The leap to ordinal: detailed functional prognosis after traumatic brain injury with a flexible modelling approach
University of Cambridge（剑桥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Stirling（斯特林大学）；Leiden University Medical Center（莱顿大学医学中心）；Karolinska Institutet（卡罗林斯卡学院）
Shubhayu Bhattacharyay, Ioan Milosevic, Lindsay Wilson, David K. Menon, Robert D. Stevens, Ewout W. Steyerberg, David W. Nelson, Ari Ercole, the CENTER-TBI inve… 展开作者
Shubhayu Bhattacharyay, Ioan Milosevic, Lindsay Wilson, David K. Menon, Robert D. Stevens, Ewout W. Steyerberg, David W. Nelson, Ari Ercole, the CENTER-TBI investigators/participants
当创伤性脑损伤（TBI）患者在受伤后进入重症监护病房（ICU）时，早期预后对于基线风险调整和共同决策至关重要。TBI结局通常通过格拉斯哥预后量表扩展版（GOSE）被划分为受伤后6个月时功能恢复的8个有序等级。现有ICU预后模型预测GOSE某一阈值处的二分类结局（例如，生存预测[GOSE>1]或功能独立预测[GOSE>4]）。我们旨在开发有序预测模型，以同时预测每个GOSE评分的概率。从Collaborative European NeuroTrauma Effectiveness Research in TBI（CENTER-TBI）患者数据集ICU层的一项前瞻性队列（n=1,550，65个中心）中，我们提取了ICU入院24小时内的所有临床信息（1,151个预测因子）和6个月GOSE评分。我们分析了两个设计要素对有序模型性能的影响：（1）基线预测因子集，范围从包含10个经验证预测因子的精简集合，到所有可能预测因子的token嵌入表示；（2）建模策略，从有序逻辑回归到多项式深度学习。通过重复k折交叉验证，我们发现扩展基线预测因子集显著改善了有序预测性能，而增加分析复杂性则没有。这些提升中有一半可通过向精简集合添加8个高影响预测因子（2个人口统计学变量、4种蛋白质生物标志物和2项严重程度评估）实现。在最佳情况下，有序模型达到0.76（95% CI：0.74–0.77）的有序区分能力（有序c指数），并对6个月GOSE的有序变异实现57%（95% CI：54%–60%）的解释度（Somers' D）。我们的结果推动了对更高GOSE的信息性预测因子的搜索，以及有序动态预测模型的开发。
英文摘要：
When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into 8, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE>1] or functional independence [GOSE>4]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n=1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of 2 design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of 10 validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of 8 high-impact predictors (2 demographic variables, 4 protein biomarkers, and 2 severity assessments) to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%-60%) explanation of ordinal variation in 6-month GOSE (Somers' D). Our results motivate the search for informative predictors for higher GOSE and the development of ordinal dynamic prediction models.
Perelman School of Medicine（佩雷尔曼医学院）；University of Pennsylvania（宾夕法尼亚大学）
Baseline injury categorization is important to traumatic brain injury (TBI) research and treatment. Current categorization is dominated by symptom-based scores that insufficiently capture injury heterogeneity. In this work, we apply unsupervised clustering to identify novel TBI phenotypes. Our approach uses a generalized low-rank model (GLRM) model for feature selection in a procedure analogous to wrapper methods. The resulting clusters reveal four novel TBI phenotypes with distinct feature profiles and that correlate to 90-day functional and cognitive status.
icometrix（icometrix公司）；Technical University of Munich（慕尼黑工业大学）
Computer Tomography (CT) is the gold standard technique for brain damage evaluation after acute Traumatic Brain Injury (TBI). It allows identification of most lesion types and determines the need of surgical or alternative therapeutic procedures. However, the traditional approach for lesion classification is restricted to visual image inspection. In this work, we characterize and predict TBI lesions by using CT-derived radiomics descriptors. Relevant shape, intensity and texture biomarkers characterizing the different lesions are isolated and a lesion predictive model is built by using Partial…
Vanderbilt University（范德堡大学）；Vanderbilt University Institute of Imaging Science（范德堡大学影像科学研究所）
Veterans with mild traumatic brain injury (mTBI) have reported auditory and visual dysfunction that persists beyond the acute incident. The etiology behind these symptoms is difficult to characterize with current clinical imaging. These functional deficits may be caused by shear injury or micro-bleeds, which can be detected with special imaging modalities. We explore these hypotheses in a pilot study of multi-parametric MRI. We extract over 1,000 imaging and clinical metrics and project them to a low-dimensional space, where we can discriminate between healthy controls and patients with mTBI. …
Northeastern University（东北大学）；University of Southern California（南加州大学）
Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain injury (TBI) and is a major public health problem that has an estimated incidence that ranges from 2%-50%, depending on the severity of the TBI. Currently, the pathomechanism that in-duces epileptogenesis in TBI patients is unclear, and one of the most challenging goals in the epilepsy community is to predict which TBI patients will develop epilepsy. In this work, we used diffusion-weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)to meas…
The University of Arizona（亚利桑那大学）；Virginia Tech（弗吉尼亚理工大学）；University of Cincinnati（辛辛那提大学）
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. . Numerous XAI approaches have been developed thus far, and a comparative analysis of these strategies seems necessary to discern their relevance to clinical prediction models. To this end, we first implemented two prediction models for short- and long-term …
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Medical College of Wisconsin（威斯康星医学院）；University of California, San Diego（加州大学圣地亚哥分校）；Baylor College of Medicine（贝勒医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of California, San Francisco（加州大学旧金山分校）
Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic represen…
Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings
Ming Hsieh Department of Electrical and Computer Engineering（明希德电气与计算机工程系）；University of Southern California（南加州大学）；Epilepsy Center（癫痫中心）；Cleveland Clinic Neurological Institute（克利夫兰诊所神经医学研究院）
Technological University Dublin（都柏林理工大学）；Stanford University（斯坦福大学）；Ford Motor Company（福特汽车公司）
Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are differen…
Traumatic brain injury can be caused by various types of head impacts. However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain. The current definitions of head impact subtypes are based on impact sources (e.g., football, traffic accident), which may not reflect the intrinsic kinematic similarities of impacts across the impact sources. To investigate the potential new definitions of impact subtypes based on kinematics, 3,161 head impacts from various sources including simulation, co…
University of Washington（华盛顿大学）；University of California, Irvine（加利福尼亚大学尔湾分校）；VA Portland Health Care System（波特兰退伍军人医疗保健系统）；Oregon Health & Science University（俄勒冈健康与科学大学）
Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently i…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Vanderbilt University Institute of Imaging Science（范德堡大学影像科学研究所）；Vanderbilt Brain Institute（范德堡脑科学研究所）
Mild traumatic brain injury (mTBI) is a complex syndrome that affects up to 600 per 100,000 individuals, with a particular concentration among military personnel. About half of all mTBI patients experience a diverse array of chronic symptoms which persist long after the acute injury. Hence, there is an urgent need for better understanding of the white matter and gray matter pathologies associated with mTBI to map which specific brain systems are impacted and identify courses of intervention. Previous works have linked mTBI to disruptions in white matter pathways and cortical surface abnormalit…
Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis
School of Electrical Engineering and Computer Science, University of Queensland（昆士兰大学电气工程与计算机科学学院）；The Centre for Advanced Imaging, University of Queensland（昆士兰大学高级成像中心）；Australian Institute for Bioengineering and Nanotechnology, University of Queensland（昆士兰大学生物工程与纳米技术研究所）；ARC Training Centre for Innovation in Biomedical Imaging Technology（ARC生物医学成像技术创新培训中心）
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；VA Tennessee Valley Healthcare System（弗吉尼亚州田纳西谷医疗系统）；Department of Veterans Affairs Medical Center（退伍军人事务部医疗中心）
Brain imaging analysis on clinically acquired computed tomography (CT) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (TBI). However, in real clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest, pelvis) are typically captured along with whole brain CT scans. For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images. Therefore…
Mining the contribution of intensive care clinical course to outcome after traumatic brain injury
University of Cambridge（剑桥大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Humanitas University（Humanitas大学）；Karolinska Institutet（卡罗林斯卡学院）；University of Stirling（斯特灵大学）；Leiden University Medical Center（莱顿大学医学中心）；Cambridge Centre for Artificial Intelligence in Medicine（剑桥医学人工智能中心）
Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Emory University School of Medicine（埃默里大学医学院）；University of Florida School of Medicine（佛罗里达大学医学院）；Brown University（布朗大学）
Automated segmentation of medical imaging is of broad interest to clinicians and machine learning researchers alike. The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image. Image segmentation is a difficult task because of multiparametric heterogeneity within the images, an obstacle that has proven especially challenging in efforts to automate the segmentation of brain lesions from non-contrast head computed tomography (CT). In this research, we have experimented with multiple available deep learning a…
Strain and strain rate are effective traumatic brain injury predictors. Kinematics-based models estimating these metrics suffer from significant different distributions of both kinematics and the injury metrics across head impact types. To address this, previous studies focus on the kinematics but not the injury metrics. We have previously shown the kinematic features vary largely across head impact types, resulting in different patterns of brain deformation. This study analyzes the spatial distribution of brain deformation and applies principal component analysis (PCA) to extract the represen…
New York University（纽约大学）；New York University Shanghai（上海纽约大学）
Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which diffusion measures are the most informative with regard to cognitive functions in both the healthy state as well as after injury. In this study, we use diffusion MRI to formulate a predictive model for performance on working memory based on the most relevant MRI features. The key ch…
