Acute ischemic stroke (AIS) is a disturbance in cerebral blood flow and has been the leading cause of serious disability and death worldwide (Khandelwal et al., 2016; Kamel et al., 2020). As an acute emergency, AIS is estimated to lead to a loss of 1.9 million neurons per minute, causing irreversible brain injury around the infarction area (Saver, 2006). Timely treatment to remove the obstruction and restore the blood flow has been shown to improve the outcomes in AIS patients (Herpich and Rincon, 2020).

Intravenous thrombolysis with r-tPA is widely recommended to be beneficial for patients with ischemic stroke within 4.5 h of symptom onset (Dong et al., 2017; Phipps and Cronin, 2020). Nevertheless, not all patients can benefit from this therapeutic regimen. Only approximately 40% of the treated patients show improvement, whereas other patients still experience a low recanalization rate (Orban-Kalmandi et al., 2021). Thus, predicting the intravenous thrombolytic effect at an early stage is important for both the clinician and patient.

Machine learning (ML) algorithms are a crucial branch of artificial intelligence and can learn from complex data by using computational techniques to find potential characteristics to make predictions (Heo et al., 2019; Rajkomar et al., 2019; Sirsat et al., 2020). Compared to conventional statistics, ML includes multiple unique algorithms, such as the random forest classifier, support vector machine, and extreme gradient boosting (XGBoost), which can allow computers to create a model by iterative learning (Deo, 2015; Wang et al., 2020). As a multidisciplinary approach, ML algorithms are gaining popularity in addressing complex problems of healthcare decision-making, and some studies have demonstrated that ML tools provide better accuracy and discrimination (Kamal et al., 2018; Fan et al., 2021). Kalscheur et al. (2018) reported the use of ML to create a model that can help doctors discriminate clinical outcomes in those accepting cardiac resynchronization therapy and improve the shared decision-making with patients. In addition, Lee et al. (2020) also found that ML algorithms using magnetic resonance imaging features were much more sensitive than human readings in identifying patients after a stroke within the time window for acute thrombolysis.

Although ML algorithms have been widely used in the prediction of stroke outcomes, few studies have reported the predictive value of the thrombolysis effect at 1 h after r-tPA infusion. In this study, we aimed to develop a model using the ML algorithm to predict the potential effect of r-tPA at admission in patients with AIS.

AIS patients were divided into favorable and unfavorable effects according to the NIHSS score at admission and 1 h after r-tPA. The baseline characteristics of the patients have been reported elsewhere. Briefly, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of them 156 patients with an average age of 63.0 (55.0–70.25) years had a favorable effect and 197 patients with an average age of 63.0 (56.0–71.0) years had an unfavorable effect. At admission, the average NIHSS score for the patients with a favorable effect was 4.0 (2.0, 9.0) and for those with an unfavorable effect was 5.0 (3.0, 8.5). About 1 h after receiving rt-PA, the NIHSS scores were 1.0 (0.0, 2.0) and 4.0 (2.0, 8.0) for the favorable and unfavorable effect groups, respectively (Table 1).

Extensive efforts have been made to stratify the long-term outcomes of AIS after r-tPA (Di Lorenzo et al., 2021; Goh et al., 2021). However, early intervention for AIS is particularly important as a disease with a high mortality and disability rate. Thus, this study was designed to evaluate the risk factors which may influence the thrombolytic effect of r-tPA in the early stage of AIS by using ML algorithm. Our study using GBDT model demonstrated that Myo, RDW-CV, FDP, BNP, Glu, and GR are risk factors and should be considered before thrombolysis is applied in clinical scenarios. As an important tool in artificial intelligence, ML has recently received increasing attention and is widely utilized in medical data processing. One of the advantages of ML is the development of predictive models for prognostic outcomes among various confounding factors. Ou et al. (2020) reported a positive result in aneurysm rupture risk assessment using the ML method when compared with a conventional statistical model. Sung et al. (2020) found that ML algorithms that integrate clinical and neuroimaging information can better predict early neurological deterioration in patients after an acute minor stroke. Although these studies showed a good performance through the use of ML, they were not aimed at predicting the super-earlier therapeutic response of r-tPA. To the best of our knowledge, this is the first study to use ML algorithm to predict the 1-h clinical outcome of AIS patients after r-tPA thrombolysis.

Making an accurate prediction under a clinical scenario is usually difficult owing to the existence of too many potential heterogeneous factors and the inherent noise of data encountered in clinical care. Thus, it is preferable to use a simple model if it is sufficiently accurate for a particular application. In our study, we used both 14 and seven variables during the modeling. Both showed good prediction efficiency; however, because the use of seven variables is more feasible under real clinical scenarios, we applied the seven variables to the GBDT model.

Although ML algorithms enable a computer to process complex calculations between variables and outcomes to achieve a more relevant prediction, some aspects of ML algorithms, such as their “black box” characteristic, have limited their predictive value (Petch et al., 2021). Owing to the black box characteristic, ML algorithms are considered to lack a transparent interpretation of the learning. Thus, an ML algorithm model should be used along with the experiences of the physician, rather than as a clinical judgment tool.

To overcome the weaknesses of ML, the SHAP method, which employs a game theory–based approach, was used to interpret the predictions made by the best model. As a unified framework that improves the interpretability and maintains the predictability of complex models, the Shapley value can provide insight for determining the relationships among numerous variables. By using the SHAP method, we identified the characteristics of the important variables that contribute to a prediction, including APTT (time), Myo, and BNP.

The present study has several limitations that should not be ignored. First, this was a single-center retrospective study with a small sample size, which may influence the model efficiency. Further validation in a multicenter setting is required. Second, only the NIHSS score at 1 h was employed in the analysis to evaluate the thrombolytic effect, and long-term follow-up results should be added in the future study. Third, further validation and assessment of the model is required. Because AIS is a complex disease which may be influenced by various factors (such as disease history and home therapies), the analysis of this study was mainly based on the NIHSS score, which may not be enough to accurately predict the thrombolysis effect.

In summary, this study demonstrated the feasibility of applying ML algorithm in predicting the curative effect 1 h after thrombolysis. However, further studies with a larger cohort are needed to validate the model accuracy in the future, and the predictive value of the model needs to be further examined in clinical practice.