ArticleRisk management and healthcare policy2022
A Dynamic Nomogram to Predict the 3-Month Unfavorable Outcome of Patients with Acute Ischemic Stroke.
Article in Risk management and healthcare policy, 2022. 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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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis.Frontiers in neurology · 2023Pooled it
- 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 Predictive Nomogram for Intra-Hospital Mortality in Acute Ischemic Stroke Patients Using LASSO Regression.Clinical interventions in aging · 2024Article
- Nomogram to predict 3 month prognosis of acute ischemic stroke among young adults.Frontiers in neurology · 2024Article
- Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke.PloS one · 2024Article
- Determinants of urinary tract infection in hospitalized patients with acute ischemic stroke.BMC neurology · 2023Article
- Development and validation of a nomogram model for predicting unfavorable functional outcomes in ischemic stroke patients after acute phase.Frontiers in aging neuroscience · 2023Article
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Authors and funding
5 authors.
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Abstract
Purpose: Despite receiving standard-of-care treatments, a significant proportion of patients with acute ischemic stroke (AIS) are left with long-term functional impairment. Therefore, an easy-to-use tool for predicting of unfavorable outcome following AIS plays an important role in clinical practice. This study was aimed to develop a dynamic nomogram to predict the 3-month unfavorable outcome for AIS patients. Methods: This was a prospective observational study conducted in consecutive patients with AIS admitted to our stroke center between September 2019 and June 2020. Baseline demographic, clinical, and laboratory information were obtained. The primary outcome was evaluated with modified Rankin Scale (mRS) scores at 3 months. Least absolute shrinkage and selection operator regression was used to select the optimal predictive factors. Multiple logistics regression was performed to establish the nomogram. Decision curve analysis (DCA) was applied to assess the clinical utility of the nomogram. The calibration and discrimination property of the nomogram was validated by calibration plots and concordance index. Results: A total of 93 eligible patients were enrolled: 28 (30.1%) patients had unfavorable outcome (mRS >2). Glycosylated hemoglobin (OR, 1.541; 95% CI, 1.051-2.261), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) (OR, 0.635; 95% CI, 0.463-0.871), and National Institute of Health Stroke Scale (NIHSS) (OR 1.484; 95% CI, 1.155-1.907) were significant predictors of the poor outcome of patients with AIS and included into the nomogram model. The nomogram showed good calibration and discrimination. C-index was 0.891 (95% CI, 0.854-0.928). DCA confirmed the clinical usefulness of the model. The dynamic nomogram can be obtained at the website: https://odywong.shinyapps.io/DBT_21/. Conclusion: The dynamic nomogram, comprised of glycosylated hemoglobin, ASPECTS, and NIHSS score at day 14, may be able to predict the 3-month unfavorable outcome for AIS patients.
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