Cardiometabolic index and modified cardiometabolic index are associated with early neurological deterioration in patients with acute ischemic stroke

Background Early neurological deterioration (END) in patients with acute ischemic stroke (AIS) leads to a poor prognosis. Previous studies suggest a high risk of END associated with obesity and metabolic abnormalities. The primary aim of this study was to determine if cardiometabolic index (CMI) and modified CMI (MCMI) are linked to END in patients with AIS. Methods This study retrospectively included 563 patients with AIS who had not received reperfusion therapy. Among the participants, 215 (38.2%) were female, with a median age of 69 years (interquartile range: 60–75) and a median baseline National Institutes of Health Stroke Scale (NIHSS) score of 2 (interquartile range: 1–3). According to the TOAST classification, 317 cases (56.3%) were identified as large artery atherosclerosis, 58 cases (10.3%) as cardioembolism, and 188 cases (33.4%) as small-artery occlusion. Patients were classified as experiencing END if their total NIHSS score increased by ≥ 2 points or the motor NIHSS score increased by ≥ 1 point within the first 72 h following admission. Multivariate Logistic regression was used to evaluate whether CMI and MCMI were independently associated with the occurrence of END in AIS patients. Restricted cubic spline (RCS) regression analyzed the nonlinear relationship between CMI, MCMI, and END. Additionally, subgroup analyses were conducted to evaluate the applicability of the findings in different populations. Results A total of 123 subjects were identified as having combined END during hospitalization. The CMI and MCMI levels in the END group were significantly elevated compared to the non-END group ( p 0.001). Multivariate logistic regression analysis indicated that both high-level CMI and MCMI, when treated as categorical or continuous variables, are independent risk factors for END in AIS patients (all p 0.05). Moreover, subgroup analysis showed that this association was stable in different populations (all p for interaction 0.05). The RCS curve showed nonlinear associations between CMI ( p for nonlinear = 0.048), MCMI ( p for nonlinear 0.001) and END. The areas under the curves of CMI and MCMI were 0.643 (95% Confidence interval (CI): 0.601–0.682) and 0.665 (95%CI: 0.625–0.704), respectively. Conclusion Our study showed that CMI and MCMI at admission were independently associated with END in AIS patients, which could be helpful for early risk stratification of stroke patients.