Lesion Without Pain, Pain Without Certainty: A Critical Reappraisal of the NeuPSIG Diagnostic Algorithm for Neuropathic Pain.

BACKGROUND: The NeuPSIG diagnostic algorithm is regarded as the reference framework for the diagnosis of neuropathic pain. It is based on a hierarchical model in which diagnostic certainty increases with the presumed objectivity of the evidence, progressing from patient-reported symptoms to clinical examination and, ultimately, to confirmatory tests demonstrating a neurological lesion.

METHODS: In this position paper, we critically examine the conceptual foundations of this framework. We argue that the distinction between subjective and objective evidence is less clear-cut than assumed, as so-called objective biomarkers are themselves influenced by patient-related, examiner-dependent and methodological factors. Moreover, the identification of a neurological lesion does not establish a causal relationship with pain, raising fundamental questions about the validity of using lesion-based markers as surrogates for pain classification.

RESULTS: Drawing on evidence from bedside examination, quantitative sensory testing, neurophysiology and structural biomarkers, we highlight the limitations of the current hierarchical approach. We show that clinical reasoning in neuropathic pain does not follow a linear progression from subjective to objective data, but rather relies on the integration of multiple, context-dependent sources of information.

CONCLUSION: We propose an alternative framework based on converging evidence, in which patient history, pain characteristics and clinical examination are accorded equivalent weight, and diagnostic confidence emerges from their coherence rather than from a hierarchy of objectivity. Within this model, investigations aimed at identifying a neurological lesion are repositioned as tools for aetiological clarification rather than as determinants of pain classification. In our opinion, this approach offers a more conceptually coherent and clinically relevant framework for the diagnosis of neuropathic pain.

SIGNIFICANCE STATEMENT: While the NeuPSIG algorithm has been useful to standardize the diagnosis of neuropathic pain in the research setting, this algorithm is based on a hierarchical model in which objective measures (sensory deficits, complementary investigations) largely outweigh self-reported symptoms. We challenge this assumption by showing that 'objective' measures are also prone to variability and propose a model which gives equivalent weight to patient reported outcomes. This model may offer a more clinically relevant approach to the diagnosis of neuropathic pain.