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A nomogram to predict cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients: A retrospective cohort study
To establish a nomogram model to predict early cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients. A retrospective cohort study was held between January 2016 and October 2018. One hundred twenty seven out of 170 consecutive patients with supratentorial spon...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824656/ https://www.ncbi.nlm.nih.gov/pubmed/31626144 http://dx.doi.org/10.1097/MD.0000000000017626 |
Sumario: | To establish a nomogram model to predict early cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients. A retrospective cohort study was held between January 2016 and October 2018. One hundred twenty seven out of 170 consecutive patients with supratentorial spontaneous intracranial hematoma were enrolled in this study. They were divided into development (n = 92) and validation (n = 35) dataset according to their admission time. Mini-mental State Examination (MMSE) was conducted between the third and the sixth month after the onset of stroke. MMSE ≤ 24 was considered as cognitive impairment. Univariate and multivariate logistic regression was used to screen for independent risk factors which correlate with cognitive impairment on the development dataset. A nomogram was built based on Akaike Information Criterion (AIC). Receiver operating characteristic (ROC) curve and calibration curve on development and validation dataset was drawn with each area under the curves (AUC) calculated. The decision curve analysis was also conducted with the development dataset. The bleeding volume, Glasgow Coma Scale (GCS), and intraventricular hemorrhage (IVH) are the most significant risk factors which may cause cognitive impairment both in the univariate and multivariate analysis. The finial model performed good discrimination ability on both development and validation dataset with AUC 0.911 and 0.919. Most patients would benefit from the model according to the decision curve analysis. A nomogram, constructed based on bleeding volume, GCS, and IVH can provide a feasible tool to evaluate cognitive impairment after supratentorial spontaneous intracranial hematoma in adult patients. |
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