Neurofilament light chain for classifying the aetiology of alteration of consciousness

Abstract

Neurofilament light chain has become a promising biomarker for neuroaxonal injury; however, its diagnostic utility is limited to chronic disorders or specific contexts. Alteration of consciousness is a common clinical problem with diverse aetiologies, many of which require timely diagnoses. We evaluated the value of neurofilament light chain alone, as well as creating diagnostic models, in distinguishing causes of alteration of consciousness. Patients presenting with alteration of consciousness were enrolled. Initial clinical data of each participant were evaluated by a neurologist to give a provisional diagnosis. Each participant subsequently received advanced investigations and follow-up to conclude the final diagnosis. All diagnoses were classified into a structural or non-structural cause of alteration of consciousness. Plasma and cerebrospinal fluid levels of neurofilament light chain were measured. Cerebrospinal fluid neurofilament light chain and other clinical parameters were used to develop logistic regression models. The performance of cerebrospinal fluid neurofilament light chain, the neurologist's provisional diagnosis, and the model to predict the final diagnosis were compared. For the results, among 71 participants enrolled, 67.6% and 32.4% of their final diagnoses were classified as structural and non-structural, respectively. Cerebrospinal fluid neurofilament light chain demonstrated an area under the curve of 0.75 (95% confidence interval 0.63-0.88) which was not significantly different from a neurologist's provisional diagnosis 0.85 (95% confidence interval 0.75-0.94) (P = 0.14). The multivariable regression model using cerebrospinal fluid neurofilament light chain and other basic clinical data achieved an area under the curve of 0.90 (95% confidence interval 0.83-0.98). In conclusion, neurofilament light chain classified causes of alteration of consciousness with moderate accuracy. Nevertheless, including other basic clinical data to construct a model improved the performance to a level that was comparable to clinical neurologists.