OBJECTIVES: To investigate the risk factors of recurrence of acute ischemic stroke (AIS) within 1 year and establish a nomogram model for predicting the recurrence risk. METHODS: This study was conducted in two cohorts of AIS patients (≤7 days) hospitalized in Dongzhimen Hospital (modeling set) and Fangshan Hospital (validation set) from March, 2021 to March, 2022. Lasso regression analysis was used to identify the important predictive factors for AIS recurrence within 1 year, and multivariate Logistic regression analysis was performed to analyze the independent factors affecting AIS recurrence. The recurrence risk prediction nomogram model was constructed using R studio, and its discriminating power and calibration were assessed using ROC curve analysis and Hosmer-Lemeshow goodness-of-fit test. RESULTS: The modeling and validation sets contained 28 cases (15.22%) and 21 cases (15.00%) of AIS recurrence, respectively. In the modeling set, compared with the non-relapse group, the recurrence group had higher proportions of patients with age >65 years, diabetes, arrhythmia, constipation after stroke, and FBG >7.5 and significantly higher levels of NLR, UREA, Cr, HbA1c, FIB and TT (P<0.05). Multivariate Logistic regression analysis showed that an age >65 years, arrhythmia, constipation after stroke, FBG >7.5, NLR and Cr were all independent risk factors of AIS recurrence (P<0.05). Hosmer-Lemeshow goodness-of-fit test and calibration curve analysis showed that the risk prediction model had good fitting between the modeling set and the verification set. The ROC curve showed that for predicting AIS recurrence within 1 year, the AUC of the predictive model was 0.857 (95%CI: 0.782-0.932) in the modeling set and 0.679 (95%CI: 0.563-0.794) in the validation set. CONCLUSIONS The nomogram model established based on age >65 years, arrhythmia, constipation after stroke, FBG >7.5, NLR and Cr has a good predictive value for AIS recurrence within 1 year.
Humans ; Nomograms ; Risk Factors ; Recurrence ; Ischemic Stroke/etiology* ; Female ; Male ; Logistic Models ; Aged ; ROC Curve ; Middle Aged ; Stroke/etiology*

OBJECTIVE:To evaluate technology innovation capability of pharmaceutical industry in 30 provinces(regions and cities)and explore suitable innovation mode for each type. METHODS:The entropy method was adopted to establish comprehen-sive evaluation mode of technology innovation capability of pharmaceutical industry. The mode was used to measure comprehensive score and ranking of pharmaceutical industry in 30 provinces(regions and cities). Those 30 provinces(regions and cities)were di-vided into 3 types,including high technology innovation ability (type Ⅰ),medium technology innovation ability (type Ⅱ) and low technology innovation ability (type Ⅲ),and relevant suggestions on innovation mode were put forward. RESULTS & CON-CLUSIONS:According to the establishment of evaluation index system that included innovation resources input capability,innova-tion output capability,innovation potentiality and innovation environment,significant differences were found among these regions in technology innovation capability. For those 8 provinces (regions and cities) with high technology innovation ability,such as Shandong,Jiangsu and so on,which belongs to the type Ⅰ,local pharmaceutical enterprises should adopt the innovation mode of independent innovation dominant,cooperative innovation as a supplement;for those 9 provinces(regions and cities)with medium technology innovation ability,such as Hebei,Sichuan and so on,which belongs to the type Ⅱ,local pharmaceutical enterprises should select the mode of cooperative innovation of the combination of production,teaching and research;for those 13 provinces (regions and cities) with low technology innovation ability,such as Hainan,Ningxia,which belongs to the type Ⅲ,should the mode of giving priority to imitation innovation and cooperative innovation as a supplement.