TY - JOUR
T1 - Novel approaches to prediction in severe brain injury
AU - Fidali, Brian C.
AU - Stevens, Robert D.
AU - Claassen, Jan
PY - 2020/12/1
Y1 - 2020/12/1
N2 - PURPOSE OF REVIEW: Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science. RECENT FINDINGS: Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques. SUMMARY: Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.
AB - PURPOSE OF REVIEW: Recovery after severe brain injury is variable and challenging to accurately predict at the individual patient level. This review highlights new developments in clinical prognostication with a special focus on the prediction of consciousness and increasing reliance on methods from data science. RECENT FINDINGS: Recent research has leveraged serum biomarkers, quantitative electroencephalography, MRI, and physiological time-series to build models for recovery prediction. The analysis of high-resolution data and the integration of features from different modalities can be approached with efficient computational techniques. SUMMARY: Advances in neurophysiology and neuroimaging, in combination with computational methods, represent a novel paradigm for prediction of consciousness and functional recovery after severe brain injury. Research is needed to produce reliable, patient-level predictions that could meaningfully impact clinical decision making.
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U2 - 10.1097/WCO.0000000000000875
DO - 10.1097/WCO.0000000000000875
M3 - Review article
C2 - 33105151
AN - SCOPUS:85096151857
SN - 1350-7540
VL - 33
SP - 669
EP - 675
JO - Current Opinion in Neurology
JF - Current Opinion in Neurology
IS - 6
ER -