Evidence map›Paper›PMID 42422201›Full record

ArticleFrontiers in neurology

Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach.

Hai Zhou, Changcun Chen, Hui Zheng, Yuheng Wu, Li Zhu, Changqing Zhou

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Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Hai ZhouDepartment of Sleep Medicine, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China.
Changcun ChenDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Hui ZhengDepartment of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Yuheng WuDepartment of Pain Management, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China.
Li ZhuDepartment of Sleep Medicine, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China.
Changqing ZhouDepartment of Sleep Medicine, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to describe the evolution patterns of cerebral hemodynamics in acute brain injury (ABI) patients. Methods: ABI patients with intracranial pressure (ICP), invasive arterial blood pressure, and heart rate (HR) records were identified from Medical Information Mart for the Intensive Care (MIMIC)-IV 3.0, eICU Collaborative Research Database (eICU-CRD) 2.0, and the Department of Neurosurgery of Longquan Hospital. Group-based multivariate trajectory (GBMT) modeling was employed to identify clusters of participants with similar evolution patterns of cerebral hemodynamics. Multivariate logistic regression analysis was used to examine the association between the GBMT clusters and outcomes. Features were selected using a random forest (RF) algorithm based on recursive feature elimination (RFE), and feature importance was interpreted using SHAP values. Furthermore, subgroup analyses were performed. Results: Data from 477 eligible patients from the MIMIC-IV and eICU-CRD databases were included in the trajectory analysis. Additionally, data from 519 patients from the Department of Neurosurgery were utilized for external validation. GBMT analyses identified five clusters with distinct cerebral hemodynamics evolution patterns. Compared to Cluster 1, Cluster 5 was the only one associated with an unfavorable outcome. Sensitivity analysis indicated that the effect size and direction in different subgroups were consistent, and the results were stable. Conclusion: This study identified a unique cerebral hemodynamics evolution pattern in ABI that was associated with unfavorable outcomes. Notably, our study suggested that the longitudinal analysis of multiple hemodynamic indicators may offer a novel perspective for the treatment of ABI patients.

Indexed as

acute brain injurycerebral hemodynamicsgroup-based multivariate trajectorygroup-based trajectory modelinglongitudinal analysis

Identifiers

PMID42422201
PMCPMC13341458

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