Search

Purpose of Review: This review investigates the use of machine learning (ML) in prognosticating outcomes for traumatic brain injury (TBI). It underscores the benefits of ML models in processing and integrating complex, multimodal data-including clinical, imaging, and physiological inputs-to identify intricate non-linear relationships that traditional methods might overlook., Recent Findings: ML algorithms of clinical features, neuroimaging, and metrics from the autonomic nervous system enhance the early detection of clinical deterioration and improve outcome prediction. Challenges persist, including issues of data variability, model interpretability, and overfitting. However, advancements in model standardization and validation are key to enhancing their clinical applicability. ML-based, multimodal approaches offer transformative potential for personalized treatment planning and patient management. Future directions include integrating digital twins and real-time continuous data analysis, reinforcing the idea that comprehensive data amalgamation is essential for precise, adaptive prognostication and decision-making in neurocritical care, ultimately leading to better patient outcomes., Competing Interests: Declaration. Competing Interest: The authors declare no competing interests. Human and Animal Rights and Informed Consent: This article does not contain any studies with human or animal subjects performed by any of the authors., (© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Paroxysmal sympathetic hyperactivity (PSH) occurs in a subset of patients with traumatic brain injury (TBI) and is associated with worse outcomes. Sepsis is also associated with worse outcomes after TBI and shares several physiologic features with PSH, potentially creating diagnostic confusion and suboptimal management of each. This is the first study to directly investigate the interaction between PSH and infection using robust diagnostic criteria., Methods: We performed a retrospective cohort study of patients with TBI admitted to a level I trauma center intensive care unit with hospital length of stay of at least 2 weeks. From January 2016 to July 2018, 77 patients diagnosed with PSH were 1:1 matched by age and Glasgow Coma Scale to 77 patients without PSH. Trauma infectious diseases subspecialists prospectively documented assessments corroborating diagnoses of infection. Extracted data including incidence, timing, classification, and anatomical source of infections were compared according to PSH diagnosis. We also evaluated daily PSH clinical feature severity scores and systemic inflammatory response syndrome (SIRS) criteria and compared values for patients with and without confirmed infection, stratified by PSH diagnosis., Results: During the first 2 weeks of hospitalization, there were no differences in rates of suspected (62%) nor confirmed (48%) infection between patients with PSH and controls. Specific treatments for PSH were initiated on median hospital day 7 and for confirmed infections on median hospital day 8. SIRS criteria could identify infection only in patients who were not diagnosed with PSH., Conclusions: In the presence of brain injury-induced autonomic nervous system dysregulation, the initiation and continuation of antimicrobial therapy is a challenging clinical decision, as standard physiologic markers of sepsis do not distinguish infected from noninfected patients with PSH, and these entities often present around the same time. Clinicians should be aware that PSH is a potential driver of SIRS, and familiarity with its diagnostic criteria as proposed by the PSH assessment measure is important. Management by a multidisciplinary team attentive to these issues may reduce rates of inappropriate antibiotic usage and misdiagnoses., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)