Diagnosis of in-hospital mortality using admission CT perfusion in severe traumatic brain injury patients (ACT-TBI study)
Bibliographic record
Abstract
BACKGROUND: Severe traumatic brain injury (TBI) stands as the leading cause of post-injury hospitalization, disability, and mortality globally. Imaging serves as a cornerstone in the assessment of patients with severe TBI and CT Perfusion (CTP) has emerged as an early prognostic tool. Our study aims to validate CTP features of non-survivable brain injury, upon hospital admission to characterize in-hospital mortality, through a well-powered prospective cohort study. METHODS: In a prospective cohort study, adult patients with severe TBI were recruited to undergo whole head CTP at the time of their first imaging. Interpretation of the CTP images were conducted by two independent neuroradiologists (JS and ME), blinded to clinical results and each other's assessment. Non-survivable brain injury was defined as a matched decrease of cerebral blood flow (CBF) and cerebral blood volume (CBV) in the brainstem. The results of CTP were not disclosed to the clinical team providing patient care, and the patients received standard institutional management. The primary outcome was a binary outcome of in-hospital mortality. The primary validity analysis involved calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for features of non-survivable brain injury on admission CTP compared to in-hospital mortality, along with 95% confidence intervals. RESULTS: Out of the 201 patients initially enrolled in the study, 195 patients (mean age 42.9 years; Male- 160, 82%) were included in the final analysis. Among the participants, a total of 55 patients (28.2%) died during their hospital stay. The odds ratio (OR) was highest for the presence of intracranial hemorrhage (ICH) (OR-20.25; 95% CI- 7.08-71.80, p < 0.001) and gun shot wound (GSW) (OR-22.67; 95% CI- 3.66-257.5, p = 0.003), which were independently associated with in-hospital mortality. With every decade of age, there was 1.77 times of (95% CI- 1.37-2.36, p < 0.001) higher odds of in-hospital mortality. Of the 55 patients with in-hospital mortality, 17 (31%) met the criteria of non-survival brain injury on the CTP at the time of hospital admission. Both CTP and CT-angiogram (CTA)A had 100% specificity and PPV. The highest sensitivity of 33% and NPV of 80% was seen with non-survivable criteria of CTP. As a result, this variable exhibited the highest accuracy of 82% with an area under the curve (AUC) of 0.67. The inter-rater reliability for CTP ranged from poor (kappa = 0.07) to fair (kappa = 0.44), indicating variability in agreement between raters. In contrast, the inter-rater reliability for CTA scales ranged from fair (kappa = 0.39) to substantial (kappa = 0.79), suggesting more consistent agreement among raters. CTP was found to be safe as no patients experience any complications associated with CTP. CONCLUSION: CTP features of non-survivable brain injury showed very high specificity and positive predictive value for diagnosing in-hospital mortality in patients with severe TBI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".