Inflammatory markers based on blood cell count predict functional outcomes in patients with acute ischemic stroke: construction and validation of a nomogram model / 国际脑血管病杂志

ABSTRACT

Objective:To investigate the value of inflammation markers based on blood cell count in the outcome assessment of acute ischemic stroke (AIS).Methods:Patients with AIS admitted to the Department of Neurology, Taizhou People's Hospital from January 2022 to April 2023 were included retrospectively. The demographic and clinical data of the patients were collected, particularly the related inflammatory markers based on blood cell count, including systemic inflammation response index (SIRI) and neutrophil/lymphocyte ratio (NLR). The patients included in the study were randomly divided into a modeling cohort and a validation cohort according to 7∶3. In the modeling cohort, the outcome assessment was performed based on the modified Rankin Scale score at 90 d after onset, and the score >2 was defined as poor outcome. Multivariate binary logistic regression model was used to screen for independent influencing factors on the outcome of patients with AIS. All independent influencing factors were included to construct a nomogram prediction model, and validate its predictive ability in the validation cohort. Results:A total of 389 patients with AIS were included and randomly divided into a modeling cohort ( n=272) and a validation cohort ( n=117) in 7:3. There was no significant difference in demographic and clinical data between the modeling cohort and the validation cohort. In the modeling cohort, 196 patients were in the good outcome group and 76 were in the poor outcome group. Multivariate logistic regression analysis showed that the baseline National Institutes of Health Stroke Scale score (odds ratio [ OR] 1.31, 95% confidence interval [ CI] 1.18-1.45; P<0.001), SIRI ( OR 1.64, 95% CI 1.04-2.58; P=0.033), NLR ( OR 1.18, 95% CI 1.02-1.36; P=0.024), smoking ( OR 2.51, 95% CI 1.16-5.46; P=0.020) and diabetes ( OR 2.48, 95% CI 1.13-5.48; P=0.024) were the independent risk factors for poor outcomes of patients with AIS. The above independent risk factors were included for drawing a nomogram prediction model. The receiver operating characteristic curve analysis showed that the area under the curve of the prediction model was 0.866 (95% CI 0.816-0.917), indicating that the model had good discrimination. The validation conducted in the validation cohort showed that the area under the curve of the prediction model was 0.884 (95% CI 0.804-0.964). The calibration curve and decision curve analysis also proved the reliability of the prediction model. Conclusions:SIRI and NLR are associated with poor outcomes of patients with AIS. The inclusion of SIRI and NLR in the nomogram model can accurately predict the risk of poor outcomes in patients with AIS.