Factors associated with early neurological deterioration after intravenous thrombolysis in acute cerebral infarction patients and establishment of a predictive model.

Objective: To analyze the factors influencing early neurological deterioration (END) after intravenous thrombolysis in patients with acute cerebral infarction (ACI) based on real-world data, and to establish a nomogram predictive model.

Methods: The clinical data of 148 ACI patients who received intravenous thrombolytic therapy within 4.5 hours of onset at Nantong Rici Hospital Affiliated with Yangzhou University, from January 2020 to December 2023, were retrospectively analyzed. Patient clinical and laboratory data were collected. Patients were divided into END and non-END groups according to whether they developed END after intravenous thrombolysis. Factors influencing the emergence of END were identified by univariate and multivariate logistic regression analyses. Risk factors were included to construct a nomogram risk predictive model, which was validated for efficacy. Model discrimination was assessed using the receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC). Model fitting was evaluated using a calibration curve, and consistency was assessed by Hosmer-Lemeshow (HL) analysis.

Results: END occurred in 27 of 148 patients (18.24%). Multivariate analysis identified age, National Institute of Health stroke scale (NIHSS) score, fibrinogen, and the time from onset to thrombolysis as factors influencing END in ACI patients after thrombolysis. A nomogram predictive model was constructed based on the above indicators. The AUC for the model in predicting END in the training set and the test set was 0.994 (95% CI: 0.982-1.000) and 0.977 (95% CI: 0.940-1.000), respectively. HL test showed high goodness of fit (χ² = 1.953, P = 0.982), and the calibration curve showed good agreement between the predicted and observed values.

Conclusion: Age, NIHSS score, fibrinogen, and time from onset to thrombolysis are significant factors influencing the development of END after thrombolysis in ACI patients. The predictive model based on these four variables demonstrates good discriminatory power and may assist in clinical decision-making.