ArticleFrontiers in neurology2023
A nomogram predicts early neurological deterioration after mechanical thrombectomy in patients with ischemic stroke.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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7 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- Prediction models for early neurological deterioration in patients with acute ischemic stroke: a systematic review and critical appraisal.Frontiers in neurologyPooled it
- Article
- Identification of signal-based gait features and blood analytes associated with stroke status and walking speed in mild acute ischemic stroke.BMC neurology · 2026Article
- Development of a simple clinical score to predict early neurological improvement after mechanical thrombectomy: a single-centre cohort study.BMC neurology · 2026Article
- Development and Validation of a Web-Based Machine Learning Model for Predicting Early Neurological Deterioration Following Stroke Thrombolysis: Multicenter Study.Journal of medical Internet research · 2025Article
- Risk Factors and Prognosis of Early Neurological Deterioration after Bridging Therapy.Current neurovascular research · 2024Article
- Related factors and a predictive model for early neurological deterioration after mechanical thrombectomy for acute ischemic stroke due to large vessel occlusion.Frontiers in neurologyArticle
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Early neurological deterioration (END) is common in acute ischemic stroke and is directly associated with poor outcome after stroke. Our aim is to develop and validate a nomogram to predict the risk of END after mechanical thrombectomy (MT) in acute ischemic stroke patients with anterior circulation large-vessel occlusion. Methods: We conducted a real-world, multi-center study in patients with stroke treated with mechanical thrombectomy. END was defined as a worsening by 2 or more NIHSS points within 72-hour after stroke onset compared to admission. Multivariable logistic regression was used to determine the independent predictors of END, and the discrimination of the scale was assessed using the C-index. Calibration curves were constructed to evaluate the calibration of the nomogram, and decision curves were used to describe the benefits of using the nomogram. Results: A total of 1007 patients were included in our study. Multivariate logistic regression analysis found age, admission systolic blood pressure, initial NIHSS scores, history of hyperlipemia, and location of occlusion were independent predictors of END. We developed a nomogram that included these 6 factors, and it revealed a prognostic accuracy with a C-index of 0.678 in the derivation group and 0.650 in the validation group. The calibration curves showed that the nomogram provided a good fit to the data, and the decision curves demonstrated a large net benefit. Discussion: Our study established and validated a nomogram to stratify the risk of END before mechanical embolectomy and identify high-risk patients, who should be more cautious when making clinical decisions.
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