Prediction models for post-thrombectomy brain edema in patients with acute ischemic stroke: a systematic review and meta-analysis

 Prediction of edema doesn't help survivors one bit, Survivors need protocols that will prevent edema. DO THE RESEARCH THAT GETS THERE!

Prediction models for post-thrombectomy brain edema in patients with acute ischemic stroke: a systematic review and meta-analysis

Lei Liu Chun-yu He ^* Jia-xin Yang Si-ting Zheng Jun Zhou Ye Kong Wen-bo Chen Yan Xie

• School of Nursing, Chengdu Medical College, Chengdu, Sichuan, China

Objective: The objective of this study is to systematically evaluate prediction models for post-thrombectomy brain edema in acute ischemic stroke (AIS) patients. This analysis aims to equip clinicians with evidence-based guidance for the selection of appropriate prediction models, thereby facilitating the early identification of patients at risk of developing brain edema post-surgery.

Methods: A comprehensive literature search was conducted across multiple databases, including PubMed, Web of Science, Embase, The Cochrane Library, CNKI, Wanfang, and Vip, aiming to identify studies on prediction models for post-thrombectomy brain edema in AIS patients up to January 2023. Reference lists of relevant articles were also inspected. Two reviewers independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines were employed to assess study bias and literature quality, respectively. We then used random-effects bivariate meta-analysis models to summarize the studies.

Results: The review included five articles, yielding 10 models. These models exhibited a relatively high risk of bias. Random effects model demonstrated that the AUC was 0.858 (95% CI 0.817–0.899).

Conclusion: Despite the promising discriminative ability shown by studies on prediction models for post-thrombectomy brain edema in AIS patients, concerns related to a high risk of bias and limited external validation remain. Future research should prioritize the external validation and optimization of these models. There is an urgent need for large-scale, multicenter studies to develop robust, user-friendly models for real-world clinical application.

Systematic review registration: https://www.crd.york.ac.uk, unique Identifier: CRD42022382790.

1. Introduction

AIS presents a substantial healthcare challenge due to its high incidence and disability rate (1). Recent clinical guidelines highlight the importance of endovascular mechanical thrombectomy (EVT) as an effective early treatment strategy for AIS (2). However, even with successful reperfusion achieved via early endovascular thrombectomy, approximately 50% of patients with AIS still encounter a range of adverse outcomes, including mortality (3). One significant complication contributing to early death in these patients is brain edema post-thrombectomy, with occurrence rates ranging from 10 to 75% (4). This condition often results in malignant neurological deterioration accompanied by significant brain swelling, potentially leading to tonsillar herniation, death, or functional impairment (5). Current therapeutic guidelines suggest decompressive craniectomy within 48 h of onset as an effective treatment for brain edema (6). Studies have shown a statistically significant difference in adverse outcomes between patients who underwent decompressive craniectomy within this 48-h window and those who did not (7). This highlights the necessity of early identification of high-risk patients and timely intervention to prevent or reduce the incidence of post-thrombectomy brain edema. Several researchers have aimed to create and validate predictive models to estimate the risk of post-thrombectomy brain edema in AIS patients. These models are intended to enhance early intervention and implement stratified management, thereby improving patient recovery. Despite these efforts, there is considerable variation among these studies in terms of study population, modeling methods, follow-up duration, and outcome measures. This systematic review aims to critically assess these models to inform their construction and application, as well as contribute to clinical strategies for preventing brain edema in AIS patients after thrombectomy. By conducting this review, we aspire to fill the current knowledge gaps and provide valuable insights to guide future research and improve clinical practice.

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