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 inFrontiers in neurology Vol. 16; p. 1499564
Main Authors Ma, Yingying, Jin, Zhili, Yi, Xianqiong, Ye, Xinxin
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 27.03.2025
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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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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