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Published in: Clinical Neuroradiology 2/2023

Open Access 15-12-2022 | Stroke | Original Article

A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis

Authors: Yusen Cai, Yuting Gu, Yanhong Wang, Peng Wang, Lei Zhang, Chaolai Liu, Jianfeng Chu, Hui Li, Zhe Lu, Yafei Zhou, Huakun Liu

Published in: Clinical Neuroradiology | Issue 2/2023

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Abstract

Background

Acute large vessel occlusion due to underlying intracranial atherosclerotic stenosis (ICAS-LVO) increases the difficulty of revascularization, resulting in frequent re-occlusion. The establishment of its pathogenesis before endovascular treatment (EVT) is beneficial for patients. We aimed at developing and validating a clinical prediction model for ICAS-LVO patients before EVT.

Methods

Patients with acute large vessel occlusion at Jining No. 1 People’s Hospital from January 2019 to September 2021 were retrospectively included as the training cohort. The 70 patients who met the inclusion and exclusion criteria were included in the validation cohort (October 2021 to May 2022). Demographics, onset form, medical history, digital subtraction angiography (DSA) imaging data, and laboratory test data were collected. Preprocedural parameters for the ICAS-LVO risk prediction model were established by stepwise logistic regression controlling for the confounding effects. Then, we constructed a nomogram model and evaluated its performance via the Hosmer-Lemeshow goodness-of-fit test, area under the ROC curve (AUC) analysis.

Results

The 231 acute LVO patients were included in the final analysis, 74 (32.3%) patients were ICAS-LVO. A preoperative diagnosis prediction model consisting of five predictors for ICAS-LVO, including fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2. The model depicted an acceptable calibration (Hosmer-Lemeshow test, p = 0.451) and good discrimination (AUC, 0.941; 95% confidence interval, 0.910–0.971). The optimal cut-off value for the ICAS-LVO scale was 2 points, with 86.5% sensitivity, 91.1% specificity, and 90.5% accuracy. In the validation cohort, the discriminative ability was promising with an AUC value of 0.897, implying a good predictive performance.

Conclusion

The established ICAS-LVO scale, which is composed of five predictors: fluctuating symptoms, NIHSS < 16, atrial fibrillation, tapered sign, and ASITN/SIR score ≥ 2, has a good predictive value for ICAS-LVO in Chinese populations.
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Metadata
Title
A Clinical Prediction Model for Patients with Acute Large Vessel Occlusion Due to Underlying Intracranial Atherosclerotic Stenosis
Authors
Yusen Cai
Yuting Gu
Yanhong Wang
Peng Wang
Lei Zhang
Chaolai Liu
Jianfeng Chu
Hui Li
Zhe Lu
Yafei Zhou
Huakun Liu
Publication date
15-12-2022
Publisher
Springer Berlin Heidelberg
Published in
Clinical Neuroradiology / Issue 2/2023
Print ISSN: 1869-1439
Electronic ISSN: 1869-1447
DOI
https://doi.org/10.1007/s00062-022-01241-3

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