Nomogram to predict unfavorable outcome of endovascular thrombectomy for large ischemic core

Han, Nannan; Zhang, Xiaobo; Zhang, Yu; Liu, Yu; Zhang, Yongqin; Ma, Haojun; Ge, Hanming; Li, Shilin; Zhang, Xiao; Yan, Xudong; Li, Tengfei; Gao, Bin; Du, Chengxue; Ji, Xinchao; Shi, Wenzhen; Tian, Ye; Chang, Mingze

doi:10.1002/acn3.51826

Cited by 5 publications

(1 citation statement)

References 32 publications

(78 reference statements)

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“…As gradual advancements and novel iterations of prognosis prediction models continue to emerge, 51 our understanding of ischemic stroke will improve, as will our comprehension and assessment of patient prognoses.…”

Section: Application Of Imaging Radiomics In Ischemic Cerebrovascular...mentioning

confidence: 99%

Application of radiomics in ischemic stroke

Yang,

Cai,

Liu

et al. 2024

J Int Med Res

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In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

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Section: Application Of Imaging Radiomics In Ischemic Cerebrovascular...mentioning

confidence: 99%

Application of radiomics in ischemic stroke

Yang,

Cai,

Liu

et al. 2024

J Int Med Res

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Endovascular thrombectomy versus medical management on outcomes with infarct volumes more than 70 mL

Han,

Zhang,

Zhang

et al. 2024

Ann Clin Transl Neurol

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ObjectiveEndovascular thrombectomy (EVT) in patients with large infarct volume remains controversial. The aim of this study is to compare clinical outcomes between EVT and medical management in acute large vessel occlusion with infarct volumes larger than 70 mL on diffusion‐weighted magnetic resonance imaging (DWI).MethodsA prospective observational cohort study was conducted, including patients with anterior cerebral circulation occlusion due to ischemic stroke with infarct volumes larger than 70 mL within 24 h of onset between July 2018 and June 2023. Eligible patients were divided into two groups: the EVT group and the medical management (non‐EVT) group. The main outcomes were functional independence and mortality at 90 days. To assess clinical endpoints, we selected variables including age, NIHSS score, infarct volume, and occlusion location for 1:1 propensity score (PS) matching and PS adjustment using inverse probability of treatment weighting (IPTW).ResultsAmong the 131 identified patients (mean [SD] age, 69.9 [13.7] years; 58 female), the median infarct volume was 123.6 mL. Of these patients, 75 (57.3%) underwent EVT. After PS adjustment, EVT was not associated with functional independence (10.9% vs. 10.9%; p = 1.000) or mortality (43.5% vs. 47.8%; p = 0.675). Additionally, after PS adjustment using IPTW, EVT was also not associated with a functional independence (15.8% vs. 13.7%; p = 0.767) or mortality (46.8% vs. 44.0%; p = 0.762).ConclusionThis study provides real‐world evidence regarding infarct volumes larger than 70 mL, indicating that EVT does not provide benefits compared to medical management alone when considering age, NIHSS score, infarct volume, and occlusion location.

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An externally validated prognostic model for critically ill patients with traumatic brain injury

Lu,

Zhang,

Jiang

et al. 2024

Ann Clin Transl Neurol

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ObjectivePatients with traumatic brain injury (TBI) who are admitted to the intensive care unit often exhibit critical conditions; thus, early prediction of in‐hospital mortality is crucial. In this study, we aimed to develop a reliable and easily promotable model for predicting the in‐hospital mortality of critically ill patients with TBI using easily accessible indicators and validate the model using external data.MethodsPatient data from the Medical Information Mart for Intensive Care‐IV 2.2 database were used as training and internal validation sets to establish and internally validate the prognostic model. Data from the Affiliated Dongyang Hospital of Wenzhou Medical University were used for external validation. The Boruta algorithm was used for the initial feature selection, followed by univariate and multivariate logistic regression analyses to identify the final independent predictors. The predictive performance was evaluated using a receiver operating characteristic curve, calibration curve, clinical practicality decision curve analysis, and clinical impact curve.ResultsThis study included 3225 patients (training set: 2042; internal validation set: 874; and external validation set: 309). Ten variables were selected for inclusion in the nomogram model: age, mechanical ventilation usage, vasoactive agent usage, intracerebral hemorrhage, temperature, respiration rate, white blood cell count, platelet count, red blood cell distribution width, and glucose. The nomogram demonstrated good predictive performance in both the internal and external validation sets.InterpretationWe developed an externally validated nomogram that exhibited good discrimination, calibration, and clinical utility for predicting in‐hospital mortality in critically ill patients with TBI.

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Nomogram to predict unfavorable outcome of endovascular thrombectomy for large ischemic core

Cited by 5 publications

References 32 publications

Application of radiomics in ischemic stroke

Application of radiomics in ischemic stroke

Endovascular thrombectomy versus medical management on outcomes with infarct volumes more than 70 mL

An externally validated prognostic model for critically ill patients with traumatic brain injury

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