brain / Q000098:blood supply*; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D025341:Principal Component Analysis; D012307:Risk Factors; D020521:Stroke / Q000209:etiology*