Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome
Objectives: This study aimed to develop a model to predict the risk of cerebral infarction in acute vestibular syndrome and assist emergency physicians in quickly identifying patients with cerebral infarction. Materials and methods: We looked at 262 patients who were split into cerebral infarction a...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023020595 |
| _version_ | 1797837055302041600 |
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| author | Guiming Lin Fangfang Liu Hengshi Xu Guanshui Bao |
| author_facet | Guiming Lin Fangfang Liu Hengshi Xu Guanshui Bao |
| author_sort | Guiming Lin |
| collection | DOAJ |
| description | Objectives: This study aimed to develop a model to predict the risk of cerebral infarction in acute vestibular syndrome and assist emergency physicians in quickly identifying patients with cerebral infarction. Materials and methods: We looked at 262 patients who were split into cerebral infarction and peripheral vertigo groups. Stepwise regression and Lasso's approach were used to screen for variables, and Boothstrap's method was used to evaluate the model's discrimination and calibration. The model's performance was compared against TriAGe+, ABCD2, and PCI scores using the area under the receiver operator characteristic curve. Clinical decision-making was aided by the use of clinical impact and decision curves. Results: In the end, nine risk factors were chosen for model 2, and ten risk factors were chosen for model 1. Model 2 was adopted as the final model. The areas under the receiver operator curve value of the model2 were 0.910(P = 0.000), much higher than the areas under the receiver operator curve value of the TriAGe + scores system and that of the PCI scores system. The clinical decision curve shows that when the threshold probability is 0.05, using the nomogram to predict cerebral infarction has more benefits than either the treat-all-patients scheme or the treat-none scheme. The clinical impact curve shows that when the threshold probability is 0.6 the model predicts disease occurrence in general agreement with the occurrence of the real disease. Conclusion: This study model can help emergency room physicians quickly triage and treat patients by accurately identifying cerebral infarction patients. |
| first_indexed | 2024-04-09T15:19:45Z |
| format | Article |
| id | doaj.art-c86d789b02ed4b258ce2672d2281c1a6 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-09T15:19:45Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c86d789b02ed4b258ce2672d2281c1a62023-04-29T14:51:18ZengElsevierHeliyon2405-84402023-04-0194e14852Development of a model to predict the risk of cerebral infarction in acute vestibular syndromeGuiming Lin0Fangfang Liu1Hengshi Xu2Guanshui Bao3Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, ChinaDepartment of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, ChinaDepartment of Blood Transfusion, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, China; Corresponding author. Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, China.Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, China; Corresponding author. Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Mohe Road280, Baoshan District, Shanghai, China.Objectives: This study aimed to develop a model to predict the risk of cerebral infarction in acute vestibular syndrome and assist emergency physicians in quickly identifying patients with cerebral infarction. Materials and methods: We looked at 262 patients who were split into cerebral infarction and peripheral vertigo groups. Stepwise regression and Lasso's approach were used to screen for variables, and Boothstrap's method was used to evaluate the model's discrimination and calibration. The model's performance was compared against TriAGe+, ABCD2, and PCI scores using the area under the receiver operator characteristic curve. Clinical decision-making was aided by the use of clinical impact and decision curves. Results: In the end, nine risk factors were chosen for model 2, and ten risk factors were chosen for model 1. Model 2 was adopted as the final model. The areas under the receiver operator curve value of the model2 were 0.910(P = 0.000), much higher than the areas under the receiver operator curve value of the TriAGe + scores system and that of the PCI scores system. The clinical decision curve shows that when the threshold probability is 0.05, using the nomogram to predict cerebral infarction has more benefits than either the treat-all-patients scheme or the treat-none scheme. The clinical impact curve shows that when the threshold probability is 0.6 the model predicts disease occurrence in general agreement with the occurrence of the real disease. Conclusion: This study model can help emergency room physicians quickly triage and treat patients by accurately identifying cerebral infarction patients.http://www.sciencedirect.com/science/article/pii/S2405844023020595StrokeVertigoDizzinessCerebral infarctionPredictNomogram |
| spellingShingle | Guiming Lin Fangfang Liu Hengshi Xu Guanshui Bao Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome Heliyon Stroke Vertigo Dizziness Cerebral infarction Predict Nomogram |
| title | Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| title_full | Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| title_fullStr | Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| title_full_unstemmed | Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| title_short | Development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| title_sort | development of a model to predict the risk of cerebral infarction in acute vestibular syndrome |
| topic | Stroke Vertigo Dizziness Cerebral infarction Predict Nomogram |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023020595 |
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