Development Of A Short-term Prognosis Evaluation System In Ischemic Stroke Based On Genetic Background And Laboratory Indicators

Objective: We studied the correlation between susceptibility genes SNP loci of ischemic stroke and patient prognosis,and further combined genetic background,clinical information,imaging and test indicators,a three-month short-term prognosis evaluation system for ischemic stroke was established.Methods: A total of 433 patients with ischemic stroke were consecutively enrolled in the Lanzhou University Second Hospital from December 2020 to December 2021.The initial blood sample,basic clinical data,NIHSS scores,imaging,and hematological examination results after admission were collected,and the three-month short-term prognosis was assessed using the modified m RS.The laboratory-built PCR-HRM molecular diagnostic method was used to genotype 14 SNP loci which have been found.After univariate analysis of all research indicators,random forests were used to rank the importance of statistically significant variables,LASSO cross-validation and stepwise backward regression were used to optimize the included variables,and the area under the ROC curve was used to select the optimal indicator combination of the model.A short-term prognosis evaluation system was established by multivariate binary logistic regression,and the factors of the final prediction model were represented by a nomogram.Hosmer-Lemeshow（HL）test and C-index calibration plot were used to evaluate the discriminative ability of the model training set and validation set,and the decision curve analysis was used to evaluate the net benefit of the decisions made by the model to clinical patients.Results:（1）A total of 433 patients with ischemic stroke were enrolled in this study,with an average of 64.22 ± 10.94 years,including 299 males（69.1%）,and 134 females（30.9%）.（2）The wild-type loci of rs12037987（OR = 0.480,95%CI = 0.307-0.777,P =0.002）,rs4932370（OR = 0.518）,95%CI = 0.236-0.823,P = 0.005),and rs2229383（OR= 0.438,95%CI = 0.255-0.750,P = 0.003）reduced the risk of poor prognosis in ischemic stroke.The wild-type loci of rs16896398（OR = 2.688,95%CI = 1.646-4.390,P = 0.002）increased the risk of poor prognosis for ischemic stroke.Besides,the rs17612742 and rs6825454 loci were associated with prognosis only in codominant and dominant models.（3）Univariate analysis showed that there were 59 indicators with differences between the good and poor prognosis groups,and 22 modeling variables were obtained after optimizing by random forest,LASSO cross-screening and step-by-step backward method.Multivariate binary logistic regression established a short-term prognostic evaluation system based on 10 indicators,including NIHSS score,previous stroke,education level,TAT,GLU,IL6,Fe,rs12037987,rs4932370 and rs16896398.（4）The HL test showed that the model has good goodness of fit.The training set C index is 93.1（95% CI: 91.3-96.5）,and the validation set C index is 96.6（95% CI:93.0-100.0）.Decision curve analysis showed that the model The short-term net clinical benefit for patients with ischemic stroke was better.Conclusion: In this study,a three-month short-term prognosis evaluation system based on genetic background and laboratory indicators was successfully developed and verified,and finally 10 predictors were included.This evaluation system provides an individualized plan for early prediction of poor prognosis in patients with ischemic stroke.more comprehensive assessment information.