ArticleFrontiers in neurology
Nomogram-based prediction of hemorrhagic transformation risk integrating platelet-to-white blood cell ratio in patients with acute ischemic stroke after intravenous thrombolysis.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Hemorrhagic transformation (HT) is a significant complication in acute ischemic stroke (AIS) patients after intravenous thrombolysis (IVT), frequently leading to unfavorable clinical prognoses. However, the association between the platelet-to-white blood cell ratio (PWR) and the risk of HT, along with its severity subtypes, remains largely unexplored. This study investigates the independent association between low PWR and the increased incidence and severity of HT following IVT. Method: This study retrospectively included AIS patients who received IVT treatment from our hospital's stroke database. HT was identified through cranial imaging (CT/MRI) conducted within 24 h post-IVT and categorized into four subtypes based on the European Cooperative Acute Stroke Study (ECASS) criteria. The relationship between PWR and HT was examined using multivariate logistic regression. A restricted cubic spline (RCS) analysis was applied to detect the nonlinear relationship of PWR and HT. Based on variables identified via multivariate logistic regression, a nomogram-based prediction model was developed, and its performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results: A total of 790 AIS participants were included in this study. Patients who developed HT exhibited significantly lower PWR levels compared to those without HT. The PWR levels were divided into four quartiles: Q1 (<22.0), Q2 (22.0-28.3), Q3 (28.3-36.2), and Q4 (>36.2), respectively. Multivariable logistic regression demonstrated that patients in the highest PWR quartile (Q4) had a 67% reduced risk of HT (odds ratio [OR]: 0.33; 95% confidence interval [CI]: 0.18-0.61). By integrating potential risk factors, the nomogram achieved an AUC of 0.767 (95% CI: 0.716-0.818). Conclusion: Lower PWR independently correlates with heightened risk and severity of HT after IVT in patients with AIS. The integration of PWR into a nomogram model provides a practical tool for stratifying HT risk, potentially guiding individualized treatment strategies.
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