Diagnostic utility of patient history, clinical examination and screening tool data to identify neuropathic pain in...

Low back-related leg pain (LBLP) is a challenge for healthcare providers to manage. Neuropathic pain (NP) is highly prevalent in presentations of LBLP and an accurate diagnosis of NP in LBLP is essential to ensure appropriate intervention. In the absence of a gold standard, the objective of this systematic review was to evaluate the diagnostic utility of patient history, clinical examination and screening tool data for identifying NP in LBLP.

This systematic review is reported in line with PRISMA and followed a pre-defined and published protocol. CINAHL, EMBASE, MEDLINE, Web of Science, Cochrane Library, AMED, Pedro and PubMed databases, key journals and the grey literature were searched from inception to 31 July 2019. Eligible studies included any study design reporting primary diagnostic data on the diagnostic utility of patient history, clinical examination or screening tool data to identify NP in LBLP, in an adult population. Two independent reviewers searched information sources, assessed risk of bias (QUADAS-2) and used GRADE to assess overall quality of evidence.

From 762 studies, 11 studies were included. Nine studies out of the 11 were at risk of bias. Moderate level evidence supports a cluster of eight signs (age, duration of disease, paroxysmal pain, pain worse in leg than back, typical dermatomal distribution, worse on coughing/sneezing/straining, finger to floor distance and paresis) for diagnosing lumbosacral nerve root compression, demonstrating moderate/high sensitivity (72%) and specificity (80%) values. Moderate level evidence supports the use of the StEP tool for diagnosing lumbar radicular pain, demonstrating high sensitivity (92%) and specificity (97%) values.

Overall low-moderate level evidence supports the diagnostic utility of patient history, clinical examination and screening tool data to identify NP in LBLP. The weak evidence base is largely due to methodological flaws and indirectness regarding applicability of the included studies. The most promising diagnostic tools include a cluster of 8 patient history/clinical examination signs and the StEP tool. Low risk of bias and high level of evidence diagnostic utility studies are needed, in order for stronger recommendations to be made.