Early Prediction of Secondary Complications and Prognosis After aSAH
Study Details
Study Description
Brief Summary
Aneurysmal subarachnoid haemorrhage (aSAH) is the most common and acute cerebrovascular disease, usually associated with a high mortality and morbidity, and with a 30% increased risk of re-rupture and a 50% increased risk of death from re-rupture.
The early stage of brain injury after subarachnoid hemorrhage is usually accompanied by complications such as delayed cerebral ischemia (DCI), rebleeding, hydrocephalus, and other organ damage, of which DCI is the most common complication in patients with SAH, accounting for about 30%, often directly determining the functional outcome of patients with aSAH. Most clinically present with no other cause of neurologic deficit 4 to 14 days after bleeding, a decrease in GCS score of 2 points and lasting >1 hour, or a new well-circumscribed low-density focus on computed tomography that is absent immediately after surgery. Since the reversible nature of vasospasm after bleeding allows DCI to be reversible or develop into cerebral infarction, predicting DCI after aSAH within the time window is critical, which is of great significance for guiding antivasospasm and other clinical treatments and improving prognosis. Hence, it is urgently to predict secondary complications and functional outcome after aSAH, which plays an important role in recognizing low and hish-risk patients. It is of great significance to guide stepdown unit and reduce medical cost of patients in intensive care unit.
| Condition or Disease | Intervention/Treatment | Phase |
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Detailed Description
In 2019, Fawaz et al. reported that the increase of leukocyte count within 72 hours of aSAH was closely related to the development of complication of DCI after aSAH especially WBC≥ 12.1 is more valuable in predicting DCI than WFNS and treatment methods. Yue et al. published a paper in the same year that considered neutrophil/lymphocyte ratio (NLR) as a clinical predictor of DCI after aSAH. In addition to serum markers, Hideyuki et al reported in Stroke in 2020 that blood clot CT HU in the interfoot cisterna was an accurate and reliable predictor of symptomatic vasospasm on admission CT in aSAH patients. Reviewing these literatures, there are some limitations or problems in predicting DCI with single predictor, including unverified predictor, large external variability, poor prediction accuracy, and poor clinical practicability and operability. Based on this, Liu et al. published a new study in September 2020 that used a common column graph to predict DCI, which was the only paper to use a Nomogram model to predict DCI. The final variables included in the model included age, Hunt-Hess grade, aneurysm location, admission blood pressure, etc. Compared with a single predictor, this prediction model improved its clinical operability and accuracy, but was not as convenient and operational as a dynamic Nomogram. At the same time, this model has not been verified, and its clinical applicability is not strong. There are currently no scholars applied dynamic Nomogram model and machine learning model fitting subarachnoid blood clot CT the Hu, serum markers, such as state of hospital patient data to predict the DCI. The predictors selected by the logistic regression model are placed in a dynamic Nomogram model, and the neurosurgeon uses this model to select the values corresponding to the results of the patient's examination to automatically analyze the probability of DCI in the patient. The principle is that the Coefficients are allocated based on the size of the regression coefficients between the variables. The coefficients of the maximum variable of the standard retrospective coefficient are 100 points. The coefficients corresponding to a probability of 0.5 are critical scores. The dynamic Nomogram model has obvious advantages over a single predictor or a common column chart, that is, it can significantly improve its clinical operability. At the same time, after internal and external verification, the stability of the model is improved and the variability is reduced, which can significantly improve the clinical accuracy and clinical applicability of DCI prediction.
At the same time, by comparing the optimum model to predict performance, for guiding the individual prevention of secondary complications and prognosis assessment has important clinical significance.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| aneurysmal subarachnoid hemorrhage primary subarachnoid hemorrhage caused by intracerebral ruputured-aneurysm |
Diagnostic Test: statiscal methods
statiscal methods including conventional logistic regression and several machine learning algorithms
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Outcome Measures
Primary Outcome Measures
- modified Rankin Scale for evaluating the prognosis [12 months]
A score greater than 3 indicates a poor prognosis, while a score less than 3 indicates a good prognosis
- Delayed cerebral ischemia [30 days]
A state of transition in cerebral ischemia
Secondary Outcome Measures
- Rebleeding [30 days]
Intracerebral aneurysm rerupture
- Hydrocephalus [30 days]
Obstruction of cerebrospinal fluid circulation leads to ventricle dilation
Eligibility Criteria
Criteria
Inclusion Criteria:
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Subarachnoid hemorrhage was confirmed by CT;
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the CTA and DSA examination confirmed is aneurysm rupture caused by subarachnoid hemorrhage;
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bleeding after 24 hours of adept 38 blood routine, biochemical function, blood coagulation function and craniocerebral CT;
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DCI was observed during 3 to 14 days after surgery;
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aneurysm clip by surgery or endovascular embolization.
Exclusion Criteria:
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Aneurysm rupture bleeding time more than 24 hours;
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by CTA and DSA examination found no intracranial aneurysm;
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Traumatic subarachnoid hemorrhage;
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the image data and check blood information is not complete;
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long-term anticoagulant drugs such as aspirin, wave dimensions;
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admitted to hospital with infectious diseases;
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merging other intracranial vascular malformation.
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Second Affiliated Hospital of Nanchang University
- Renmin Hospital of Wuhan University
Investigators
- Study Chair: Xingen Zhu, Prof, Second Affiliated Hospital of Nanchang University
- Principal Investigator: Qianxue Chen, Prof, Renmin Hospital of Wuhan University
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- SHNCU-aSAH