Comparison of the Predictive Value of Inflammatory Biomarkers for the Risk of Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke

Abstract

Jingyi Li,1,2 Haowen Luo,1 Yongsen Chen,1,2 Bin Wu,1,2 Mengqi Han,1,2 Weijie Jia,1,2 Yifan Wu,1,2 Rui Cheng,1,2 Xiaoman Wang,1,2 Jingyao Ke,1,2 Hongfei Xian,1,2 JianMo Liu,1 Pengfei Yu,1 Jianglong Tu,3 Yingping Yi1 1Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 2School of Public Health, Nanchang University, Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang, People’s Republic of China; 3Department of Neurology, The Second Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of ChinaCorrespondence: Yingping Yi, Department of Medical Big Data Research Centre, The Second Affiliated Hospital of Nanchang University, 1 MinDe Road, Nanchang, 330006, People’s Republic of China, Email [email protected] Jianglong Tu, Department of Neurology, The Second Affiliated Hospital of Nanchang University, 1 MinDe Road, Nanchang, 330006, People’s Republic of China, Email [email protected]: To investigate the predictive value of various inflammatory biomarkers in patients with acute ischemic stroke (AIS) and evaluate the relationship between stroke-associated pneumonia (SAP) and the best predictive index.Patients and Methods: We calculated the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), systemic inflammation response index (SIRI), systemic immune inflammation index (SII), Glasgow prognostic score (GPS), modified Glasgow prognostic score (mGPS), and prognostic index (PI). Variables were selectively included in the logistic regression analysis to explore the associations of NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI with SAP. We assessed the predictive performance of biomarkers by analyzing receiver operating characteristic (ROC) curves. We further used restricted cubic splines (RCS) to investigate the association. Next, we conducted subgroup analyses to investigate whether specific populations were more susceptible to NLR.Results: NLR, PLR, MLR, SIRI, SII, GPS, mGPS, and PI increased significantly in SAP patients, and PNI was significantly decreased. After adjustment for potential confounders, the association of inflammatory biomarkers with SAP persisted. NLR showed the most favorable discriminative performance and was an independent risk factor predicting SAP. The RCS showed an increasing nonlinear trend of SAP risk with increasing NLR. The AUC of the combined indicator of NLR and C-reactive protein (CRP) was significantly higher than those of NLR and CRP alone (DeLong test, P< 0.001). Subgroup analyses suggested good generalizability of the predictive effect.Conclusion: NLR, PLR, MLR, PNI, SIRI, SII, GPS, mGPS, and PI can predict the occurrence of SAP. Among the indices, the NLR was the best predictor of SAP occurrence. It can therefore be used for the early identification of SAP.Keywords: stroke-associated pneumonia, acute ischemic stroke, inflammation, prediction

Keywords

• stroke-associated pneumonia
• acute ischemic stroke
• inflammation
• prediction