Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis

An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study

“… 18 , 40 However, it has also been shown that the addition of these factors does not have a significant effect on the enhancement of the model effect. 17 , 41 Second, our model focused on the short-term mortality of patients with sTBI, which is one of the most important and practical concerns in clinical practice. However, the long-term prognosis is also very important but was not assessed in this study.…”

PaCO2 Association with Traumatic Brain Injury Patients Outcomes at High Altitude: A Prospective Single-Center Cohort Study.

“…This re ects the complexity of treatment and prognosis when many factors are involved, leaving aside the variability of management across centers and regions (53). Despite this challenge, some prognostic models have been developed and validated, for instance, The Corticosteroid Randomization After Signi cant Head Injury (CRASH) model and the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) in TBI model (54,55,56). These models estimate the probability of disability and mortality and consider factors such as age, Glasgow motor score, pupillary reactivity, and imaging ndings on head CT scans.…”

Ethical Considerations in Neuroprognostication Following Acute Brain Injury

“…[91][92][93][94] These models carry promise to predict mortality and functional outcome up to 6 months following injury, but they have not been validated to predict outcomes beyond 6 months, 95 and challenges remain in applying group-level analyses to individual patients. 96 For example, a meta-analysis of prognostic models in severe TBI highlights how the predictive capacity of models varies depending on the size and case-mix variation of the datasets used for development and validation, and may be influenced by factors such as age, geographic location, medical center, and outcome distributions that could be dissimilar to the target population when implemented externally. 96 ML and other AI tools are uniquely positioned to aid in neuroprognostication, and may provide added performance over classical statistical and algorithmic models.…”

“…96 For example, a meta-analysis of prognostic models in severe TBI highlights how the predictive capacity of models varies depending on the size and case-mix variation of the datasets used for development and validation, and may be influenced by factors such as age, geographic location, medical center, and outcome distributions that could be dissimilar to the target population when implemented externally. 96 ML and other AI tools are uniquely positioned to aid in neuroprognostication, and may provide added performance over classical statistical and algorithmic models. 97 These techniques utilize an iterative learning process to improve predictive performance, and allow for incorporating nonlinear relationships.…”

“…96 For example, a meta-analysis of prognostic models in severe TBI highlights how the predictive capacity of models varies depending on the size and case-mix variation of the datasets used for development and validation, and may be influenced by factors such as age, geographic location, medical center, and outcome distributions that could be dissimilar to the target population when implemented externally. 96…”