Bayes at the Bedside: Biomarkers in Situations of Clinical Uncertainty

Laboratory biomarkers influence a large proportion of clinical decision-making, yet their application is often limited by incomplete validation and context-dependent interpretability. Serum neurofilament light chain (sNfL), a biomarker of neuroaxonal injury in multiple sclerosis (MS), exemplifies this challenge. Although associated with inflammatory activity, lesion burden, and disability progression at the population level, its translation into individual patient management remains problematic. In this Perspective, we synthesise current literature on sNfL in MS and apply Bayesian diagnostic reasoning as a conceptual framework for its interpretation in individualised MS care. The need for such a framework arises from the heterogeneity of MS pathology, in which subclinical inflammation and neurodegeneration may occur as partly dissociated processes that are incompletely captured by clinical and radiological measures. Consequently, substantial uncertainty persists in disease monitoring and therapeutic decision-making. In this setting, sNfL may provide complementary information, but its interpretation is complicated by biological variability, methodological differences, confounding factors (e.g., age, body mass index, and comorbidities), and the absence of universally validated thresholds. We argue that sNfL should be interpreted within a Bayesian framework, in which biomarker results modify rather than determine the probability of disease activity. Its clinical utility is likely greatest when the pre-test probability is intermediate but remains constrained by uncertainty in both test characteristics and clinical context, leading to uncertainty propagation. Overall, sNfL should be interpreted longitudinally and within multimodal clinical decision models. Further prospective studies are needed to better define its role in individualised MS management.