Total intracranial volume as a covariate for predicting prognosis in patients with primary intracerebral hemorrhage

Wang, Lixiang; Liu, Chunyang; Lu, Enzhou; Zhang, Haopeng; Xu, Xun; Li, Rui; Yuan, Chao; Sun, Jian; Zhou, Qi; Chen, Xin; Wang, Lu; Yang, Guang

doi:10.1016/j.clineuro.2022.107135

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Additional factors potentially influencing functional outcomes included INR, BUN, epencephalon hemorrhage, surgery, and primary hematoma volume. Several reasons could explain these results: (1) elevated INR has already been demonstrated to be associated with a poor (25,26). Therefore, early intervention strategies should be implemented for identified modifiable risk factors in order to improve patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors

Feng,

Wang,

Wang

et al. 2023

Front. Neurol.

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BackgroundThis study aimed to identify the risk factors and construct a prediction model for the prognosis of intracerebral hemorrhage (ICH) at discharge, 3 months, and 12 months.MethodsA total of 269 patients with ICH were retrospectively enrolled at our hospital between January 2014 and August 2016. The prognosis of ICH was assessed using the modified Rankin Scale (mRS); an mRS score > 2 was considered a poor outcome. The primary endpoint was the 3-month mRS, whereas the secondary endpoints included the mRS scores at discharge and 12 months, and mortality.ResultsThe Glasgow Coma Scale (GCS), National Institutes of Health (NIH) stroke scale, International Normalized Ratio (INR), blood urea nitrogen (BUN), epencephalon hemorrhage, and primary hematoma volume were significantly associated with a poor mRS score at 3 months. The predictive value of the prediction model based on these factors for a poor mRS score was 87.8%. Furthermore, a poor mRS score at discharge was affected by the GCS, NIH stroke scale, and primary hematoma volume; the constructed model based on these factors had a predictive value of 87.6%. In addition, the GCS, NIH stroke scale, and surgery were significantly related to a poor mRS score at 12 months; the predictive value of the constructed model based on the aforementioned factors for a poor mRS score was 86.5%. Finally, primary hematoma volume is significantly associated with the risk of 12 months mortality.ConclusionsThe study identified risk factors and constructed a prediction model for poor mRS scores and mortality at discharge, 3 and 12 months in patients with ICH. The prediction models for mRS scores showed a relatively high predictive performance.

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Section: Discussionmentioning

confidence: 99%

Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors

Feng,

Wang,

Wang

et al. 2023

Front. Neurol.

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“…Although the rapid development of modern medical science and technology and the continuous development of minimally invasive surgical techniques and equipment provide new methods for the treatment of hypertensive intracerebral hemorrhage, the total mortality of intracerebral hemorrhage is still very high. Hematoma volume was a determinant of mortality within 30 days [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network in Microsurgery Treatment of Spontaneous Intracerebral Hemorrhage

Wu¹,

Chen

2022

Computational and Mathematical Methods in Medicine

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Objective. To explore the convolutional neural network (CNN) method in measuring hematoma volume-assisted microsurgery for spontaneous cerebral hemorrhage. Methods. A total of 120 patients with spontaneous cerebral hemorrhage were selected and randomly divided into control and CNN groups with 60 patients in each group. Patients in the control group received traditional Tada formula to calculate hematoma volume and microsurgery. Convolutional neural network algorithm segmentation was used to measure hematoma volume, and microsurgery was performed in the CNN group. This article assessed neurological function, ability to live daily, complication rate, and prognosis. Results. The incidence of postoperative complications in the CNN group (13.33%) was lower than the control group (43.33%). The neurological function and daily living ability in the CNN groups were recovered better. The incidence of poor prognosis in the CNN group (16.67%) was lower than the control group (30.00%). Conclusion. Convolutional neural network measurement of hematoma volume to assist microsurgical treatment of spontaneous intracerebral hemorrhage patients is conducive to early recovery, reducing the damage to the patients’ cerebral nerves.

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Correspondence on the article: “Exploring the relationship between admission pulse pressure and clinical features in patients with spontaneous supratentorial intracerebral hemorrhage”

Navarrete,

Sobral,

de Sousa Santos

2024

Neurosurg Rev

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Total intracranial volume as a covariate for predicting prognosis in patients with primary intracerebral hemorrhage

Cited by 4 publications

References 19 publications

Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors

Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors

Convolutional Neural Network in Microsurgery Treatment of Spontaneous Intracerebral Hemorrhage

Correspondence on the article: “Exploring the relationship between admission pulse pressure and clinical features in patients with spontaneous supratentorial intracerebral hemorrhage”

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