探讨ASPECTS、DRAGON和SEDAN评分对我国急性缺血性卒中患者静脉溶栓后急性期内出血转化的预测价值 Predictive Value of ASPECTS, DRAGON and SEDAN Scores for Hemorrhagic Transformation after Intravenous Thrombolysis in Patients with Acute Ischemic Stroke in China
Bibliographic record
Abstract
【摘要】 目的 探讨Alberta卒中项目早期CT评分（Alberta stroke program early CT score，ASPECTS）、DRAGON评分和SEDAN评分对我国急性缺血性卒中患者静脉溶栓后急性期内出血转化的预测价值。 方法 回顾性连续收集2012年12月-2017年12月在同济大学附属同济医院神经内科急诊收入的接受静脉溶栓治疗的急性缺血性卒中患者的临床资料，记录有关基线资料，并使用ASPECTS、DRAGON和SEDAN 3个量表进行评分。以住院期间出血转化作为观察终点。应用受试者工作特征（receiver operating characteristic，ROC）曲线评估量表对静脉溶栓后出血转化的预测诊断价值，ROC曲线下面积采用C值表示，通过C值比较3个量表的预测价值；使用Hosmer-Lemeshow（H-L）拟合优度[χ2（P）]检验法判断各模型与实际结果的拟合度；进行Logistic回归分析探讨各评分与溶栓后出血转化的关系。 结果 共纳入199例患者，ASPECTS、DRAGON和SEDAN评分在总体患者中C值分别为0.889、0.810和0.793；前循环中C值分别为0.889、0.823和0.788；男性组中C值分别为0.893、0.788和0.818；女性组中C值分别为0.882、0.808和0.720（均P<0.05）。ASPECTS、DRAGON和SEDAN评分在总体患者中H-L拟合优度检验结果分别为8.253、2.685和7.511；在前循环中分别为9.875、4.330和6.441；在男性组中分别为8.966、1.697和3.049；在女性组中分别为4.284、6.548和7.669（仅前循环和男性组的ASPECTS评分P＜0.05，余P＞0.05）。Logistic回归分析ASPECTS、DRAGON和SEDAN评分的OR值在总体患者分别为0.588、1.839和2.229，在前循环分别为0.567、1.951和2.198，在男性组分别为0.595、1.969和2.675，在女性组分别为0.573、1.833和1.787（均P<0.05）。 结论 ASPECTS、DRAGON和SEDAN评分量表均可用于急性缺血性卒中患者静脉溶栓后出血转化风险的预测，ASPECTS评分要优于另外2种评分模型。 【Abstract】 Objective To investigate the predictive value of ASPECTS, DRAGON and SEDAN scores for hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke (AIS). Methods The baseline data of consecutive AIS patients treated with intravenous thrombolysis from Department of Neurology of Tongji Hospital of Tongji University from December 2012 to December 2017 were retrospectively collected, and all patients were scored with ASPECTS, DRAGON and SEDAN scales. The primary endpoint was hemorrhagic transformation during hospitalization. The receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of the three scales, and the area under the ROC curve was expressed by C value; the goodness of fit of the three scales were judged by Hosmer-Lemeshow (H-L) goodness-offit test; and the correlation between the three scales and outcome events was evaluated by logistic regression analysis. Results A total of 199 patients were included in this study. The C value of ASPECTS, DRAGON and SEDAN scores in all patients were 0.889, 0.810 and 0.793, respectively; in anterior circulation were 0.889, 0.823 and 0.788, respectively; in male patients were 0.893, 0.788 and 0.818, respectively; in female patients were 0.882, 0.808 and 0.720, respectively (all P<0.05). The χ2 value of H-L goodness-of-fit test of the three scales in all patients were 8.253, 2.685 and 7.511, respectively; in anterior circulation were 9.875, 4.330 and 6.441, respectively; in male patients were 8.966, 1.697 and 3.049, respectively; in female patients were 4.284, 6.548 and 7.669, respectively (P<0.05 were only for ASPECTS in anterior circulation and male groups, and all the rest P>0.05). The OR in logistic regression analysis of the three scales in all patients were 0.588, 1.839 and 2.229, respectively; in anterior circulation were 0.567, 1.951, 2.198, respectively; in male patients were 0.595, 1.969 and 2.675, respectively; in female patients were 0.573, 1.833 and 1.787, respectively (all P<0.05). Conclusions ASPECTS, DRAGON and SEDAN scales all have strong predictive ability for the risk of hemorrhagic transformation after intravenous thrombolysis in AIS patients, and ASPECTS is superior to the other two prediction models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".