Due to its well-established high efficiency and relative safety, mechanical thrombectomy (MT) has been considered the first-line strategy for managing acute anterior circulation (AC)-large-vessel occlusion (LVO) for the past several years (1–5). MT in patients with LVO-induced stroke, even with an extended time window of 24 h (6, 7), has demonstrated improved recanalization rates (58.7 to 88%), amelioration of functional outcomes, and reduced disability rates at 90 days (8). Despite these beneficial effects, more than half of the patients (51–56.7%) still exhibit a non-independent functional outcome (modified Rankin Scale [mRS] score of ≥3 at 90 days) after MT (6, 7, 9). In addition, 37.1% of patients who underwent MT were severely disabled for life (mRS score of ≥4 at 90 days), and even with successful recanalization following MT, only 27% of the patients were disability-free at 90 days. The definition of non-independent functional outcomes after MT has been a topic of debate, with most studies suggesting that non-functionally independent outcomes include mainly moderate disability, severe disability, very severe disability, and death. However, moderate disability is characterized by the ability to walk with help or assistance, which, in reality, is an acceptable functional outcome after MT for acute LVO. Furthermore, the median mRS score for the 90-day clinical outcome in most RCT studies comparing the findings with medication was 3, which was closer to the mRS score of 2 rather than 4 in clinical practice (10). Therefore, more clinical attention should be directed toward the poor outcomes that impose an unbearable burden on stroke patients, such as the inability to walk and to look after themselves with necessary help, and even worse scenarios.

At present, to ensure the maximum benefit of MT for LVO stroke patients, a large number of neurologists are committed to exploring the risk factors for unfavorable outcomes (a 90-day mRS score of 3–6) after MT by using various clinical predictive models and scoring systems for the accurate prediction and precise assessment of the surgical procedure (11, 12). While a vast majority of such studies (13–16) have focused on non-independent functional outcomes (mRS score of 3–6) as unfavorable outcomes at 90 days following MT for acute stroke, few studies have addressed the 90-day poor outcomes (mRS score of 4–6) in AC-LVO patients who underwent MT, and studies reporting the risk factors for poor outcomes in this patient population are even more scarce.

Thus, the present study aimed to identify the clinical risk factors for 90-day poor outcomes (mRS score of 4–6) after MT in patients with acute AC ischemic stroke, to develop a nomogram model for predicting the probability of poor outcomes at 90 days in this population, and to validate the performance of this nomogram using an external single-center dataset.

The present study developed a nomogram model composed of age, glucose level, baseline NIHSS score, unsuccessful recanalization, and END to predict the probability of a poor outcome at 90 days after MT in patients with AC-LVO. This prognostic nomogram for the prediction of 90-day poor outcomes was derived from a prospective, multicenter, randomized clinical trial of MT with two flow reconstruction devices within 8 h of symptom onset in AIS. Additionally, it was externally validated in a retrospective cohort study from another single center. The nomogram showed good predictive power with C-index values of 0.763 (0.693–0.832) in the training set and 0.804 (0.719–0.889) in the validation set.

In the present study, 45.2% (84/186) of the patients developed poor outcomes (mRS scores of 4–6) at 90 days after MT, which was comparable to the corresponding values (37.1%) in the highly effective reperfusion evaluated in the multiple endovascular stroke (HERMES) clinical trial (9) and the individual patient data meta-analysis on the RCTs of MT across a 6–24 h time window (AURORA trial; 40%) (17). The incidence of poor outcomes (mRS scores of 4–6) at 90 days after MT in AC-LVO patients documented by the previous RCTs varied substantially (6–8), ranging from 12 to 48.79%, which was expected to be further reduced by actively screening the relevant risk factors of prognosis. In addition, we achieved a successful recanalization rate of 76.3%, consistent with the 71% rate in the HERMES trial (9) and 81% in the AURORA clinical trial (17). With the sICH rate of 5.4% and a mortality rate of 20.9% being comparable to those in the HERMES (4.4 and 15.3%, respectively) (9) and AURORA (5.3 and 16.5%, respectively) trials (17), our study confirmed that MT was safe for treating AIS patients with AC-LVO within 8 h of symptom onset.

At present, scoring systems (13, 14, 18–24) and nomograms (16, 25, 26) are the two main types of prognostic prediction tools for accurately and conveniently predicting clinical outcomes after MT. Although the existing scoring systems, including the THRIVE score (20), PRE score (14), and MR PREDICT score (27), have been externally validated, most of them focused on the 90-day independent functional outcomes (mRS scores of 0–2) or unfavorable outcomes (mRS ≥ 3), with less focus on the 90-day poor outcomes (mRS ≥ 4) (Supplementary Table S2) (12, 18, 19, 21, 22, 28). Among these, HAIT (18) and HIAT2 (19) were designed for the prediction of poor outcomes (mRS scores of 4–6) at discharge, which may have undermined their efficiency in predicting longer prognoses in clinical practice. The subsequent SAD score (22) and mTHRIVE score (21) that incorporated radiology variables based on the previous PRE (14) and THRIVE scores (20) were effective when applied for the prediction of 90-day poor outcomes; nevertheless, population-based cohort trials in large sample sizes were required to validate the utility of these scoring tools in clinical practice. Most of the recently developed nomogram models for predicting the 90-day outcomes after MT in acute AC-LVO patients were focused on either a good outcome (mRS scores of 0–2) or an unfavorable outcome (mRS scores ≥3). To our knowledge, the nomogram model constructed in this study is the first of its kind for the prediction of 90-day poor outcomes (mRS scores of 4 to 6) after MT in patients with AC-LVO, which provided a more accurate individualized estimation of the risk probability of poor outcomes (ranging from 0.2 to 0.95) by translating the nomogram into a formula instead of tracing lines along the scheme.

Prognostic prediction models have been classified into pre-procedure and post-procedure tools (11), which would be helpful for the selection and management of thrombectomy patients in decision-making. However, the variables used by scoring systems and nomogram models for predicting the risk of unfavorable outcomes vary considerably (12), posing a dilemma for their clinical application. In our study, three pre-treatment parameters (age, baseline NIHSS score, and glucose level on admission) were incorporated into the nomogram model. Previous studies have reported that advanced age is strongly associated with poor clinical outcomes in patients receiving MT (29–31). In the PRE scoring system, it was also reported that, when applied to assess a good prognosis for octogenarian patients who have successfully achieved recanalization, the requirement was for their PRE scores to be within a specific range (14). Unlike previous studies that converted ages into a categorical variable by dichotomizing them on the basis of the median value, our study included age as a continuous variable in the nomogram and reported a contribution of 95 points to the total score to ensure complete utilization of within-category information. The baseline NIHSS score is a well-recognized variable that has been shown to be associated with 90-day functional independence following MT in AC-LVO (14, 19, 23). In our nomogram model, the baseline NIHSS score contributed 74 points to the total score for patients predicting the 90-day poor outcomes after MT in acute AC-LVO patients, although it was not an independent predictor of poor outcomes in the final nomogram model. The mechanisms by which hyperglycemia damages the blood vessels, causes the accumulation of extracellular glutamate, disrupts the blood–brain barrier, and eventually aggravates brain injury have been well established (32–34). Therefore, higher levels of glucose would exacerbate the clinical outcomes in patients with LVO. Kim et al. (35) and Goyal et al. (36) demonstrated that hyperglycemia increased the risk of poor outcomes of incomplete reperfusion after MT, supporting the synergistic effect of ischemia–glucose concentration. Similarly, our study found that the glucose level on admission significantly contributed to a higher risk of poor outcomes in LVO patients after MT in the prognostic nomogram model, with 100 points contributing to the total score of the nomogram model. Consistently, the meta-analysis conducted by Calos et al. (37) also demonstrated that hyperglycemia could increase the risk of unfavorable clinical outcomes in LVO patients undergoing MT.

Unlike the previous scoring systems and the nomogram models based on pre-treatment variables for prognostic prediction, we developed a nomogram that also incorporated two post-treatment variables for predicting the probability of 90-day poor outcomes in AIS patients eligible for MT. Although the reasons why 19–43% of AIS patients with futile recanalization exhibited unfavorable outcomes (23) and 34% of AIS patients with failed recanalization obtained favorable outcomes (38) have not been fully elucidated, the beneficial effect of MT in AIS patients remains undeniable. Nevertheless, an unsuccessful recanalization rate of 12–41.3% (8) has been associated with the unfavorable outcome of AIS patients after MT, weakening the clinical efficacy of MT to a certain extent. In the IER-START nomogram (16), the reperfusion grade was demonstrated as a strong post-treatment radiological predictor of unfavorable outcomes 3 months after thrombectomy. The most recently published study of a clinical nomogram also reinforced the increased risk of 3-month unfavorable outcomes after unsuccessful recanalization (25). Consistently, we found that unsuccessful recanalization was a strong predictor of poor clinical outcomes, with a contribution of 44 to the total score, in patients with AIS following MT and can be effectively used to estimate the probability of poor outcomes after MT. The incidence of END in LVOS patients after MT is 35–42% (39–41), and it has been identified as a definite risk factor for 90-day poor clinical outcomes in AIS patients treated with MT (40, 42–44). Unfortunately, no previous study has reported its role in predicting the probability of unfavorable outcomes in AIS patients treated with MT. We employed END as a risk factor that exhibited strong power in predicting the probability of a 90-day poor outcome, with a contribution of 40 points to the total score.

Our post-treatment nomogram model consisting of five variables (age, baseline NIHSS score, glucose level on admission, unsuccessful recanalization, and END) exhibited very good efficacy in external validation and can be used to accurately estimate the probability of a poor outcome at 90 days for LVOS patients who underwent MT within 8 h after the stroke onset. This post-MT nomogram model can be easily translated into practice and can complement existing models.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving humans were approved by the ethics committee of PLA Rocket Force Characteristic Medical Center (X2017008). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

XL: Writing – original draft, Data curation, Investigation. CL: Writing – original draft, Writing – review & editing, Investigation, Methodology, Project administration, Resources. A-fL: Writing – original draft, Writing – review & editing, Investigation, Methodology. C-cJ: Investigation, Writing – review & editing, Methodology, Project administration. Y-qZ: Investigation, Methodology, Writing – review & editing. YL: Investigation, Methodology, Writing – review & editing. Y-yZ: Investigation, Writing – review & editing, Formal analysis. H-yL: Formal analysis, Writing – review & editing, Software. W-jJ: Writing – review & editing, Conceptualization, Funding acquisition, Methodology, Resources, Supervision. JL: Conceptualization, Supervision, Writing – review & editing, Formal analysis, Writing – original draft.