Study on the Prognostic Prediction Model of Patients With Acute Intracerebral Hemorrhage by Artificial Intelligence
Study Details
Study Description
Brief Summary
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| intracerebral hemorrhage group Patients with the intracerebral hemorrhage who presented to the hospital within 24 hours of symptom onset |
Other: Functional outcome follow-up of patients
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
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Outcome Measures
Primary Outcome Measures
- patient outcome [3 month]
Neurological recovery status was measured by the modified Rankin Scale
Eligibility Criteria
Criteria
Inclusion Criteria:
-
- aged 18-80 years; 2. patients diagnosed with acute cerebral hemorrhage by CT examination; 3. complete non-contrast CT and CTA images; 4. the time interval from onset to the first baseline CT and CTA examination is less than 6 hours; 5. follow-up data within 3 months; 6. agree and sign a written document.
Exclusion Criteria:
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- Patients with secondary aneurysm hemorrhage; 2. Patients with secondary hemorrhage of cerebrovascular malformation; 3. Patients with dissecting aneurysm hemorrhage; 4. Patients with cerebral infarction hemorrhage transformation; 5. Patients lost to follow-up within 3 months; 6. CT or CTA images have a heavy artefact.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Beijing Tiantan Hospital, Capital Medical University | Beijing | Beijing | China | 100070 |
Sponsors and Collaborators
- Beijing Neurosurgical Institute
Investigators
- Principal Investigator: Shengjun Sun, Beijing Neurosurgical Instuitute
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- Gregório T, Pipa S, Cavaleiro P, Atanásio 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.
- 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). pii: 112. doi: 10.3390/jpm12010112.
- 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.
- 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.
- Pszczolkowski S, Manzano-Patrón 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.
- 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.
- 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.
- 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.
- 2022-2-1074