|Other Abstract||Objective To explore the correlation between clinical laboratory indicators and ischemic stroke (IS), and establish clinical models to evaluate and early warning the occurrence and development of IS through retrospective study.
Methods The blood samples, demographic data, neurology scores, and imaging data of 300 IS patients who were admitted to the Department of Neurology of Lanzhou University Second Hospital from June 2019 to December 2020 were collected. The patient’s information was confirmed and supplemented through telephone consultation. Then, we entry the tested items of 90 common clinical laboratory indicators such as blood routine, biochemical items, coagulation, and thyroid function directly. Missing items and patient serum Lp-PLA2, CRP, NT-proBNP, IL-6, PCT, FFA were entered after supplementary testing. After stratifying the data based on the cerebral infarction area, degree of neurological deficit, carotid artery plaque and intracranial aortic stenosis in patients with IS, independent influencing factors and joint diagnostic model were determined by analysis of differences between groups, collinearity test, unconditional binary logistic regression, and ROC curve. Finally, the model calibration chart and nomogram in the R software were used to verify the early warning performance of the joint diagnosis model.
Results 1) Cys C, CO2, PHOS, IBIL, ApoAⅠ/ApoB and educational level are independent influencing factors of cerebral infarction area in patients with IS;The diagnosis model including patient educational level, Cys C, CO2, PHOS, D dimer, ApoAⅠ/ApoB, ApoB levels has better diagnostic efficiency for non-lacunal cerebral infarction. 2) NE/HDL-C, ApoAⅠ/ApoB and education level are independent influencing factors of neurological deficit in IS patients;the diagnosis model including patient education level, FT3, NE/HDL-C, and ApoAⅠ/ApoB level is effective in the diagnosis of moderate to severe neurological deficit better. 3) Cys C, FIB and age are independent risk factors for carotid artery plaque in patients with IS;the diagnosis model including patient age, Glu, Cys C and FIB levels has a better diagnostic performance for carotid artery plaque. 4) History of type 2 diabetes, hospitalized hypertension, Cl and TC are independent risk factors for aortic stenosis in patients with IS;including the history of type 2 diabetes, a diagnostic model of Cl, TC, Cys C and NT-proBNP levels for the diagnosis of aortic stenosis is better.
Conclusion Laboratory indicators and demographic information are closely related to the incidence and prognosis of IS. The models developed in this study have good clinical value after internal and external verification, which provides a new basis for IS evaluation and early warning.|