Development of Novel Inflammatory Biomarkers Integrated with Machine L

Introduction

Stroke ranks as the second leading cause of global mortality and third in Disability-Adjusted Life Years (DALYs), projected to ascend to the second position by 2050.1 Ischemic stroke predominates (65.3% of incident cases), with large artery occlusion (LAO) constituting 30–40% of acute ischemic stroke (AIS) and strongly correlating with 90-day mortality.2,3 MT is established as the standard-of-care for LAO-AIS through pivotal randomized trials.4–6 Nevertheless, substantial disparities persist: 90-day mortality and disability rates remain markedly elevated in low- and middle-income countries (LMICs), which bear 83.3% of the global stroke burden, with Asia contributing 61%.7–10 Despite MT’s efficacy, reperfusion injury and malignant cerebral edema represent prevalent, life-threatening complications that significantly contribute to post-procedural mortality, underscoring the need for improved risk stratification tools.11

Post-ischemic stroke, rapid neuroinflammation occurs at the infarction core.12 Neutrophils, as pivotal mediators in the thromboinflammatory cascade, critically interact with platelets, amplifying inflammation and dysregulating the immune microenvironment – processes directly implicated in secondary brain injury.13,14 The NPR, calculated from admission peripheral blood, reflects a relative neutrophilia and systemic inflammatory state. Elevated NPR signifies enhanced neutrophil activation and platelet consumption, facilitating neutrophil recruitment to the infarct via thromboinflammatory pathways and establishing a deleterious “vicious cycle” that exacerbates blood-brain barrier disruption and edema.14 Critically, excessive neuroinflammation driven by these mechanisms (eg, complement activation, inflammatory mediator release) worsens tissue damage, infarct expansion, and poor outcomes.15 Given evidence that early anti-inflammatory intervention improves prognosis,16,17 this study aims to develop and validate a novel NPR-based predictive model, leveraging machine learning, to forecast early (90-day) post-MT mortality in LAO patients. This will enable timely identification of high-risk individuals for targeted therapeutic strategies.

MethodsPatient Selection Statistical Analysis

This retrospective study analyzed data from all 320 patients diagnosed with LAO-AIS who underwent MT at the Affiliated Hospital of Guilin Medical University between January 2023 and January 2025. Data with a missing rate of ≥15% were excluded, while those with a missing rate of ≤15% were processed using multiple imputation. The study was approved by the Ethics Committee of the Affiliated Hospital of Guilin Medical University and conducted in accordance with the Declaration of Helsinki.

Patients were included if they met the following criteria (Figure 1):

Figure 1 Patient selection.

Abbreviations: LAO-AIS, large artery occlusion acute ischemic stroke; MT, mechanical thrombectomy.

1. Admission within 24 hours of symptom onset, presenting with neurological deficits confirmed as LAO-AIS by cranial CT angiography (CTA) or digital subtraction angiography (DSA).

2. Eligibility for MT and subsequent treatment at our hospital. The indications for mechanical thrombectomy included:

NIHSS score ≥ 6 with symptoms attributable to the occlusion of the responsible vessel.

Alberta Stroke Program Early CT Score (ASPECTS) ≥ 6; for patients with large infarctions and ASPECTS ≤ 5, thrombectomy was performed if a salvageable ischemic penumbra was present.18

Absence of intracranial hypodense lesions affecting more than one-third of the cerebral hemisphere or intracranial hemorrhage as confirmed by CT or Magnetic Resonance Imaging (MRI).

For anterior circulation strokes, imaging criteria met the Deferring Antiplatelet Therapy Until Symptomatic Intracranial Stenosis (DAWN) or Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution with Intravenous Alteplase (DEFUSE-3) guidelines, allowing an extended treatment window of up to 24 hours post-onset.

For posterior circulation strokes due to basilar artery occlusion, the MT window was extended to 12–24 hours based on the Basilar Artery Occlusion Chinese Endovascular Trial (BAOCHE trial).19

3. Age ≥ 18 years.

Patients were excluded if they met the following criteria:

(1) Lack of blood test information, n=14 (2) Comorbid Life-Threatening Conditions: Patients diagnosed with severe comorbidities that carry a high short-term mortality risk, which could independently influence the study outcome and confound data interpretation. Specific exclusions in this category encompassed. Severe Liver Disease: Such as decompensated cirrhosis or severe hepatitis. Active Malignancy: Both treated and untreated metastatic or hematological cancers. End-Stage Organ Failure: Including end-stage renal disease (on renal replacement therapy), severe heart failure (NYHA Class III–IV), and advanced respiratory failure (eg, requiring long-term oxygen therapy).(Total number of patients excluded based on the above conditions: n=11) (3) Patients with venous vessel occlusion, n=8 (4) Lost to follow-up patients, n=15

Early mortality was defined as death occurring at discharge or within 90 days post-discharge.

Data Collection

The research team gathered baseline clinical data for all patients, encompassing age, gender, atrial fibrillation, hyperlipidemia, hypertension, diabetes, smoking and alcohol consumption. Mortality data were ascertained through medical records for inpatients and follow-up visits/telephone interviews for discharged patients. LAO and responsible arterywas defined as occlusion of proximal intracranial arteries, including: Anterior circulation: Internal carotid artery (cervical/intracranial), M1/proximal M2 segment of middle cerebral artery, A1 segment of anterior cerebral artery; Posterior circulation: Intracranial vertebral artery (V4), basilar artery, P1 segment of posterior cerebral artery. Occlusion was confirmed by CTA/MRA/DSA with vessel diameter ≥2.0 mm. Hyperlipidemia is characterized by elevated triglycerides and/or total cholesterol, elevated Low-Density Lipoprotein Cholesterol (LDL-C), and reduced High-Density Lipoprotein Cholesterol (HDL-C). Hypertension was defined as a documented history of the condition at admission or a systolic/diastolic blood pressure of ≥140/90 mmHg upon admission. Diabetes was identified based on a prior diagnosis at admission. Atrial fibrillation was classified as either a pre-existing condition or a diagnosis confirmed by electrocardiogram. The study utilized the first laboratory test results obtained at admission. The original baseline data are detailed in Appendix A. Early mortality was defined as death occurring either at discharge or within 90 days post-discharge. The NPR was calculated as NPR = neutrophils/platelets. Given that the raw NPR values might be excessively small, potentially compromising the model’s stability, the study applied centering and standardization to NPR, represented as NPR_std.

Statistical Analysis

Continuous baseline data following a normal distribution were presented as mean ± standard deviation, with intergroup comparisons performed using t-tests. For non-normally distributed continuous data, values were expressed as median and interquartile range, and intergroup comparisons were conducted using rank sum tests. Categorical variables were reported as percentages, n (%), with comparisons between groups performed using chi-square tests. When more than 20% of the cells in the contingency table had expected frequencies of <5, Fisher’s exact test was applied. Multiple imputation, implemented via the mice package, was used to address missing data comprising less than 15% of the original dataset, with 10 imputations performed. The dataset with the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values was selected for analysis in this study (Appendix B).20

The Boruta feature selection algorithm was employed to identify variables for inclusion in the logistic multiple regression analysis, with 100 iterations.21 Independent predictors were then integrated to construct a predictive model, which was randomly partitioned into a training set (n = 224) and an internal validation set (n = 96) at a 7:3 ratio. The model’s predictive performance was assessed using the receiver operating characteristic (ROC) curve, while its discriminative capability was verified through calibration plots. Decision curve analysis was conducted to evaluate the practical utility of the nomogram in clinical decision-making. Additionally, four machine learning algorithms—DT, XGBoost, SVM, and NB—were employed for model development. Among these, the best-performing XGBoost model underwent SHAP analysis to interpret variable importance. All statistical analyses were conducted using R software (version 4.4.2), with a significance threshold of P < 0.05.

Results

All results were derived from the multiply imputed dataset. The study included a total of 320 participants, comprising 67 deceased and 253 surviving patients. Significant baseline differences were observed in Age, DC, Responsible Artery, WBC, NE, PLT, NPR, and NPR_std (P < 0.05; Table 1). The baseline characteristics of the imputed dataset showed no substantial deviation from those of the original data (Appendix A).

Table 1 Patient Baseline Data Table

Eight variables, namely NPR_std, Age, DC, WBC, Responsible arterie, LYM, PT, and D-D, were selected by Boruta feature screening (Figure 2). As NPR_std is composed of neutrophils and platelets, these two variables were not incorporated into the Boruta feature screening. The feature details are presented in Appendix C.

Figure 2 Boruta feature screening map.

Abbreviations: DC, decompressive craniectomy; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; NPR_std, centralized and standardized neutrophil-to-platelet ratio; LYM, lymphocyte; MO, monocyte; PT, prothrombin time; APTT, activated partial thromboplastin time; D-D, d-dimer; FIB, fibrinogen; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ALB, albumin; K, potassium; Ca, calcium; Cl, chlorine.

Notes: The core of Boruta is to test whether a feature’s predictive importance is significantly greater than that of randomized “shadow features” created by permuting the original data. This establishes a benchmark for significance (the maximum shadow feature importance, or “shadow Max,” represented by the rightmost blue marker). Through an iterative process, features were categorized as follows: Confirmed predictors (Green), including NPR_std, Age, DC, etc., whose importance consistently surpassed the “shadow Max” threshold; and Rejected features (Red), which were deemed statistically insignificant as their importance was lower than the random benchmark. This rigorous iterative testing guarantees the selection of features that demonstrate stable and significant importance beyond random chance.

The selected variables were incorporated into the logistic multiple regression analysis (Figure 3). As shown in Table 2 and Figure 3, NPR_std, Age, DC, Responsible Artery, LYM, and PT were identified as independent predictors of mortality in LAO-AIS patients. The results indicate that for each additional year of age, the risk of death increases by 0.10 times (P < 0.001, OR = 1.10, 95% CI = 1.05–1.15). Patients who underwent DC exhibited a 0.80-fold reduction in mortality risk (P < 0.001, OR = 0.19, 95% CI = 0.07–0.44). Each unit increase in NPR_std was associated with a 3.51-fold increase in mortality risk (P < 0.001, OR = 4.51, 95% CI = 2.90–7.49). Compared to patients with posterior circulation involvement, those with anterior circulation as the responsible artery had a 0.65-fold decrease in mortality risk (P = 0.006, OR = 0.34, 95% CI = 0.15–0.73). Each unit increase in LYM level was linked to a 1.143-fold increase in mortality risk (P = 0.008, OR = 2.14, 95% CI = 1.25–3.89). Furthermore, for each additional second of PT, the risk of death increased by 0.31 times (P = 0.002, OR = 1.31, 95% CI = 1.06–1.62).

Table 2 Multivariate Logistic Regression Analysis

Figure 3 Multiple regression forest plot.

Abbreviations: DC, decompressive craniectomy; NPR_std, centralized and standardized values of the neutrophil-to-platelet ratio; LYM, lymphocyte; PT, prothrombin time.

Notes: Only independent predictive factors were included in the figure, while variables without statistical significance in multiple regression were excluded. Age, NPR_std, LYM, and PT were identified as independent risk factors (OR > 1), whereas DC and the responsible artery served as independent protective factors (OR < 1).

In further regression and stratified analysis, Table 3 indicates that within the investigated patient population, older age (≥56 years) and higher NPR_std (≥0.075) are independent risk factors for the occurrence of the primary outcome. Conversely, the presence of DC (YES) and anterior circulation (AC) as the responsible artery are independent protective factors against the occurrence of the primary outcome. Lymphocyte count (LYM) and prothrombin time (PT) were not found to be significantly associated with the outcome in this study.

Table 3 Independent Logistic Regression and Stratified Analyses

There were statistically significant differences in NPR_std, Age, DC, and the responsible artery between the survival group and the death group. There is no statistical difference between LYM and PT (Figure 4).

Figure 4 Differential analysis between survival group and death group.

Abbreviations: DC, decompressive craniectomy; NPR_std, centralized and standardized values of the neutrophil-to-platelet ratio; LYM, lymphocyte; PT, prothrombin time; PC, posterior circulation; AC, anterior circulation.

Notes: (A–F) show the distribution of NPR_std, Age, LYM, PT, responsible artery, and DC in the alive versus dead groups, respectively. There were statistically significant differences in NPR_std, Age, DC, and the responsible artery between the survival group and the death group, Asterisks denote statistical significance: **** for P < 0.0001, *** for P < 0.001, and ns for non-significant (P > 0.05).

ROC curve analysis (Figure 5) showed that NPR_std exhibited the best predictive performance with an AUC of 0.794 (P<0.001), followed by Age with an AUC of 0.759 (P<0.001). Resp. Art. and DC also demonstrated statistically significant predictive value, with AUCs of 0.643 (P<0.001) and 0.616 (P=0.003), respectively. In contrast, LYM (AUC=0.543, P=0.275) and PT (AUC=0.541, P=0.294) showed poor discriminative ability, as their AUCs were close to 0.5 and the P-values were greater than 0.05, indicating no significant predictive efficacy for the target outcome.

Figure 5 Roc analysis.

Abbreviations: DC, decompressive craniectomy; NPR_std, centralized and standardized values of the neutrophil-to-platelet ratio; LYM, lymphocyte; PT, prothrombin time; PC, posterior circulation; AC, anterior circulation; Resp. Art., responsible artery.

The graph illustrates the association between standardized NPR (NPR std) and an outcome variable (Figure 6). Statistical analysis reveals a highly significant overall association (P < 0.001), indicating a notable correlation between changes in NPR std and the outcome variable, while the non-linear test yields a P-value of 0.852 (P > 0.05), suggesting no significant evidence of a non-linear relationship.

Figure 6 Analysis of RCS for NPR_std.

Table 4 shows the data of the randomly split training set and test set, indicating that the two groups of data are basically balanced and comparable.

Table 4 Baseline Tables for Training and Testing Sets

This study identified six independent predictive factors: NPR_std, Age, DC, Responsible Artery, LYM, and PT. A numerical value was assigned to each factor, and the total score was derived by summing these individual scores. This composite score served as an indicator for predicting early mortality risk in LAO-AIS patients (Figure 7).

Figure 7 A nomogram for predicting early death in patients with Lao – AIS.

Abbreviations: DC, decompressive craniectomy; NPR_std, centralized and standardized values of the neutrophil-to-platelet ratio; LYM, lymphocyte; PT, prothrombin time; PC, Posterior circulation; AC, Anterior circulation.

Notes: The sixth sequentially sorted patient in the input dataset was selected as an illustrative case, demonstrating an early mortality probability of 0.405.

In the predictive model developed in this study, the AUC for the training set in logistic regression was 0.926 (95% CI = 0.885–0.967), while the AUC for the validation set was 0.853 (95% CI = 0.753–0.952), demonstrating strong predictive performance (Figure 8A). To ensure the reliability of the calibration curve analysis, 500 internal repeated samplings were conducted for both the training and validation sets. The results exhibited a high degree of concordance between the predicted and observed values in the nomogram (Figure 8BI and Bii). Additionally, Figure 8Ci, 8Cii, 8Di, and 8Dii illustrate that clinical interventions guided by this prediction model provided substantial clinical benefits.

Figure 8 ROC curve, calibration curve and DCA plot.

Notes: The AUC of the training and test sets in (A), (Bi and Bii) are the calibration curves of the internally repeated sampling training and test sets, respectively. (Ci and Di) are the Decision Curve Analysis of the training set, and (Cii and Dii) are the Decision Curve Analysis of the test set.

To identify the optimal predictive model, this study employed and rigorously evaluated four distinct machine learning algorithms: Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Naïve Bayes (NB). The models were assessed using a comprehensive set of metrics, each selected for its specific insight into model behavior. Accuracy provides an overall view of correctness, while Sensitivity (Recall) and Specificity are critical in clinical settings for quantifying the model’s ability to identify true positives and true negatives, respectively. The F1-Score, as the harmonic mean of Precision and Recall, offers a single metric to balance the concern of missing true cases (low Recall) against the cost of false alarms (low Precision). Finally, the Brier Score evaluates the calibration of predicted probabilities, where a lower score indicates more reliable and confident predictions.

Based on the results (Table 5 and Figure 9), a detailed comparison reveals distinct strengths and weaknesses. On the training set, XGBoost achieved near-perfect sensitivity (0.95) and the highest accuracy (0.89), demonstrating its powerful capacity for pattern recognition. However, its performance on the validation set provides the true test of generalizability. Here, while the tree-based DT model exhibited the highest F1-score (0.66) and competitive accuracy (0.81), its significant drop in sensitivity from train to test (0.69 to 0.72) compared to XGBoost’s more pronounced drop (0.95 to 0.66) suggests that DT may be less complex and thus more robust, whereas XGBoost shows signs of potential overfitting to the training data. This is further supported by the Brier Score; although all models had identical test set Brier Scores (0.11), XGBoost’s notably lower training Brier Score (0.06) confirms its superior probability calibration on seen data.

Table 5 Machine Learning Feature Table

Figure 9 Machine learning feature maps.

Abbreviations: LR, logistic regression; DT, decision tree; XGBoost, extreme gradient boosting; SVM, support vector machine; NB, naive bayes; Dfclass, different classes.

Notes: (Ai and Bi) are the machine learning results of the training set, while (Aii and Bii) are the machine learning results of the test set.

Therefore, the selection of the XGBoost model as the final predictor is a nuanced decision. It is justified not merely by a single metric but by a holistic view of its high discriminative ability (as evidenced by the leading validation AUC of 0.86), its excellent calibration, and its best-in-class performance on the training data which suggests high potential with a larger dataset. While it may be more complex, its overall performance profile makes it the most suitable model for the predictive task at hand.

The SHAP analysis of the model developed using XGBoost reveals that NPR_std holds the highest importance in this model (Figure 10A). In the SHAP value plots of each variable, NPR_std, Age, and LYM exhibit an approximately positive correlation, while PT takes the form of a downward-opening curve (Figure 10B). We also present the SHAP value plot of one of the patients (Figure 10C).

Figure 10 SHAP Explanation Diagram.

Abbreviations: DC, decompressive craniectomy; NPR_std, centralized and standardized values of neutrophil-to-platelet ratio; LYM, lymphocyte; PT, prothrombin time.

Notes: (A) represents the SHAP analysis of all variables, (B) depicts the distribution of SHAP values for individual variables, and (C) shows the SHAP values for a particular case.

Discussion

This study primarily investigates the predictive factors for mortality within 90 days following mechanical thrombectomy in patients with LAO-AIS and develops a nomogram centered on the inflammatory marker NPR. The significance of NPR is further validated through four machine learning approaches.

Despite growing focus on post-MT prognosis in LAO-AIS, critical gaps persist in existing predictive approaches. Most rely on indirect inflammatory indices (eg, NLR, PLR)22 that fail to directly capture neutrophil-platelet thrombotic-inflammatory crosstalk—key to MT-induced reperfusion injury.23 Unstandardized cell ratios and overreliance on simple models (eg, logistic regression alone) further limit stability and discriminative power, while few tools are tailored to MT’s unique pathophysiology (eg, recanalization-triggered neuroinflammation), rather than unselected AIS.24

To address these, our study offers targeted innovations: (1) NPR_std (centralized/standardized NPR) eliminates scaling biases to reflect neutrophil-platelet interactions accurately; (2) Boruta feature selection paired with XGBoost (outperforming traditional models) enhances predictive robustness; (3) a concise nomogram integrates NPR_std with clinical factors for bedside utility. This fills the unmet need for MT-specific, inflammation-centered risk tools, aiding timely interventions to reduce early mortality.

Globally, stroke incidence is rising, with early 3-month mortality reaching 15–30% due to individual and healthcare variations.25,26 Stroke triggers multi-system pathophysiological changes, including neuroinflammation where neutrophils and platelets play pivotal roles.27 In ischemic stroke, neutrophils rapidly infiltrate brain tissue via chemokine signaling (eg, CXCL1/CXCR2),28 releasing ROS, proteases (MMP-9), and cytokines (IL-1β, TNF-α) that exacerbate neuronal and vascular injury.29 Neutrophil-endothelial adhesion (CD11b/CD18-ICAM-1) further impairs microcirculation.30 Platelet activation amplifies this process: neutrophil-platelet aggregates form through receptors (eg, PSGL-1/P-selectin, Mac-1/GPIIb/IIIa),31 driving (1) pro-thrombotic NETs release,32 (2) inflammatory amplification via RANTES/HMGB1,33 and (3) thrombolytic resistance through PAI-1.34 Targeting these interactions may mitigate secondary injury in LAO-AIS.

Research indicates that mechanical thrombectomy in stroke patients can further reactivate the inflammatory response, with neutrophil infiltration occurring as early as 180 minutes after ischemia and 30 minutes post-reperfusion.35 Thus, neutrophils serve as the initiating factor in the inflammatory cascade, both following cerebral ischemia and after vessel recanalization via thrombectomy. The risk of reocclusion after thrombectomy is closely linked to platelet activity. Studies have demonstrated that thrombi retrieved during thrombectomy, particularly those rich in platelets and fibrin (such as arterial sources), exhibit poor responsiveness to both thrombolysis and mechanical thrombectomy, potentially increasing the likelihood of reocclusion.36–38, Preoperative intravenous thrombolysis with recombinant tissue plasminogen activator (rtPA) may alter thrombus composition by degrading fibrin, thereby reducing platelet consumption after thrombectomy and potentially mitigating the risk of reocclusion.37

Current research suggests that elevated levels of both neutrophils and platelets in stroke patients may contribute to poor prognosis. However, no well-established indicator specifically integrates these two parameters to assess early mortality after thrombectomy. Most existing studies rely on the platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) to predict adverse cardiovascular and cerebrovascular events,39–41 while few directly link neutrophils and platelets. This may be due to the significant differences in their values and units, which could introduce errors in multiple regression and machine learning analyses. To accurately investigate the combined impact of neutrophils and platelets on early mortality in LAO-AIS, this study first applied centering and standardization to these values. While this approach may introduce some challenges in interpretation, it ensures the stability and balance of the model. The Boruta feature selection method was then employed to identify the most influential variables, effectively reducing the computational burden during model training and prediction while maintaining or even enhancing model performance.42 Boruta has been widely applied in predicting mortality risks across various diseases, including cardiovascular diseases, acute kidney injury, and sepsis-related acute kidney injury.43–45 Multivariate regression analysis in this study identified NPR_std, Age, DC, Responsible arterie, LYM, and PT as independent predictors (P < 0.05), and a nomogram was developed accordingly. Prior studies have consistently highlighted age as a key factor influencing early mortality after thrombectomy in LAO-AIS patients, with significantly increased mortality rates observed in those over 80 years old.46 Additionally, posterior circulation artery occlusion and prolonged PT have been identified as significant risk factors for mortality,47–49 aligning with the findings of this study. Interestingly, SHAP analysis of PT revealed a biphasic effect, with both excessively short and prolonged PT values markedly influencing SHAP scores, suggesting a complex role of PT in stroke prognosis. Furthermore, timely DC has been shown to preserve brain function and significantly reduce mortality in patients with extensive cerebral infarction and severe cerebral edema.50 Notably, this study diverged from previous research by identifying elevated LYM as a predictor of poor prognosis. Most studies have examined the prognostic implications of NLR or other blood cell ratios (eg, PLR, LMR) in acute ischemic stroke, often linking elevated NLR with adverse outcomes and suggesting that lymphopenia contributes to poor prognosis.51–53 One study also reported that elevated total WBC counts, including lymphocytes, at admission were associated with increased three-month mortality following thrombolysis or thrombectomy but did not differentiate among lymphocyte subtypes.54 These findings indicate that further preclinical research is needed to elucidate the precise mechanisms underlying the impact of lymphocyte levels on stroke prognosis.

It is noteworthy that although LYM and prothrombin time (PT) were identified as independent predictors in the multivariate regression model (Table 2), their individual discriminative capacity in univariate ROC analysis was limited (Figure 5). This apparent discrepancy is a recognized statistical phenomenon, wherein powerful predictors like NPR_std and Age may obscure the more subtle, yet independent, contributions of other variables in a univariate setting. Within the multivariate context, LYM and PT provide significant incremental value by capturing unique pathophysiological pathways—pertaining to immunomodulation and coagulation, respectively—that are not redundant with other model features. This justifies their inclusion in the final nomogram, which is designed as an integrated tool where the combined effect of all variables yields a robust prediction, as evidenced by the high overall AUC, rather than relying on any single parameter.

This study employed Boruta feature selection and applied centering and standardization to NPR, enhancing the stability and reliability of the model. The developed nomogram provides a practical tool for estimating early mortality risk in LAO-AIS patients following thrombectomy. Among various machine learning models evaluated, XGBoost was identified as the most suitable method based on multiple performance indicators. Furthermore, SHAP analysis highlighted the significance of NPR in the model, offering a clearer visualization of how different variable distributions influence SHAP values. This allows for a more intuitive understanding of the importance of each predictor at varying levels. Unlike previous studies that indirectly assess the roles of neutrophils and platelets through composite ratios (eg, NLR, PLR) or focus solely on LAO-AIS prognosis without considering thrombectomy, this study directly incorporates NPR as a predictive factor. By doing so, it provides a more clinically relevant approach, facilitating targeted interventions for patients post-thrombectomy—an aspect not addressed in prior research.

Beyond stroke-specific contexts, elevated neutrophil-to-platelet ratio (NPR) demonstrates significant clinical relevance across diverse conditions. In sepsis patients, high NPR correlates with adverse prognosis (eg, increased mortality), and multivariate analysis confirms its independent predictive value.55 For burn patients, NPR combined with other inflammatory indices (eg, NLR, PLR) predicts in-hospital mortality, with the high-NPR group exhibiting significantly reduced survival rates.56 Following cardiovascular surgery, NPR associates with perioperative complications (eg, infections, organ failure), where elevated NPR on the first postoperative day indicates poor outcomes.57 In AIS patients, NPR links to hemorrhage risk after intravenous thrombolysis, and the high-NPR group shows higher adverse event incidence.58 Furthermore, in testicular germ cell tumors (GCT), NPR correlates with tumor staging and clinical characteristics, with high-NPR patients demonstrating increased propensity for disease progression or metastasis.59 These findings collectively underscore NPR’s role as a biomarker of inflammatory-thrombotic dysregulation.

To bridge our findings to bedside application, we propose a concrete clinical workflow for the proposed nomogram. Upon admission of an LAO-AIS patient eligible for MT, routine baseline data is collected. This includes a complete blood count (for calculating NPR_std and obtaining LYM) and a coagulation profile (for PT), alongside standard assessments such as neuroimaging (eg, CTA/DSA for determining the Resp. Art). NIHSS scoring, documentation of Age, and evaluation for potential DC. Risk Stratification: Prior to or immediately following MT, the clinician inputs these six parameters (NPR_std, Age, DC, Resp. Art., LYM, PT) into the nomogram to generate a quantifiable probability of 90-day mortality. This output stratifies patients into distinct risk categories (eg, low, intermediate, high). Personalized Clinical Decision-Making: This risk score is then integrated into clinical pathways to personalize post-procedural care: High-Risk Patients (eg, a hypothetical 75-year-old with basilar artery occlusion and elevated NPR_std): Would be flagged for intensified monitoring in a stroke unit or neuro-ICU. This facilitates preemptive management, such as stricter hemodynamic control to mitigate reperfusion injury, proactive screening and treatment of infections given the pro-inflammatory state, and early discussion with families regarding prognosis and goals of care. Low-Risk Patients: This prediction can aid in avoiding unnecessary overtreatment and rationalize resource allocation, allowing clinical focus to be directed toward higher-risk individuals. This practical framework demonstrates how our model directly addresses the stated research goal: to provide a clinically actionable tool that leverages NPR and other readily available parameters to guide post-thrombectomy management, thereby enhancing personalized medicine in LAO-AIS.

Notably, the socioeconomic context of Huizhou, Guangdong—where our cohort was recruited—offers valuable insights for researchers in Asian low- and middle-income countries (LMICs): as a transitional economy with a 2023 per capita GDP of ~$13,203 (surpassing typical LMIC thresholds) yet persistent urban-rural income gaps (2024 Q1-Q3: $5990 urban vs $3480 rural), it mirrors the economic duality of many upper-middle-income Asian regions. This context shaped our cohort’s key traits: high mechanical thrombectomy (MT) accessibility (evidenced by our 37.5% MT rate) stems from Huizhou’s robust stroke care infrastructure (eg, regional stroke centers, >95% health insurance coverage) and policy-driven investments (eg, “Sanming healthcare reform” funds), providing a replicable model for LMICs scaling MT. Conversely, our cohort’s 41.2% prevalence of uncontrolled hypertension reflects lingering gaps—such as low health literacy (34.22%)—that echo chronic disease management challenges in resource-constrained LMICs. Thus, our findings balance two critical takeaways for Asian LMICs: the feasibility of high-quality MT via targeted healthcare investment, and the need to address preventive care gaps to improve stroke outcomes.

Limitations

This study has several limitations that warrant emphasis. Its single-center, retrospective design inherently constrains the generalizability of our findings. The model was derived from a specific patient cohort treated under localized protocols, reflecting unique demographic and socioeconomic characteristics. Consequently, the predictive performance and clinical applicability of the nomogram may vary when applied in other geographic or healthcare settings. External validation through multi-center, prospective studies remains essential to confirm its broad utility.

Furthermore, several methodological constraints should be acknowledged. Key procedural variables—such as recanalization status and number of passes—as well as post-operative medications were not systematically analyzed, yet these may independently influence mortality risk. Although blood sampling within 24 hours captured acute inflammatory responses, the predictive stability of NPR could be affected by pre-analytical variations and the timing of measurement relative to thrombectomy. Future studies incorporating standardized pre- and post-procedural biomarker assessments are needed to enhance clinical translatability.

Conclusion

In conclusion, this study developed a predictive model for early mortality risk in LAO-AIS patients following thrombectomy. While the model incorporates six variables, its predictive capability is strongly driven by key factors, most notably the standardized Neutrophil-to-Platelet Ratio (NPR_std) and patient age, with SHAP analysis visually confirming the pivotal role of NPR_std in the decision-making process.

Abbreviation

NPR, neutrophil-to-platelet ratio; MT, mechanical thrombectomy; LAO-AIS, large artery occlusive acute ischemic stroke; DT, decision tree; XGBoost, extreme gradient boosting; SVM, support vector machine; NB, naive bayes; NPR_std, centralized and standardized neutrophil-to-platelet ratio; DC, decompressive craniectomy; LYM, lymphocyte count; PT, prothrombin time; C-index, concordance index; DCA, clinical decision curve analysis; AUC, area under the ROC curve; F1, F1-score; SHAP, shapley additive explanations; DALYs, disability-adjusted life years; LAO, large artery occlusion; AIS, acute ischemic stroke; LMICs, low- and middle-income countries; CTA, CT angiography; DSA, digital subtraction angiography; ASPECTS, alberta stroke program early CT score; MRI, magnetic resonance imaging; DAWN, deferring antiplatelet therapy until symptomatic intracranial stenosis; DEFUSE-3, diffusion and perfusion imaging evaluation for understanding stroke evolution with intravenous alteplase; BAOCHE trial, basilar artery Occlusion chinese endovascular trial; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; AIC, akaike information criterion; BIC, bayesian information criterion; ROC, receiver operating characteristic; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell; NE, neutrophil granulocyte; PLT, platelet; MO, monocyte; APTT, activated partial thromboplastin time; D-D, d-dimer; FIB, fibrinogen; ALB, albumin; K, potassium; Ca, calcium; Cl, chlorine; PC, posterior circulation; AC, anterior circulation; Resp. Art., responsible artery; LR, logistic regression; Dfclass, different classes; rtPA, recombinant tissue plasminogen activator; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; GCT, germ cell tumors; LMICs, low- and middle-income countries.

Data Sharing Statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Ethical Statement

The study involving human participants was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Guilin Medical University (Approval No. 2024YJSLL-18). The committee waived the requirement for written informed consent due to the retrospective nature of the study and in accordance with national legislation and institutional policies.

Acknowledgments

This study adhered to the RECORD (Reporting of studies Conducted using Observational Routinely-collected Data) guidelines.

Author Contributions

Shaohuai Xia: Writing – original draft; Formal analysis; Data curation; Methodology. Junhong Hu: Writing – original draft; Data curation; Formal analysis. Yingye Liao: Writing – original draft; Investigation (Data collection). Jing Wu: Writing – original draft; Investigation (Data collection). Xinrong Zhong: Writing – original draft, Investigation (Data collection). Xiaoguang Fan: Writing – original draft; Formal analysis; Investigation. Jinping Li: Writing – review and editing, Investigation. Guifeng Liang: Writing – review and editing, Investigation. Li Chen: Conceptualization; Supervision; Writing – review and editing. Xuewei Xia: Conceptualization; Supervision; Writing – review and editing. All authors have read and approved the final manuscript. All authors have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was supported by the Major Project (2023Y9316) of the Fujian Provincial Department of Science and Technology.

Disclosure

The authors declare no conflict of interest.

References

1. Feigin VL, Stark BA, Johnson CO. Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet Neurol. 2021;20(10):795–820. doi:10.1016/S1474-4422(21)00252-0

2. Malhotra K, Gornbein J, Saver JL. Ischemic strokes due to large-vessel occlusions contribute disproportionately to stroke-related dependence and death: a review. Front Neurol. 2017;8:651. doi:10.3389/fneur.2017.00651

3. Berkhemer OA, Fransen PS, Beumer D, et al. A randomized trial of intraarterial treatment for acute ischemic stroke. New Engl J Med. 2015;372(1):11–20. doi:10.1056/NEJMoa1411587

4. Aslan A, Abuzahra S, Adeeb N, et al. The feasibility of mechanical thrombectomy versus medical management for acute stroke with a large ischemic territory. J Neurointervent Surg. 2025;17(2):223–228. doi:10.1136/jnis-2023-021368

5. Costalat V, Lapergue B, Albucher JF, et al. Evaluation of acute mechanical revascularization in large stroke (ASPECTS ⩽5) and large vessel occlusion within 7 h of last-seen-well: the LASTE multicenter, randomized, clinical trial protocol. Int J Stroke. 2024;19(1):114–119. doi:10.1177/17474930231191033

6. Kühn AL, Puri AS, Salim HA, et al. Multicenter evaluation of mechanical thrombectomy for distal medium vessel occlusions with national institute of health stroke scale scores ≥ 6 and ≤ 6. J Neurol. 2024;271(9):5853–5863. doi:10.1007/s00415-024-12537-4

7. Vollset SE, Ababneh HS, Abate YH, et al. Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the global burden of disease study 2021. Lancet. 2024;403(10440):2204–2256.

8. Bauza C, Yeatts SD, Borg K, et al. Determining the joint effect of obesity and diabetes on functional disability at 3-months and on all-cause mortality at 1-year following an ischemic stroke. BMC Endocr Disord. 2018;18(1):40. doi:10.1186/s12902-018-0255-1

9. He Y, Wang J, Ying C, et al. The interplay between ferroptosis and inflammation: therapeutic implications for cerebral ischemia-reperfusion. Front Immunol. 2024;15:1482386.

10. Yasufuku Y, GBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: a systematic analysis for the global burden of disease study 2021. Lancet Neurol. 2024;23(10):973–1003. doi:10.1016/S1474-4422(24)00369-7

11. Jadhav AP, Desai SM, Jovin TG. Indications for mechanical thrombectomy for acute ischemic stroke: current guidelines and beyond. Neurology. 2021;97(20 Suppl 2):S126–s136. doi:10.1212/WNL.0000000000012801

12. Bellut M, Bieber M, Kraft P, Weber ANR, Stoll G, Schuhmann MK. Delayed NLRP3 inflammasome inhibition ameliorates subacute stroke progression in mice. J Neuroinflamm. 2023;20(1):4. doi:10.1186/s12974-022-02674-w

13. Silvestre-Roig C, Braster Q, Ortega-Gomez A, Soehnlein O. Neutrophils as regulators of cardiovascular inflammation. Nat Rev Cardiol. 2020;17(6):327–340. doi:10.1038/s41569-019-0326-7

14. Caillon A, Trimaille A, Favre J, Jesel L, Morel O, Kauffenstein G. Role of neutrophils, platelets, and extracellular vesicles and their interactions in COVID-19-associated thrombopathy. J Thrombosis Haemostasis. 2022;20(1):17–31. doi:10.1111/jth.15566

15. Chamorro A, Hallenbeck J. The harms and benefits of inflammatory and immune responses in vascular disease. Stroke. 2006;37(2):291–293. doi:10.1161/01.STR.0000200561.69611.f8

16. Chamorro Á, Dirnagl U, Urra X, Planas AM. Neuroprotection in acute stroke: targeting excitotoxicity, oxidative and nitrosative stress, and inflammation. Lancet Neurol. 2016;15(8):869–881. doi:10.1016/S1474-4422(16)00114-9

17. Kelly PJ, Lemmens R, Tsivgoulis G. Inflammation and stroke risk: a new target for prevention. Stroke. 2021;52(8):2697–2706. doi:10.1161/STROKEAHA.121.034388

18. Uchida K, Shindo S, Yoshimura S, et al. Association between alberta stroke program early computed tomography score and efficacy and safety outcomes with endovascular therapy in patients with stroke from large-vessel occlusion: a secondary analysis of the recovery by endovascular salvage for cerebral ultra-acute embolism-japan large ischemic core trial (RESCUE-Japan LIMIT). JAMA Neurol. 2022;79(12):1260–1266. doi:10.1001/jamaneurol.2022.3285

19. Jovin TG, Li C, Wu L, et al. Trial of thrombectomy 6 to 24 hours after stroke due to basilar-artery occlusion. New Engl J Med. 2022;387(15):1373–1384. doi:10.1056/NEJMoa2207576

20. Noghrehchi F, Stoklosa J, Penev S, Warton DI. Selecting the model for multiple imputation of missing data: just use an IC! Stat Med. 2021;40(10):2467–2497. doi:10.1002/sim.8915

21. Ye Z, An S, Gao Y, et al. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models. Eur J Med Res. 2023;28(1):33. doi:10.1186/s40001-023-00995-x

22. Gong P, Liu Y, Gong Y, et al. The association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, and lymphocyte to monocyte ratio with post-thrombolysis early neurological outcomes in patients with acute ischemic stroke. J Neuroinflamm. 2021;18(1):51. doi:10.1186/s12974-021-02090-6

23. Chen S, Wu A, Shen X, Kong J, Huang Y. Disrupting the dangerous alliance: dual anti-inflammatory and anticoagulant strategy targets platelet-neutrophil crosstalk in sepsis. J Controlled Rel. 2025;379:814–831. doi:10.1016/j.jconrel.2025.01.053

24. Li R, Yin Y, Cai X, et al. Effect of routine inflammatory markers on clinical outcomes in acute basilar artery occlusion after endovascular thrombectomy: results from ATTENTION registry. Int J Stroke. 2023;18(8):976–985. doi:10.1177/17474930231176948

25. Darban YM, Askari H, Ghasemi-Kasman M, et al. The role of induced pluripotent stem cells in the treatment of stroke. Curr Neuropharmacol. 2024;22(14):2368–2383. doi:10.2174/1570159X22666240603084558

26. Almallouhi E, Zandpazandi S, Anadani M, et al. Outcomes of mechanical thrombectomy in stroke patients with extreme large infarction core. J Neurointervent Surg. 2024;16(12):1268–1274. doi:10.1136/jnis-2023-021046

27. Huang S, Liu L, Tang X, et al. Research progress on the role of hormones in ischemic stroke. Front Immunol. 2022;13:1062977. doi:10.3389/fimmu.2022.1062977

28. Li Q, Ye J, Li Z, et al. The role of neutrophils in tPA thrombolysis after stroke: a malicious troublemaker. Front Immunol. 2024;15:1477669. doi:10.3389/fimmu.2024.1477669

29. Han Z, Li L, Zhao H, et al. MicroRNA-193a-5p rescues ischemic cerebral injury by restoring n2-like neutrophil subsets. Transl Stroke Res. 2023;14(4):589–607. doi:10.1007/s12975-022-01071-y

30. Wang H, Zhang S, Xie L, Zhong Z, Yan F. Neuroinflammation and peripheral immunity: focus on ischemic stroke. Int Immunopharmacol. 2023;120:110332. doi:10.1016/j.intimp.2023.110332

31. Stoll G, Schuhmann MK, Nieswandt B, Kollikowski AM, Pham M. An intravascular perspective on hyper-acute neutrophil, T-cell and platelet responses: similarities between human and experimental stroke. J Cerebral Blood Flow Metab. 2022;42(9):1561–1567. doi:10.1177/0271678X221105764

32. Fang H, Bo Y, Hao Z, Mang G, Jin J, Wang H. A promising frontier: targeting NETs for stroke treatment breakthroughs. Cell commun signaling. 2024;22(1):238. doi:10.1186/s12964-024-01563-4

33. De Wilde M, Desender L, Tersteeg C, Vanhoorelbeke K, De Meyer SF. Spatiotemporal profile of neutrophil extracellular trap formation in a mouse model of ischemic stroke. Res Pract thrombosis Haemostasis. 2023;7(1):100028. doi:10.1016/j.rpth.2022.100028

34. Zhao Z, Pan Z, Zhang S, et al. Neutrophil extracellular traps: a novel target for the treatment of stroke. Pharmacol Ther. 2023;241:108328.

35. González-Flecha B, Cutrin JC, Boveris A. Time course and mechanism of oxidative stress and tissue damage in rat liver subjected to in vivo ischemia-reperfusion. J Clin Invest. 1993;91(2):456–464. doi:10.1172/JCI116223

36. Berndt M, Friedrich B, Maegerlein C, et al. Thrombus permeability in admission computed tomographic imaging indicates stroke pathogenesis based on thrombus histology. Stroke. 2018;49(11):2674–2682. doi:10.1161/STROKEAHA.118.021873

37. Krajíčková D, Krajina A, Šteiner I, et al. Fibrin clot architecture in acute ischemic stroke treated with mechanical thrombectomy with stent-retrievers – cohort study. Circul J. 2018;82(3):866–873. doi:10.1253/circj.CJ-17-0375

38. Weisel JW, Litvinov RI. Exploring the thrombus niche: lessons learned and potential therapeutic opportunities. Blood. 2025;146(12):1389–1399. doi:10.1182/blood.2024025319

39. Hu Y, Huang K, Ji Z, et al. High neutrophil-to-lymphocyte ratio is associated with poor clinical outcome in patients with critically ill stroke. Minerva Anestesiolog. 2020;86(9):939–947. doi:10.23736/S0375-9393.20.14310-4

40. Wang P, Huang J, Xu L, Hu R. The association between the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio and delirium in ischemic stroke patients. Front Med. 2024;11:1456742. doi:10.3389/fmed.2024.1456742

41. Zhang Z, Zhang W, Liu Z, et al. Association between systemic inflammation markers and cardiovascular mortality in adults with metabolic dysfunction-associated steatotic liver disease. Nutr Metab Cardiovasc Dis. 2024;35:103781. doi:10.1016/j.numecd.2024.10.019

42. Panda P, Bisoy SK, Panigrahi A, et al. BIMSSA: enhancing cancer prediction with salp swarm optimization and ensemble machine learning approaches. Front Genetics. 2024;15:1491602. doi:10.3389/fgene.2024.1491602

43. Liu M, Fan Z, Gao Y, et al. A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury. Sci Rep. 2024;14(1):16794. doi:10.1038/s41598-024-63793-3

44. Wu Z, Hilowle AM, Zhou Y, Zhao C, Yang S. Delving into biomarkers and predictive modeling for CVD mortality: a 20-year cohort study. Sci Rep. 2025;15(1):4134. doi:10.1038/s41598-025-88790-y

45. Yang J, Peng H, Luo Y, Zhu T, Xie L. Explainable ensemble machine learning model for prediction of 28-day mortality risk in patients with sepsis-associated acute kidney injury. Front Med. 2023;10:1165129. doi:10.3389/fmed.2023.1165129

46. Ippen FM, Schregel K, Ungerer M, Feisst M, Ringleb PA, Gumbinger CK. Outcomes in elderly patients undergoing endovascular thrombectomy in association with premorbid Rankin Scale scores. Front Neurol. 2024;15:1418415. doi:10.3389/fneur.2024.1418415

47. Carfora A, Holthaus B, Yacoub S, et al. Von Willebrand factor targeted thrombolysis in canine basilar artery occlusion. Front Neurol. 2024;15:1436291. doi:10.3389/fneur.2024.1436291

48. Huang J, Wang M, Li F, et al. Clinical predictors for early mortality of patients with acute basilar artery occlusion. Cerebrovascular Dis. 2023;52(2):202–209. doi:10.1159/000526124

49. Nogueira RG, Jovin TG, Liu X, et al. Endovascular therapy for acute vertebrobasilar occlusion (VERITAS): a systematic review and individual patient data meta-analysis. Lancet. 2025;405(10472):61–69. doi:10.1016/S0140-6736(24)01820-8

50. Thorén M, Escudero-Martínez I, Andersson T, et al. Reperfusion by endovascular thrombectomy and early cerebral edema in anterior circulation stroke: results from the SITS-International Stroke Thrombectomy Registry. Int J Stroke. 2023;18(10):1193–1201. doi:10.1177/17474930231180451

51. Gao W, Annadurdyyev A, Yu L, et al. Day 3 neutrophil-to-lymphocyte ratio and its derived indices predict 90-day poor outcomes following mechanical thrombectomy in acute ischemic stroke patients. Front Neurol. 2024;15:1496628. doi:10.3389/fneur.2024.1496628

52. Rossi R, Douglas A, Gil SM, et al. S100b in acute ischemic stroke clots is a biomarker for post-thrombectomy intracranial hemorrhages. Front Neurol. 2022;13:1067215. doi:10.3389/fneur.2022.1067215

53. Shan Y, Zhang R, Lu J, et al. Neutrophil to lymphocyte ratio and five-year mortality in patients with acute ischemic stroke. Heliyon. 2024;10(17):e36827. doi:10.1016/j.heliyon.2024.e36827

54. Altersberger VL, Enz LS, Sibolt G, et al. Thrombolysis in stroke patients with elevated inflammatory markers. J Neurol. 2022;269(10):5405–5419. doi:10.1007/s00415-022-11173-0

55. Zhu J, Zhang C, Deng Z, Ouyang L. Association between neutrophil-platelet ratio and 28-day mortality in patients with sepsis: a retrospective analysis based on MIMIC-IV database. BMC Infect Dis. 2025;25(1):685. doi:10.1186/s12879-025-11064-5

56. Li F, He Q, Peng H, et al. The systemic inflammation indexes after admission predict in-hospital mortality in patients with extensive burns. Burns. 2024;50(4):980–990. doi:10.1016/j.burns.2024.01.020

57. Zhou W, Wang H, Li C, et al. Alterations in novel inflammatory biomarkers during perioperative cardiovascular surgeries involving cardiopulmonary bypass: a retrospective propensity score matching study. Front Cardiovasc Med. 2024;11:1433011. doi:10.3389/fcvm.2024.1433011

58. Gao B, Pan W, Hu X, et al. Neutrophil-related ratios predict the 90-day outcome in acute ischemic stroke patients after intravenous thrombolysis. Front Physiol. 2021;12:670323. doi:10.3389/fphys.2021.670323

59. Bumbasirevic U, Bojanic N, Simic T, et al. Interplay between comprehensive inflammation indices and redox biomarkers in testicular germ-cell tumors. J Personal Med. 2022;12(5). doi:10.3390/jpm12050833.