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Spontaneous intracerebral hemorrhage(SICH) is the most lethal and disabling stroke. Timely and accurate assessment of patient prognosis could facilitate clinical decision making and stratified management of patients and is important for improving patient clinical prognosis. However, current studies on the prediction of prognosis of patients with SICH are limited and only include a single variable, with less precise results and inconvenient clinical application, which may lead to delays in effective patient treatment. Our group's previous studies on SICH showed that hematoma heterogeneity and the degree of contrast extravasation within the hematoma are closely related to the clinical outcome of patients, but they are difficult to describe quantitatively based on imaging signs. Based on this, we propose to use radiomics to quantitatively extract hematoma features from NCCT and CTA images, combine them with patients' clinical information and laboratory tests, study their relationship with the prognosis of cerebral hemorrhage, and use artificial intelligence to establish a rapid and accurate prognostic prediction model for patients with SICH, which is of great significance to guide clinical individualized treatment.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| intracerebral hemorrhage group | Patients with the intracerebral hemorrhage who presented to the hospital within 24 hours of symptom onset |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Functional outcome follow-up of patients | Other | Patients were followed up by telephone after discharge, every 4 weeks, until the end of the 3-month follow-up. Their functional status was determined based on the MRS score (modified Rankin Scale). Those with less than 3 points were defined as having a good prognosis, and those with more than 3 points were defined as having a poor prognosis |
| Measure | Description | Time Frame |
|---|---|---|
| patient outcome | Neurological recovery status was measured by the modified Rankin Scale | 3 month |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with acute cerebral hemorrhage (within 6 hours of the onset of symptoms) who presented to the hospital between May 2022 and September 2024 and had complete medical records.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Shengjun Sun | Contact | 13611293369 | sunshengjun0212@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Shengjun Sun | Beijing Neurosurgical Instuitute | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Tiantan Hospital, Capital Medical University | Recruiting | Beijing | Beijing Municipality | 100070 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 33860831 | Background | Pszczolkowski S, Manzano-Patron JP, Law ZK, Krishnan K, Ali A, Bath PM, Sprigg N, Dineen RA. Quantitative CT radiomics-based models for prediction of haematoma expansion and poor functional outcome in primary intracerebral haemorrhage. Eur Radiol. 2021 Oct;31(10):7945-7959. doi: 10.1007/s00330-021-07826-9. Epub 2021 Apr 16. | |
| 31385050 |
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To obtain individual participant data, please contact the principal investigator.
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| ID | Term |
|---|---|
| D002543 | Cerebral Hemorrhage |
| ID | Term |
|---|---|
| D020300 | Intracranial Hemorrhages |
| D002561 | Cerebrovascular Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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|
| Xie H, Ma S, Wang X, Zhang X. Noncontrast computer tomography-based radiomics model for predicting intracerebral hemorrhage expansion: preliminary findings and comparison with conventional radiological model. Eur Radiol. 2020 Jan;30(1):87-98. doi: 10.1007/s00330-019-06378-3. Epub 2019 Aug 5. |
| 35055424 | Background | Guo R, Zhang R, Liu R, Liu Y, Li H, Ma L, He M, You C, Tian R. Machine Learning-Based Approaches for Prediction of Patients' Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage. J Pers Med. 2022 Jan 14;12(1):112. doi: 10.3390/jpm12010112. |
| 30458727 | Background | Gregorio T, Pipa S, Cavaleiro P, Atanasio G, Albuquerque I, Chaves PC, Azevedo L. Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis. BMC Med Res Methodol. 2018 Nov 20;18(1):145. doi: 10.1186/s12874-018-0613-8. |
| 30354984 | Background | Fu F, Sun S, Liu L, Gu H, Su Y, Li Y. Iodine Sign as a Novel Predictor of Hematoma Expansion and Poor Outcomes in Primary Intracerebral Hemorrhage Patients. Stroke. 2018 Sep;49(9):2074-2080. doi: 10.1161/STROKEAHA.118.022017. |
| 33123072 | Background | Wang J, Wang W, Liu Y, Zhao X. Associations Between Levels of High-Sensitivity C-Reactive Protein and Outcome After Intracerebral Hemorrhage. Front Neurol. 2020 Oct 6;11:535068. doi: 10.3389/fneur.2020.535068. eCollection 2020. |
| 33183885 | Background | Menon G, Johnson SE, Hegde A, Rathod S, Nayak R, Nair R. Neutrophil to lymphocyte ratio - A novel prognostic marker following spontaneous intracerebral haemorrhage. Clin Neurol Neurosurg. 2021 Jan;200:106339. doi: 10.1016/j.clineuro.2020.106339. Epub 2020 Oct 28. |
| 32847959 | Background | Morotti A, Arba F, Boulouis G, Charidimou A. Noncontrast CT markers of intracerebral hemorrhage expansion and poor outcome: A meta-analysis. Neurology. 2020 Oct 6;95(14):632-643. doi: 10.1212/WNL.0000000000010660. Epub 2020 Aug 26. |
| 33743639 | Background | Tseng WC, Wang YF, Wang TG, Hsiao MY. Early spot sign is associated with functional outcomes in primary intracerebral hemorrhage survivors. BMC Neurol. 2021 Mar 20;21(1):131. doi: 10.1186/s12883-021-02146-3. |
| D009422 | Nervous System Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D006470 | Hemorrhage |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |