Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke

Yina Wang; Weijiao Feng; Jie Peng; Fen Ye; Jun Song; Xiaoyan Bao; Chaosheng Li

doi:10.1016/j.jocn.2024.04.022

Development and validation of a risk prediction model for aspiration in patients with acute ischemic stroke

J Clin Neurosci. 2024 Apr 22:124:60-66. doi: 10.1016/j.jocn.2024.04.022. Online ahead of print.

Authors

Yina Wang¹, Weijiao Feng², Jie Peng³, Fen Ye², Jun Song⁴, Xiaoyan Bao⁵, Chaosheng Li⁶

Affiliations

¹ Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China; Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, China.
² Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China.
³ Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, China.
⁴ Department of Otolaryngology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China.
⁵ Department of Nephrology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China.
⁶ Department of Neurology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu Province, China. Electronic address: wxsylcs@163.com.

PMID: 38652929
DOI: 10.1016/j.jocn.2024.04.022

Abstract

Background: Aspiration is a frequently observed complication in individuals diagnosed with acute ischemic stroke, leading to potentially severe consequences. However, the availability of predictive tools for assessing aspiration probabilities remains limited. Hence, our study aimed to develop and validate a nomogram for accurately predicting aspiration probability in patients with acute ischemic stroke.

Methods: We analyzed 30 potential risk factors associated with aspiration in 359 adult patients diagnosed with acute ischemic stroke. Advanced statistical techniques, such as Least absolute shrinkage and selection operator (LASSO) and Multivariate Logistic regression, were employed to identify independent predictors. Subsequently, we developed a nomogram prediction model based on these predictors, which underwent internal validation through 1000 bootstrap resampling. Two additional cohorts (Cohort A n = 64; Cohort B, n = 105) were included for external validation. The discriminatory power and calibration performance of the nomogram were assessed using rigorous methods, including the Hosmer-Lemeshow test, area under the receiver operating characteristic curve (AUC), calibration curve analyses, and decision curve analyses (DCA).

Results: The nomogram was established based on four variables: sputum suction, brain stem infarction, temporal lobe infarction, and Barthel Index score. The predictive model exhibited satisfactory discriminative ability, with an area under the receiver operating characteristic curve of 0.853 (95 % confidence interval, 0.795-0.910), which remained consistent at 0.852 (95 % confidence interval, 0.794-0.912) during the internal validation. The Hosmer-Lemeshow test (P = 0.394) and calibration curve demonstrated favorable consistency between the predicted and observed outcomes in the development cohort. The AUC was 0.872 (95 % confidence interval, 0.783-0.962) in validation cohort A and 0.877 (95 % confidence interval, 0.764-0.989) in validation cohort B, demonstrating sustained accuracy. DCA showed a good net clinical benefit of the nomogram.

Conclusions: A nomogram for predicting the probability of aspiration in patients with acute ischemia has been successfully developed and validated.

Keywords: Aspiration; Nomogram; Risk model; acute ischemic stroke (AIS).