Assessing Stroke Recurrence Risk by Using a Lipoprotein-Associated Phospholipase A2 and Platelet Count-Based Nomogram.

Stroke recurrence remains a critical challenge in clinical neurology, necessitating the identification of reliable predictive markers for better management and treatment strategies. This study investigates the interaction between lipoprotein-associated phospholipase A2 (Lp-PLA2) and platelets as a potential predictor for stroke recurrence, aiming to refine risk assessment and therapeutic approaches. In a retrospective cohort of 580 ischemic stroke patients, we analyzed clinical data with a focus on Lp-PLA2 and platelet levels. By using multivariable logistic regression, we identified independent predictors of stroke recurrence. These predictors were then used to develop a comprehensive nomogram. The study established diabetes mellitus, hypertension, low-density lipoprotein (LDL), Lp-PLA2 levels, and platelet counts as independent predictors of stroke recurrence. Crucially, the interaction parameter Lp-PLA2 * platelet (multiplication of Lp-PLA2 and platelet count) exhibited superior predictive power over each factor considered separately. Our nomogram incorporated diabetes mellitus, cerebral infarction causes, hypertension, LDL, and the Lp-PLA2 * platelet count interaction and demonstrated enhanced accuracy in predicting stroke recurrence compared to traditional risk models. The interaction between Lp-PLA2 and platelets emerged as a significant predictor for stroke recurrence when integrated with traditional risk factors. The developed nomogram offers a novel and practical tool in molecular neurobiology for assessing individual risks, facilitating personalized treatment strategies. This approach underscores the importance of multifactorial assessment in stroke management and opens avenues for targeted interventions to mitigate recurrence risks.