Evidence map›Paper›PMID 39086933›Full record

ArticleFrontiers in physiology2024

The factors associated with the modified Fisher grade in patients with aneurysmal subarachnoid hemorrhage.

Di Zhao, Yating Li, Jianzhong Cui

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Article in Frontiers in physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Di Zhao *Department of Neurosurgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Yating Li *School of Nursing, Hebei Medical University, Shijiazhuang, China.
Jianzhong CuiDepartment of Surgery, Hebei Medical University, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a life-threatening medical condition with a high fatality and morbidity rate. There was a substantial link between the modified Fisher grade of aSAH and the neurological function deficit. This study aimed to analyze the factors associated with the modified Fisher grade of aSAH using a machine learning approach. Methods: A multi-center observational study was conducted. The patients with aSAH were recruited from five tertiary hospitals in China. The volume of hemorrhage in aSAH was measured using the modified Fisher grade scale. The risk factors responsible for the modified Fisher grade of aSAH were analyzed, which include sociodemographic factors, clinical factors, blood index, and ruptured aneurysm characteristics. We built several tree-based machine learning models (XGBoost, CatBoost, LightGBM) for prediction and used grid search to optimize model parameters. To comprehensively evaluate the model, we used Accuracy, Precision, Area Under the Receiver Operating Characteristic Curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), and Brier as evaluation indicators to assess the model performance and select the best model. Results: A total of 888 patients with aSAH were recruited, of whom 305 with modified Fisher grade of 3 and 4. The results show that the XGBoost model has the highest AUROC of 0.772, and the indicators are better than CatBoost and LightGBM. The feature importance graph shows that the top feature variables include platelet, thrombin time, fibrinogen, preadmission systolic blood pressure, activated partial thromboplastin time, and the time interval between the onset of aSAH and the first-time CT examination. Conclusion: The factors responsible for the modified Fisher grade of aSAH were identified, which offered valuable insights for future research and clinical intervention. These risk factors should be controlled in the treatment of unruptured aneurysms, and appropriate treatment can be given if necessary to reduce the risk of severe hemorrhage after aneurysm rupture.

Indexed as

aneurysmal subarachnoid hemorrhageFisher gradeintracranial hemorrhagemachine learning approachrisk factor

Identifiers

PMID39086933
PMCPMC11289185

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.