Stroke, a sudden neurological disorder, is a leading cause of disability and death in adults (1). Among stroke cases, acute ischemic stroke (AIS) accounts for 60 to 80% (2). Intravenous thrombolysis with recombinant tissue-type plasminogen activator (rt-PA) within 4.5 h of onset of symptoms is the treatment of choice and significantly improves neurological function in patients (3). However, a certain percentage of patients continue to experience poor prognostic outcomes (4). Therefore, the early identification of patients at risk for poor outcome, along with timely and accurate therapeutic interventions, is crucial for improving patient recovery and outcomes (5, 6). A nomogram is a visual scoring model that utilizes biological and clinical variables to accurately calculate the probability of an individual patient’s risk for a specific clinical event (7). The chart is widely used for clinical decision-making in a wide range of conditions (8, 9). Nomograms surpass traditional scoring systems in their ability to more accurately identify patients with poor outcome, assist in selecting optimal treatment options, and enhance the quality of patient survival (10). The aim of this study was to construct a nomogram to predict the risk probability of poor outcome in AIS patients following intravenous thrombolytic therapy.

A total of 465 patients who received intravenous thrombolysis with rt-PA were enrolled in this study. A total of 42 patients were excluded from the study due to various reasons: 21 patients underwent endovascular thrombolysis, 6 patients had autoimmune diseases, and 15 patients had incomplete follow-up data. Ultimately, 425 patients were successfully enrolled in this study, among which 102 patients experienced poor outcome, resulting in an incidence rate of 24%.

All subjects who met the established criteria were randomly assigned to the training cohort (n = 298) and the validation cohort (n = 127) in a 7:3 ratio. The median age of participants was 66 (57, 72) years in the training cohort and 65 (59, 71) in the validation cohort. There were no significant differences between the two groups regarding age, gender, or medical history (all p > 0.05, Table 1).

Univariate analysis of the training set showed that NIHSS, SII, SIRI, age, and history of diabetes were significantly associated with poor outcome in patients with AIS. Further multifactorial logistic regression analysis confirmed that SII (OR = 1.001, 95% CI: 1.000–1.002, p = 0.008), SIRI (OR = 1.584, 95% CI: 1.122–2.236, p = 0.009), NIHSS (OR = 1.101, 95% CI: 1.044–1.160, p < 0.001), and history of diabetes mellitus (OR = 2.582, 95% CI: 1.285–5.188, p = 0.008) were identified as independent risk factors for the development of poor outcome in patients with AIS (Table 2).

A nomogram was constructed to predict poor outcome following intravenous thrombolytic therapy in patients with AIS, based on independent risk factors (Figure 1). The likelihood of poor outcome for the corresponding patients was subsequently calculated. The predictive efficacy of this line plot was evaluated using area under the receiver operating characteristic curve (ROC-AUC) analysis (Figure 2; Table 3). The AUC values for the training and validation cohorts were 0.854 (95% CI: 0.807–0.901) and 0.855 (95% CI: 0.783–0.927), respectively. Calibration plots indicated that the predicted values for both the training and validation cohorts generally aligned with the actual observed values (Figure 3). To further validate the predictive ability of this nomogram regarding the incidence of poor outcome, we employed a decision curve analysis. As illustrated in Figure 4, the net benefit of utilizing the nomogram was found to be relatively high within the threshold probability ranges of 4.28 to 66.4% for the training cohort and 4.01 to 67.8% for the validation cohort.

This study included a total of 425 patients with acute ischemic stroke (AIS) who were treated with intravenous thrombolysis. Among these patients, 102 (24.0%) exhibited a poor outcome, indicating a high probability of adverse outcomes in AIS patients. Furthermore, the existing prediction models appear inadequate for accurately assessing patient outcome. Consequently, there is an urgent need for the development of new prediction models to assist clinicians in providing precise risk assessments and improving patient outcomes.

Our study identified that the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), National Institutes of Health Stroke Scale (NIHSS) score, and a history of diabetes were independent risk factors for poor short-term outcome following intravenous thrombolytic therapy in patients with acute ischemic stroke (AIS). Utilizing these independent risk factors, we developed and validated a nomogram prediction model aimed at forecasting poor outcome after intravenous thrombolytic therapy in AIS patients. Among these, SII and SIRI serve as stable markers of the inflammatory response and can be easily obtained through blood cell counts, making them both practical and reproducible. Originally proposed to be associated with poor outcomes in cancer patients (12, 13), SII and SIRI incorporate a composite index of three distinct leukocyte and platelet subpopulations, offering new insights by integrating the interplay of platelet increase, inflammation, and immunity (14, 15). As novel indicators of inflammation, SII and SIRI are linked to the inflammatory response and secondary brain damage in patients with AIS (16). Following the onset of cerebral ischemia, inflammatory cells respond rapidly, with neutrophils being the first to appear in the penumbra and infarct core (17). Once activated, neutrophils exacerbate brain edema and damage the blood-brain barrier by releasing pro-inflammatory mediators and metalloproteinases. Concurrently, additional monocytes migrate to the ischemic brain tissue, further worsening cerebral ischemia-reperfusion injury (18). An animal experiment showed that necrotic platelets interact with neutrophils during reperfusion, and rapid platelet exposure is extremely critical for inducing CypD-mediated platelet necrosis and necrosis-dependent brain injury (19). In contrast, lymphocytes primarily serve a protective function, capable of modulating the inflammatory response following a stroke, mitigating the disruption of the blood–brain barrier, and facilitating the recovery of neurological function (20). Furthermore, previous studies have indicated that elevated SII and SIRI can serve as predictors for post-stroke cognitive impairment in patients (21, 22). Additionally, research has demonstrated that SIRI and SII values correlate with a poor 90-day outcome in patients with AIS who have undergone intravenous thrombolysis, aligning with the findings of this study (23, 24).

This study demonstrates that the NIHSS score is an independent risk factor for poor 90-day outcome in patients with AIS, aligning with findings from previous research (25, 26). The NIHSS score serves as an indicator of disease severity in AIS patients; an elevated score suggests greater vascular occlusion or compromised collateral circulation capacity (27). Furthermore, the NIHSS score can predict the location and extent of infarction in stroke patients (28). Research indicates that the baseline NIHSS score also predicts post-thrombolytic hemorrhagic transformation in patients undergoing rt-PA intravenous thrombolysis (29, 30). In this study, a history of diabetes emerged as an independent risk factor for poor outcome following intravenous thrombolysis in AIS patients, with those having diabetes being 2.582 times more likely to experience poor outcomes compared to their non-diabetic counterparts. Tang et al. demonstrated that diabetes is an independent predictor of early neurologic improvement after IVT and a risk factor for incomplete recanalization at 24 h post-treatment (31). Additionally, previous studies have indicated that stroke patients with diabetes mellitus exhibit higher rates of disability and mortality following hospital discharge (32).

In the present study, novel inflammatory markers SII and SIRI were incorporated into the Short-term Adverse Outcome Scale for patients with AIS. Independent risk factors, including the NIHSS score and a history of diabetes mellitus, were also integrated into the nomogram to explore the risk of poor outcomes in AIS patients following intravenous thrombolysis in a comprehensive and cohesive manner. Almost all healthcare organizations, even those in economically disadvantaged areas, have these predictors readily available. Furthermore, validation of the nomogram indicated that the predictive model exhibited good differentiation and calibration, suggesting it may serve as a reliable tool for predicting the risk of poor outcomes in AIS patients treated with rt-PA. For patients identified by our nomogram as being at high risk for poor outcomes following thrombolysis, clinicians may consider alternative treatments to enhance clinical benefits. However, this study has certain limitations: first, it is a retrospective analysis; second, it is a single-center study, and its results have not been fully validated in an external cohort. Future prospective multicenter studies are necessary. More importantly, external validation in other cohorts is still needed before formal routine clinical practice can be established. This will provide a more reliable scientific basis for the therapeutic and prognostic assessment of patients with AIS.

Our study proposes a novel and practical nomogram that incorporates SII, SIRI, the NIHSS score, and a history of diabetes to effectively predict the probability of an unfavorable outcome at 90-day following intravenous thrombolysis in patients with ischemic stroke.