关键词: 急性缺血性卒中; 就医决策延迟; 列线图; 预测模型

Abstract: Objective  To explore the important risk factors of medical decision-making delay in patients with acute ischemic stroke (AIS) and construct a nomogram prediction model, aiming to provide an individualized assessment tool for early identification of high-risk patients with medical decision-making delay.
Methods  AIS patients admitted to three hospitals in Shenzhen between September 2024 and April 2025 were prospectively enrolled. Patients were divided into a delay group (>1 h) and a non-delay group (≤1 h) based on the time interval from symptom onset or recognition to the first decision to seek medical help. Multivariate logistic regression analysis was used to identify independent influencing factors of medical decision-making delay, and a nomogram prediction model was constructed. The model performance was evaluated using ROC curves, Hosmer-Lemeshow goodness of fitting test, calibration curves, and decision curve analysis. 
Results  A total of 344 AIS patients were enrolled in this study, among whom 201 had medical decision-making delay. Multivariate logistic regression analysis showed that compared with the non-delay group, patients in the delay group had a younger mean age (OR 2.303, 95%CI 1.201-4.416, P=0.012), lower educational level (OR 3.908, 95%CI 1.460-10.463, P=0.007), higher proportion of diabetes mellitus history (OR 1.923, 95%CI 1.054-3.509, P=0.033), lower NIHSS scores at onset (OR 3.245, 95%CI 1.700-6.191, P<0.001), higher proportion of poorer understanding of stroke (OR 3.262, 95%CI 1.247-8.532, P=0.016), and higher proportion of tendency to adopt negative coping style (OR 11.436, 95%CI 6.069-21.550, P<0.001). The AUC of the nomogram prediction model constructed based on these six factors was 0.861 (95%CI 0.820-0.902). The Hosmer-Lemeshow goodness of fitting test indicated that the model had a good fit (χ²=8.064, P=0.427). The calibration curves showed a high degree of consistency between the predicted values and observed values of the model. Decision curve analysis showed that the model yielded a high clinical net benefit within a wide range of risk thresholds.
Conclusions  The nomogram prediction model constructed with age, educational level, history of diabetes mellitus, NIHSS score at onset, understanding of stroke, and disease coping style as core variables can serve as a valid predictive tool for clinically assessing the risk of medical decision-making delay in patients with AIS.

Key words: Acute ischemic stroke; Medical decision-making delay; Nomogram; Prediction model