Association Between TCBI (Triglycerides, Total Cholesterol, and Body Weight Index) and Stroke-Associated Pneumonia in Acute Ischemic Stroke Patients

Abstract

Yufeng Liu,1,* Yan Chen,2,* Zhongwen Zhi,1,* Ping Wang,1 Mengchao Wang,1 Qian Li,1 Yuqian Wang,1 Liandong Zhao,1 Chun Chen1 1Department of Neurology, The Affiliated Huai’an Hospital of Xuzhou Medical University and The Second People’s Hospital of Huai’an, Huai’an, Jiangsu, 223002, People’s Republic of China; 2Department of Neurological Medicine, Siyang Hospital of Traditional Chinese Medicine, Siyang, Jiangsu, 223700, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chun Chen; Liandong Zhao, Department of Neurology, The Affiliated Huai’an Hospital of Xuzhou Medical University and The Second People’s Hospital of Huai’an, No. 62, South Huaihai Road, Huai’an, Jiangsu, 223002, People’s Republic of China, Email [email protected]; [email protected]: Stroke-associated pneumonia (SAP) usually complicates stroke and is linked to adverse prognoses. Triglycerides, total cholesterol, and body weight index (TCBI) is a new and simple calculated nutrition index. This study seeks to investigate the association between TCBI and SAP incidence, along with its predictive value.Patients and Methods: Nine hundred and sixty-two patients with acute ischemic stroke were divided into SAP group and Non-SAP group. The TCBI was divided into three layers: T1, TCBI 1647.15. Binary Logistic regression analysis was used to determine the relationship between TCBI levels and the incidence of SAP. Furthermore, restricted cubic splines (RCS) analysis was utilized to evaluate the influence of TCBI on the risk of SAP.Results: TCBI in the SAP group was markedly lower compared to that in the Non-SAP group (P < 0.001). The Logistic regression model revealed that, using T3 layer as the reference, T1 layer had the highest risk for SAP prevalence (OR = 2.962, 95% CI: 1.600– 5.485, P = 0.001), with confounding factors being controlled. The RCS model found that TCBI had a linear relationship with SAP (P for nonlinear = 0.490, P for overall = 0.004). Moreover, incorporating TCBI into the A2DS2 (Age, atrial fibrillation, dysphagia, sex, and severity) model substantially enhanced the initial model’s predictive accuracy.Conclusion: Low TCBI was associated with a higher risk of SAP. In clinical practice, TCBI has shown predictive value for SAP, contributing to early intervention and treatment of SAP.Keywords: acute ischemic stroke, stroke-associated pneumonia, nutritional status, TCBI

Keywords

• acute ischemic stroke
• stroke-associated pneumonia
• nutritional status
• tcbi