Huimin Wu, Anxing Huang, Keke Jiang, Xiuping Wei, Zhi Ma, Haibo Jiang, Lili Xu, Wenya Lan, Mingyang Du, Hui Cao, Shanshan Chen, Xingjian Lin, Feng Qiu
PLR exhibited the strongest correlation with neurological deficit severity. The combined model may provide a more accurate laboratory‑based tool for risk stratification in YIS patients.
PURPOSE: To investigate the correlation between inflammatory markers and neurological deficit severity in young ischemic stroke (YIS) patients, and to evaluate their predictive value for short‑term prognosis.
METHODS: We retrospectively collected clinical data from 196 YIS patients at Nanjing Brain Hospital between January 2022 and December 2025. Patients were divided into mild stroke (MIS) and moderate‑to‑severe stroke (MSS) groups based on the National Institutes of Health Stroke Scale (NIHSS) scores assessed within 24 hours of admission. Short-term prognosis at 3 months was evaluated using the modified Rankin Scale (mRS), and patients were classified into good and poor outcome groups. Correlation analysis was used to evaluate the relationships between inflammatory markers and NIHSS scores. LASSO regression was conducted to screen variables, followed by Firth's penalized logistic regression to correct for small-sample bias. Receiver operating characteristic curves were generated to evaluate predictive performance.
RESULTS: The systemic inflammatory response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) were markedly elevated in the MSS group and positively correlated with NIHSS scores. SIRI, PLR, and MLR were significantly higher in the poor outcome group. LASSO identified D‑dimer (D-D), low-density lipoprotein cholesterol (LDL‑C), and NLR as independent predictors of poor outcomes. The area under the curve (AUC) for NLR was 0.592, whereas the combined model (D-D + LDL-C + NLR) yielded an AUC of 0.734.
CONCLUSION: PLR exhibited the strongest correlation with neurological deficit severity. The combined model may provide a more accurate laboratory‑based tool for risk stratification in YIS patients.