Observational studyScientific reports2019
Machine learning-based dynamic mortality prediction after traumatic brain injury.
Observational study in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care.Critical care (London, England) · 2020Trial
- Neuroplasticity after traumatic brain injury: mechanisms and clinical implications.Irish journal of medical science · 2026Review
- External validation and comparative performance of Marshall, Rotterdam, and Helsinki computed tomography scores in adult traumatic brain injury.BMC emergency medicine · 2026Article
- Predictive models for the occurrence of expansive intracranial hematomas and outcomes after surgical evacuation in patients with traumatic brain injury in Uganda: a prospective cohort study.Frontiers in surgery · 2026Article
- Influence of early temperature trajectories on clinical outcomes in traumatic brain injury: a multicenter validation study using machine learning.European journal of medical research · 2025Article
- A Hybrid Bidirectional Deep Learning Model Using HRV for Prediction of ICU Mortality Risk in TBI Patients.Journal of healthcare informatics research · 2025Article
- Transcranial photobiomodulation promotes traumatic brain injury recovery via modulating glial cell polarization and neuroinflammation: a study of 1064 nm light-emitting diodes.Journal of translational medicine · 2025Article
- Review
- Traumatic Brain Injury: Advances in Diagnostic Techniques and Treatment Modalities.Journal of clinical medicine · 2025Review
- Evaluation of CT and MRI Radiomics for an Early Assessment of Diffuse Axonal Injury in Patients with Traumatic Brain Injury Compared to Conventional Radiological Diagnosis.Clinical neuroradiology · 2025Article
- Mortality During In-Hospital Stay and the First 24 h After Decompressive Craniectomy in Severe Traumatic Brain Injury: A Multi-Center, Retrospective Propensity Score-Matched Study.Journal of clinical medicine · 2025Article
- Predicting outcomes after moderate and severe traumatic brain injury using artificial intelligence: a systematic review.NPJ digital medicine · 2025Article
- Traumatic Brain Injury and Artificial Intelligence: Shaping the Future of Neurorehabilitation-A Review.Life (Basel, Switzerland) · 2025Review
- Machine Learning Approaches to Prognostication in Traumatic Brain Injury.Current neurology and neuroscience reports · 2025Review
- Clinical Prediction Models in Neurocritical Care: An Overview of the Literature and Example Application to Prediction of Hospital Mortality in Traumatic Brain Injury.Neurocritical care · 2025Review
- Exploration of simultaneous transients between cerebral hemodynamics and the autonomic nervous system using windowed time-lagged cross-correlation matrices: a CENTER-TBI study.Acta neurochirurgica · 2024Article
- Time to death and its predictors among traumatic brain injury patients admitted to East Amhara comprehensive specialized hospitals, Ethiopia: retrospective cohort study.BMC neurology · 2024Article
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.
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