Volume 19 Issue 10
Oct.  2021
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Article Navigation >  Chinese Journal of General Practice  > 2021  >  19(10): 1666-1668,1696
WANG Ya, ZHOU Hou-yuan, LUO Zhi-qiang, MENG Ding-qiang, ZHENG Ying-ying. Predictive value of the new predictive model AULTS score for ischemic stroke[J]. Chinese Journal of General Practice, 2021, 19(10): 1666-1668,1696. doi: 10.16766/j.cnki.issn.1674-4152.002137
Citation: WANG Ya, ZHOU Hou-yuan, LUO Zhi-qiang, MENG Ding-qiang, ZHENG Ying-ying. Predictive value of the new predictive model AULTS score for ischemic stroke[J]. Chinese Journal of General Practice, 2021, 19(10): 1666-1668,1696. doi: 10.16766/j.cnki.issn.1674-4152.002137
shu

Predictive value of the new predictive model AULTS score for ischemic stroke

doi: 10.16766/j.cnki.issn.1674-4152.002137
  • 1.

    Department of Neurology, Chongqing Tongliang District Hospital of Traditional, Chinese Medicine, Chongqing 402560, China

Funds:

 81760043

  • Received Date: 2020-11-01
    Available Online: 2022-02-15
    Abstract
  •   Objective  To establish a new predictive model to provide a new method for the assessment of the risk of stroke.  Methods  Data were collected from hospitalised patients who were clinically diagnosed with stroke in the Department of Neurology, Chongqing Tongliang District Hospital of Traditional Chinese Medicine from 2012 to 2017. The controls were selected from subjects of outpatient physical examination during the same period. SPSS 22.0 software and R software were used for data analysis and model construction. We selected variables according to the area under the ROC curve. These variables were used to construct the model. The model construction was carried out in R software and presented with a nomogram.  Results  Univariate analysis showed significant differences in drinking, uric acid, blood lipids and systolic blood pressure between the ischemic stroke group and the control group (all P < 0.05). The variables that entered the final model included age (AUC=0.737), uric acid (AUC=0.567), triglycerides (AUC=0.537), low-density lipoprotein cholesterol (AUC=0.541) and systolic blood pressure (AUC=0.615). The area under the ROC curve of the predictive value of the predictive model for stroke was 0.789 (95% CI: 0.765-0.814, P < 0.001). We constructed a new nomogram based on the model prediction score. According to the model prediction probability score quartile, the probabilities of stroke in Q1, Q2, Q3 and Q4 were 18.3%, 40.3%, 60.0% and 82.7%, respectively.  Conclusion  This study shows that the new risk prediction model has good predictive value for stroke and is worthy of popularisation and application.

     

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