Prediction of Late Hospital Arrival in Patients with Mild and Rapidly Improving Acute Ischemic Stroke in a Rural Area of China

Yeping Song; Fei Shen; Qing Dong; Liling Wang; Jianhua Mi

doi:10.2147/RMHP.S414700

Prediction of Late Hospital Arrival in Patients with Mild and Rapidly Improving Acute Ischemic Stroke in a Rural Area of China

Risk Manag Healthc Policy. 2023 Jun 20:16:1119-1129. doi: 10.2147/RMHP.S414700. eCollection 2023.

Authors

Yeping Song¹, Fei Shen¹, Qing Dong¹, Liling Wang^#¹, Jianhua Mi^#²

Affiliations

¹ Cerebrovascular Disease Center, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200127, People's Republic of China.
² Health Management Center, Renji Hospital, School of Medical School, Shanghai Jiaotong University, Shanghai, 200127, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: Among all ischemic stroke patients, more than half are mild and rapidly improving acute ischemic stroke (MaRAIS) patients. However, many MaRAIS patients do not recognize the disease early on, and thus they delay access to the treatment that would be most effective if provided earlier. This is especially true in rural areas. The aim of this study was to develop and validate a late hospital arrival risk nomogram in a rural Chinese population of patients with MaRAIS.

Methods: We developed a prediction model based on a training dataset of 173 MaRAIS patients collected from September 9, 2019 to May 13, 2020. Data analyzed included demographics and disease characteristics. A least absolute shrinkage and selection operator (LASSO) regression model was used to optimize feature selection for the late hospital arrival risk model. Multivariable logistic regression analysis was applied to build a prediction model incorporating the features selected in the LASSO regression models. The discrimination, calibration, and clinical usefulness of the prediction model were assessed using the C-index, calibration plot, and decision curve analysis, respectively. Internal validation was then assessed using bootstrapping validation.

Results: Variables contained in the prediction nomogram included transportation mode, history of diabetes, knowledge of stroke symptoms, and thrombolytic therapy. The model had moderate predictive power with a C-index of 0.709 (95% confidence interval: 0.636-0.783) and good calibration. In the internal validation, the C-index reached 0.692. The risk threshold was 30-97% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice.

Conclusion: This novel nomogram, which incorporates transportation mode, history of diabetes, knowledge of stroke symptoms, and thrombolytic therapy, was conveniently applied to facilitate individual late hospital arrival risk prediction among MaRAIS patients in a rural area of Shanghai, China.

Keywords: late hospital arrival; mild and rapidly improving acute ischemic stroke; mild stroke; prehospital delay.

Grants and funding

This study was supported by Renji Hospital National Natural Breeding Fund (No. 2017PYQA01).