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Prognosis in moderate-severe traumatic brain injury in a Swedish cohort and external validation of the IMPACT models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Enblad: Neurosurgery.ORCID iD: 0000-0003-1218-6247
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Enblad: Neurosurgery.ORCID iD: 0000-0002-2534-8842
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Enblad: Neurosurgery.ORCID iD: 0000-0001-9369-3886
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Enblad: Neurosurgery.
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2022 (English)In: Acta Neurochirurgica, ISSN 0001-6268, E-ISSN 0942-0940, Vol. 164, no 3, p. 615-624Article in journal (Refereed) Published
Abstract [en]

Background: A major challenge in management of traumatic brain injury (TBI) is to assess the heterogeneity of TBI pathology and outcome prediction. A reliable outcome prediction would have both great value for the healthcare provider, but also for the patients and their relatives. A well-known prediction model is the International Mission for Prognosis and Analysis of Clinical Trials (IMPACT) prognostic calculator. The aim of this study was to externally validate all three modules of the IMPACT calculator on TBI patients admitted to Uppsala University hospital (UUH).

Method: TBI patients admitted to UUH are continuously enrolled into the Uppsala neurointensive care unit (NICU) TBI Uppsala Clinical Research (UCR) quality register. The register contains both clinical and demographic data, radiological evaluations, and outcome assessments based on the extended Glasgow outcome scale extended (GOSE) performed at 6 months to 1 year. In this study, we included 635 patients with severe TBI admitted during 2008–2020. We used IMPACT core parameters: age, motor score, and pupillary reaction.

Results: The patients had a median age of 56 (range 18–93), 142 female and 478 male. Using the IMPACT Core model to predict outcome resulted in an AUC of 0.85 for mortality and 0.79 for unfavorable outcome. The CT module did not increase AUC for mortality and slightly decreased AUC for unfavorable outcome to 0.78. However, the lab module increased AUC for mortality to 0.89 but slightly decreased for unfavorable outcome to 0.76. Comparing the predicted risk to actual outcomes, we found that all three models correctly predicted low risk of mortality in the surviving group of GOSE 2–8. However, it produced a greater variance of predicted risk in the GOSE 1 group, denoting general underprediction of risk. Regarding unfavorable outcome, all models once again underestimated the risk in the GOSE 3–4 groups, but correctly predicts low risk in GOSE 5–8.

Conclusions: The results of our study are in line with previous findings from centers with modern TBI care using the IMPACT model, in that the model provides adequate prediction for mortality and unfavorable outcome. However, it should be noted that the prediction is limited to 6 months outcome and not longer time interval.

Place, publisher, year, edition, pages
Springer Nature Springer Nature, 2022. Vol. 164, no 3, p. 615-624
National Category
Neurology
Identifiers
URN: urn:nbn:se:uu:diva-464149DOI: 10.1007/s00701-021-05040-6ISI: 000733864000001PubMedID: 34936014OAI: oai:DiVA.org:uu-464149DiVA, id: diva2:1627308
Funder
Science for Life Laboratory, SciLifeLabAvailable from: 2022-01-13 Created: 2022-01-13 Last updated: 2025-11-24Bibliographically approved
In thesis
1. Refining outcome prediction in traumatic brain injury
Open this publication in new window or tab >>Refining outcome prediction in traumatic brain injury
2025 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traumatic brain injury (TBI) remains a leading cause of mortality and a major contributor to global disability-adjusted life years in young and middle-aged adults. While animal studies have shown potential, effective pharmacological treatments have failed to materialize in the clinical setting. A primary challenge in TBI research is the inherent heterogeneity of the condition.

Outcome prediction models can reduce this complexity by better risk stratification for clinical trials, optimizing resource allocation, and improving prognostic communication with patients and families. TBI outcomes are commonly measured by the Glasgow Outcome Scale Extended (GOSE), an 8-point scale ranging from death (1) to upper good recovery (8). However, current prediction models typically dichotomize this scale into favorable/unfavorable outcomes which limits nuanced prognostication.

The primary objective of this thesis was to develop prediction models capable of predicting the full 8-grade GOSE. A secondary objective was to evaluate the integration of machine learning in clinical care, specifically assessing an AI decision support system for detecting intracranial hemorrhage in computed tomography (CT) scans.

We found that advanced machine learning methods performed comparably to standard statistical models when limited to admission variables. However, the incorporation of dynamic physiological data captured prognostic signals that static admission models missed, thereby improving prediction accuracy.

Regarding the AI diagnostic tool, while it successfully identified most hemorrhages, we observed no significant clinical benefit for the patients. This underscores that in clinical settings, the implementation strategy is as critical as the technology itself.

Finally, this thesis emphasizes that predictive models are not static tools. They are sensitive to temporal changes in populations and healthcare protocols. Therefore, future implementation must prioritize the continuous updating and validation of these models.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2025. p. 115
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 2217
Keywords
Traumatic brain injury, TBI, Acquired brain injury, Artificial Intelligence, Machine learning, prognosis, prognostic model, Neurosurgery, Neuroradiology.
National Category
Neurosciences
Identifiers
urn:nbn:se:uu:diva-571980 (URN)978-91-513-2687-0 (ISBN)
Public defence
2026-01-16, H:son-Holmdahlsalen, Dag Hammarskjölds Väg 8, Uppsala, 12:15 (Swedish)
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Supervisors
Available from: 2025-12-18 Created: 2025-11-24 Last updated: 2025-12-18

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Rostami, ElhamGustafsson, DavidHånell, AndersHowells, TimothyLenell, SamuelLewén, AndersEnblad, Per

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