Prognostic nomogram for acute cerebral infarction based on risk factors: treatment with urinary kallidinogenase and...

To investigate the prognostic factors in patients with acute cerebral infarction (ACI) treated with urinary kallidinogenase combined with edaravone-dexborneol and to construct a predictive model.

A total of 120 ACI patients admitted to Dongyang People's Hospital, affiliated to Wenzhou University, from January 2022 to December 2023 were retrospectively collected, of whom 101 were included after screening. An additional 105 ACI patients admitted to other hospitals during the same period were collected as an external validation cohort. Prognosis was assessed using the modified Rankin Scale (mRS), and patients were classified into the good prognosis group (mRS ≤ 2 points) and the poor prognosis group (mRS > 2 points). Clinical data of the two groups were collected for univariate and binary logistic regression analyses to construct a nomogram prediction model, and ROC curve analysis was used to validate the predictive performance.

Univariate analysis indicated that comorbidities, TOAST classification, infarct location, thrombolytic/anticoagulant therapy drugs, age, disease duration, National Institutes of Health Stroke Scale (NIHSS) score at admission, Glasgow Coma Scale (GCS) score at admission, and fasting blood glucose were factors associated with poor prognosis. Binary logistic regression analysis showed that brainstem infarction, clopidogrel as thrombolytic/anticoagulant therapy, and NIHSS score at admission were independent risk factors. Model Y was constructed, and the Hosmer-Lemeshow test yielded χ² = 9.660 and P = 0.290. A nomogram prediction model was developed, and ROC curve analysis showed that the AUC of the training set was 0.867 (95% CI = 0.789-0.867), the AUC of the test set was 0.769 (95% CI = 0.502-1.000), and the AUC of the validation cohort was 0.879 (95% CI = 0.810-0.949).

The prognosis of ACI patients treated with urinary kallidinogenase combined with edaravone-dexborneol is influenced by multiple factors. Clinicians may construct a prognostic prediction model based on previous medical records to identify risk factors and adjust treatment strategies to improve prognosis.