External Validation of Multiple Intravenous Thrombolysis Prediction Models

    


    


    Figure 7: The calibration curve for the START score.

    

The Brier scores, 0.2406, 0.0264, 0.1691, 0.2938, and 0.2266 for ASPECTS, ASTRAL, THRIVE-c, DRAGON, and START, respectively.

Discussion

The AUROC of the ASPECTS, ASTRAL, DRAGON, THRIVE-c, and START models for predicting 3-month adverse prognosis in our cohort of patients receiving intravenous thrombolysis for AIS was found to be in the acceptable good range for differentiating patients with a 3-month adverse prognosis. The difference did not reach a statistically significant level, however. In addition, although all five had acceptable predictive discrimination, analysis of their pair wise calibration showed that only the DRAGON score model was well calibrated and the accuracy of the remaining four models in predicting prognosis was less than optimal. The discrimination reflects the ability of the prediction model to distinguish between the occurrence and non-occurrence of outcome events; if a model has good discrimination, it can correctly distinguish patients with a high incidence of outcome events from those with a low incidence, focusing on the classification of groups of patients. Calibration refers to the agreement between the risk of occurrence of an outcome event predicted by the model and the actual observed probability of occurrence of the outcome event, i.e., the accuracy of predicting the probability of occurrence of an outcome event in a given individual. An ideal prediction model should have both discrimination and calibration. In this study, we found that the five models had fairdiscrimination in predicting the 3-month adverse prognosis of AIS patients, but the models, except DRAGON, were poorly calibrated, suggesting that they are insufficient to provide accurate information about the likelihood of adverse prognosis at 3 months for individual patients and are difficult to use as a tool to guide clinical AIS patient treatment decisions [38]. This study is the first domestic and international study to validate five models simultaneously as well as to perform simultaneous pre- or post-loop classification validation. The aspects [9] scoring entity model was introduced in 2000. The very beginning, it was based on the results of preoperative head CT examination of 117 patients. This patient received intravenous thrombolytic therapy for internal reasons of middle cerebral artery supply area infarction within 3 hours after the onset of the disease in two teaching hospitals in North America, only involving the CT examination results. In the subsequent experiments, it was found that the actual effect of aspects score of AIS patients was basically very different from the NIHSS score used in clinical medicine. In 2008 [39] Kimura K et al. Clearly proposed in Okayama, Japan that DWI level reliably predicted improper conclusions within 3 hours after the onset of venous thrombolysis, and DWI level ≤ nezu t et al [40] found that baseline DWI level ≥ 7 was related to better clinical medical conclusions (OR= 1.85; 95% confidence interval: 1.07 to 3.24), The aspect is related to death (OR= 3.61; 95% confidence interval: in 2011, nezu t et al. [41] Further found that for patients with hyperacute stroke, dwi-aspects score is about 1 point lower than ct-aspects. Aspects is a useful predictor of symptomatic cerebral hemorrhage and consciousness 3 months after the recombining of institutional fibrin chaperone. In recent years, Cheng x, Su x [42] et al. Will base on deep learning (Edwi aspect) The DWI Alberta cerebral apoplexy advocate initial computed tomography score calculated by the automated technical tool software of the compared with the evaluation of acute apoplexy by neurology emergency department doctors, and found that the evaluation of Edwi was the same as that of senior neurology emergency department doctors. Although the uncertainty scoring rules and the influence of the center line movement cause weak to moderate consistency in M5, internal capsule and caudate nucleus regions. Sakai K et al. [43] found in the experiment of intravascular therapy of the nervous system in Japan that according to the DWI interpretation of the join Smartphone Application and the DWI interpretation of the application platform PC monitor, the application of the smartphone application allows capillary neurologists to add .The diagnosis and treatment intelligent mobile phone application shows the huge market prospect of acute stroke management methods. Aspects score [44] has a high predictive analysis value for the recovery of patients with anterior and posterior circulation AIS patients after intravenous thrombolysis. It has been certified in China and other countries in the world.

The ASTRAL score [11] was developed in 2010 by Patrikk, a Swiss scholar, to predict the prognosis at 3 months after acute ischemic stroke by combining the patient's age, time from onset to admission, NIHSS score, blood glucose in the acute phase, visual field range deficit, and impaired level of consciousness, and does not require cranial imaging findings, making the scale much more useful. The ASTRAL score was first validated in China by Liu G [45] in 2013 as a reliable tool for predicting adverse outcomes at 3 and 12 months after AIS. 2013 Papavasileiou V et al [24] validated the ASTRAL score in the Athens Stroke Registry as a reliable predictor of 5-year functional prognosis and mortality in patients with acute ischemic stroke. In 2015 Vanacker [46] et al. developed the ASTRAL-R score in a cohort of patients from four stroke centers in Switzerland to predict 24-hour revascularization in patients undergoing intravenous thrombolysis, with 5 variables and a total score of 6 points, including 1 point each for glucose >7 mmol/l, severe extracranial stenosis, large artery occlusion,and visual field defects, and 2 points for reduced level of consciousness. A score of 2 was assigned to reduced level of consciousness. The Dragon [10] score (high-density artery, Mrs, age, blood sugar value, effect to treatment and NIHSS) was formulated in Germany in 2012. The predicted independent variables include high-density artery relative density / clinical symptoms of initial infarction, Mrs score before stroke, age, baseline blood sugar value, time from onset to use and baseline NIHSS score. In 2013, strbian d [23] and others found that in the innovative information of 12 stroke cores in Germany, Germany, Switzerland, Italy and Spain that continuously accept intravenous thrombolysis for the treatment of cerebral ischemic stroke, dragon score is well represented in the large collaborative sequence of anterior and posterior circulation stroke. In 2015, Zhang x [47] and others found that in China, the Dragon score showed a good predictive effect after the operation of institutional plasminogen chaperone protein. In order to use dragon for patients with MRI examination as baseline imaging data information, Turc [48] et al. Replaced the sign of high density of middle cerebral artery on CT with MRA proximal middle cerebral artery occlusion in France, and replaced the sign of initial infarction on CT with DWI ≤ 5 points in 2013. Other independent variables are saved. The total area under the ROC curve predicted by mri-dragon score for poor prognosis in AIS three months after intravenous thrombolysis was 0.83, which was higher than the Dragon score based on CT. In 2014 [49] Turc g et al. Of France clearly proposed and proved that mri-dragon score predicted the recovery of subacute cerebral ischemic stroke patients after receiving MRI treatment for 3 months after intravenous thrombolysis (IV tPA). 2020 lesenne a [50] et al found that ct-dragon score was used to predict the long-term effect of anterior circulation and posterior circulation after subacute stroke.

Thrive12score was formulated by flint et al in 2010 for patients with cerebral embolism (meci) and mechanical venous thrombosis elimination in multi meci experiments, which is composed of baseline NIHSS score, age and chronic disease score (including patients with high blood pressure, diabetes and atrial fibrillation). In the traditional thrive scoring system, NIHSS scores and age, which were continuous variables at the beginning, are converted into classification variables, and the risk of poor prognosis is determined by measuring the total score of each predictor. Thrive-c [27] model is a modification of the existing thrive score. This model converts all predictors into continuous variables and establishes a logical equation to accurately estimate the possibility of poor prognosis of individual patients in clinical treatment. Predicting the short-term or long-term adverse prognosis of AIS before and after the cycle time has the same prediction effect.

The START [13] model, developed and validated in 2018 from a national multicenter cohort in Italy (SITS-ISTR), consists of four non-categorical predictors (baseline National Institutes of Health (NIH) Stroke Scale score: 0-25, age 18 years, onset to treatment time: 0- 270 minutes and pre-stroke modified Rankin Scale score: 0-2,), can reliably predict the probability of poor prognosis in stroke patients receiving intravenous thrombolytic therapy. Also, the authors compared the START model with the DRAGON and ASTRAL models, both of which had higher predictive power (AUROC) than these two models.2019 Song B [28] et al. validated in China found that the START nomogram is a reliable and simple clinical tool to predict unfavorable.

Although all five models had acceptable AUROC values in distinguishing patients with different outcomes, the ideal calibration was not obtained when the five models were applied to our patient cohort. We guess the following possibilities. First, the most important factor may be the difference in the distribution of predictors and outcomes between the model building cohort and the prediction cohort, i.e., the case-mix effect [53]. Therefore, we compared the distribution of predictors and outcomes between the validation cohort and the original cohort. The proportion of patients with a 3-month adverse prognosis also differed. The distribution of predictors not included in the original prognostic model was also part of the impact of the case-mix. Second, studies have shown that more patients in Asia use 0.6 mg/kg alteplase [54]. In the cohort of this study, 67 (9.2%) patients used used low-dose alteplase, and the difference in the proportion of patients using low-dose alteplase may also be an important influencing factor. However, it is the difference in characteristics between the external validation cohort and the model development cohort that allows for validation of the generalizability of the model, and if the patient characteristics are extremely similar to those of the development model, it only reflects whether the predictive model is reproducible. In addition, the passage of time and changes in clinical practice, such as changes in current guidelines, patient management, and the increasing proportion of patients receiving endovascular therapy, may be another influencing factor on the predictive power of the model. Therefore, prediction models need to be continuously updated and improved to adapt to changes in clinical practice for better application and clinical service [55]. In addition to prediction accuracy, there are other influencing factors on whether a prognostic model can be applied in the clinic, such as convenience and validity in clinical practice, especially in the environment where AIS revascularization treatment is a matter of seconds. As a good early prognostic model, predictor variables need to be easily accessible and reliable, and the number of predictor variables should be minimized to facilitate clinical application. Models that are overly complex or include variables that cannot be routinely used in emergency situations (e.g., complex neuroimaging parameters) will be limited in their clinical use, even if they have perfect predictive power [56]. In this regard, ASPECTS requires only imaging information, DRAGON requires a combination of imaging, other explicit variables of the patient, and explicit variables in ASTRAL, THRIVE-c model, and START model, and does not require imaging information in the acute phase. This study is the first national and international study to compare three types of models simultaneously: imaging score alone, inclusion of imaging model, and exclusion of imaging information. The current study has some limitations. First, because our validation data came from a single database of stroke centers with a single sample source, the results are not representative of the entire Chinese population. Further external validation of the five model types using multicenter observations from more geographic regions is needed. Secondly, some patients were followed up by telephone, which may bias the results in part, and future studies should evaluate patients in the field as much as possible to reduce the bias of follow-up information. The amount of missing data (3.02%) from the final 3-month MRS follow-up may have influenced the study results.

In summary, this study confirmed the validity of the ASPECTS, ASTRAL, DRAGON, THRIVE-c and START scoring models in predicting the prognosis of patients with AIS 90 d after intravenous thrombolytic therapy by ROC curve analysis, and also compared the differences between the five prediction models, and validated the differences between the five prediction models from the pre- and post-loop subgroups. The DRAGON score was found to be more effective than the other four groups in predicting the prognosis of AIS patients at 90 d after intravenous thrombolytic therapy. In addition, all five model scores could be used for the predictive assessment of 90-d prognosis after intravenous thrombolysis in AIS patients in the anterior and posterior cycles. In the anterior cycle, ASPECTS was better than the other four groups in predicting 90-d prognosis after intravenous thrombolysis in patients with AIS. In the post-loop, the DRAGON model score was better than the other four groups in predicting the 90-d prognosis after intravenous thrombolytic therapy in patients with AIS. Our validation study found that the calibration of the remaining four models, except for the DRAGON model, was less than optimal. More studies are needed in the future to validate the performance ability of these five models in more cohorts and further model optimization to propose simpler models suitable for prehospital healthcare workers' prediction [57].

Conclusions

All five score prediction models, ASPECTS, ASTRAL, DRAGON, THRIVE-c, and START, predicted 3-month adverse prognostic risk in patients with AIS treated with intravenous thrombolysis in both anterior and posterior circulation lesions, but the DRAGON score had the highest predictive diagnostic value in the posterior circulation. the probability of the DRAGON score predicting prognosis in the prediction models was in better agreement with the actual probability was in good agreement, and the calibration of the remaining four prediction models was less than ideal.

Conflicts of Interest

The authors declare no conflict of interests pertinent to this study. There is no conflict of interests to be declared.

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