Risk factors analysis and nomograph model construction of unplanned readmission for ischemic stroke within 31 days in Wenzhou
To investigate independent risk factors for unplanned readmission of ischemic stroke patients within 31 days in Wenzhou, and establish a nomogram model for risk prediction. A total of 3,035 patients with ischemic stroke were randomly grouped (in an 8:2 ratio) into 2,428 training set and 607validatio...
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Published in | Frontiers in neurology Vol. 16; p. 1499564 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
27.03.2025
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Subjects | |
Online Access | Get full text |
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Summary: | To investigate independent risk factors for unplanned readmission of ischemic stroke patients within 31 days in Wenzhou, and establish a nomogram model for risk prediction.
A total of 3,035 patients with ischemic stroke were randomly grouped (in an 8:2 ratio) into 2,428 training set and 607validation set. Independent sample
-test, Pearson chi-square test, Fisher's exact and multivariate logistic regression analysis were used to determine the factors associated with 31-day unplanned readmission in ischemic stroke, and the nomogram was established and validated.
Six hundred and sixty nine patients (22.04%) had unplanned readmission. Atrial fibrillation, smoking, education of junior high school and below, length of stay >16 days, Hcy, and UA were independent risk factors for 31-day unplanned readmission in patients with ischemic stroke. The training set [AUC = 0.883 (95% CI = 0.867-0.899)] and validation set [AUC = 0.817 (95% CI = 0.777-0.858)], and the calibration curve closely resembled the ideal curve, demonstrating good agreement between the predicted and actual values, it shows that the prediction model has a good degree of differentiation and calibration. At the same time, the decision curve shows that the model has a high clinical net benefit rate.
The nomograph model established in this study to predict the risk of unplanned readmission of ischemic stroke patients within 31 days has good prediction ability. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Edited by: Arinjita Bhattacharyya, University of Louisville, United States Dileep Nair, RWTH Aachen University, Germany ORCID: Zhili Jin, orcid.org/0009-0003-1704-1300 Reviewed by: Aksheev Bhambri, University of Texas Southwestern Medical Center, United States Dilpreet Kour, Yale University, United States |
ISSN: | 1664-2295 1664-2295 |
DOI: | 10.3389/fneur.2025.1499564 |