Searching for just a few words should be enough to get started. If you need to make more complex queries, use the tips below to guide you.
Article type: Research Article
Authors: Wang, Meia | Liu, Yalib; *
Affiliations: [a] Nursing Department, Linquan County People’s Hospital, Fuyang, Anhui, China | [b] Neurology Department, Linquan County People’s Hospital, Fuyang, Anhui, China
Correspondence: [*] Corresponding author: Yali Liu, Neurology Department, Linquan County People’s Hospital, No. 109 Xuangyang Road, Linquan County, Fuyang, Anhui, China. E-mail: liuyalilylm@126.com.
Abstract: BACKGROUND: The etiology of early neurological deterioration (END) occurring after intravenous thrombolysis is unclear. OBJECTIVE: To investigate the factors associated with END following intravenous thrombolysis in patients with acute ischemic stroke, and to construct a prediction model. METHODS: We selected a total of 321 patients with acute ischemic stroke, who were divided into two groups: the END group (n= 91) and the non-END group (n= 230). They were compared for their demographics, onset-to-needle time (ONT), door-to-needle time (DNT), related score results, and other data. The risk factors of the END group were identified using logistic regression analysis, and we constructed a nomogram model using the R software. A calibration curve was used to evaluate the calibration of the nomogram, and we assessed its clinical applicability using decision curve analysis (DCA). RESULTS: In our multivariate logistic regression analysis, we found that four indexes, namely, complication with atrial fibrillation, post-thrombolysis National Institutes of Health Stroke Scale (NIHSS) score, pre-thrombolysis systolic blood pressure (SBP), and serum albumin level, were independent risk factors for END following intravenous thrombolysis in the patients (P< 0.05). We constructed an individualized nomogram prediction model using the above four predictors. The AUC value of the nomogram model was 0.785 (95% CI: 0.727–0.845) after internal validation, and the mean absolute error (MAE) in the calibration curve was 0.011, which indicated that the nomogram model had good prediction value. The decision curve analysis indicated that the nomogram model was clinically relevant. CONCLUSION: The model was found to have excellent value in clinical application and prediction of END. This will be beneficial for healthcare providers to develop individualized prevention measures for END in advance, and thus reduce the incidence of END following intravenous thrombolysis.
Keywords: Early neurological deterioration (END), intravenous thrombolysis, nomogram, prediction model, risk factor
DOI: 10.3233/THC-230140
Journal: Technology and Health Care, vol. 31, no. 6, pp. 2213-2223, 2023
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
USA
Tel: +1 703 830 6300
Fax: +1 703 830 2300
sales@iospress.com
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
IOS Press
Nieuwe Hemweg 6B
1013 BG Amsterdam
The Netherlands
Tel: +31 20 688 3355
Fax: +31 20 687 0091
info@iospress.nl
For editorial issues, permissions, book requests, submissions and proceedings, contact the Amsterdam office info@iospress.nl
Inspirees International (China Office)
Ciyunsi Beili 207(CapitaLand), Bld 1, 7-901
100025, Beijing
China
Free service line: 400 661 8717
Fax: +86 10 8446 7947
china@iospress.cn
For editorial issues, like the status of your submitted paper or proposals, write to editorial@iospress.nl
如果您在出版方面需要帮助或有任何建, 件至: editorial@iospress.nl