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TRUVACE RECORD VERSION
record: TRV-2026-0572
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-26T06:09:47.781330Z
status: published
lens: g_space
sector: health
headline: Early Identification of Recovery Potential After Acute Brain Injury Using Functional Near-Infrared Spectroscopy
dek: Background Accurate early prognostication in patients with acute brain injury remains a major challenge in neurocritical care. Conventional bedside assessments provide limited insight into long-term outcomes and may not fully capture preserved brain function that supports recovery. Functional neuroimaging can detect brain activity not evident at the bedside, but its use in intensive care remains constrained by cost, logistics, and the need for stronger evidence supporting its value. Functional near-infrared spec…
gain_title: A machine learning model using bedside fNIRS functional connectivity during audio movie clips predicted 6-month functional outcome in ICU patients with acute brain injury with 81.3% balanced accuracy, outperforming clinical models.
problem_title: (none)
trace_subject: (none)
gain_reading: A machine learning model using bedside fNIRS functional connectivity during audio movie clips predicted 6-month functional outcome in ICU patients with acute brain injury with 81.3% balanced accuracy, outperforming clinical models.
gain_evidence: Bedside fNIRS provides objective neural measures associated with later functional recovery and behavioral responsiveness in patients with acute brain injury. | The fNIRS-based model predicted 6-month outcome with a balanced accuracy of 81.3% (sensitivity = 85.7%, specificity = 76.9%; p = 0.006), outperforming clinical models (balanced accuracy = 67.6%; p = 0.018). | Functional connectivity features were used to train a machine learning model to classify 6-month functional outcome
problem_reading: (none)
problem_evidence: (none)
quick_read: In a prospective cohort of 33 ICU patients with acute brain injury, investigators tested whether bedside functional near-infrared spectroscopy during passive audio movie listening could support early prognostication. Using functional connectivity features to train a machine learning model, they classified 6-month functional outcome defined by Glasgow Outcome Scale-Extended.

The findings matter because conventional bedside assessments provide limited insight into long-term recovery and advanced neuroimaging is costly and logistically constrained in critical care. While the fNIRS model outperformed clinical models in this small sample, the authors note that larger multicenter validation is needed to establish reliability and clinical utility.
limitation: Small single-center cohort of 33 patients with imbalanced outcomes limits generalizability and requires larger multicenter validation before clinical use.
tag: Evidence-backed gain
key_points: Prospective observational cohort of 33 ICU patients with acute brain injury who underwent fNIRS while listening to two audio-only movie clips. | Model predicted 6-month Glasgow Outcome Scale-Extended with balanced accuracy 81.3%, sensitivity 85.7%, specificity 76.9%, p=0.006 versus clinical model 67.6%. | Same fNIRS approach predicted recovery of behavioral responsiveness at 78.5% balanced accuracy, p=0.008, but did not significantly predict covert awareness at 70.4%, p=0.109.
rundown: Researchers recorded bedside functional near-infrared spectroscopy in 33 patients with acute brain injury in the ICU while they listened to two audio-only movie clips, then extracted functional connectivity features to train a classifier for 6-month Glasgow Outcome Scale-Extended.

The fNIRS-based classifier achieved 81.3% balanced accuracy for favorable versus unfavorable outcome and 78.5% for recovery of behavioral responsiveness, both statistically significant, while prediction of covert awareness was not significant and clinical models performed worse.
sources:
- peer_reviewed | Neurocritical Care | https://doi.org/10.1007/s12028-026-02605-0 | 2026-07-24
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