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. 2023 Mar 31;18(3):e0281856.
doi: 10.1371/journal.pone.0281856. eCollection 2023.

Targeting earlier diagnosis: What symptoms come first in Degenerative Cervical Myelopathy?

Colin F Munro  1 , Ratko Yurac  2   3 , Zipser Carl Moritz  4 , Michael G Fehlings  5   6   7 , Ricardo Rodrigues-Pinto  8   9 , James Milligan  10 , Konstantinos Margetis  11   12 , Mark R N Kotter  1   13 , Benjamin M Davies  1   13   14
Affiliations

Targeting earlier diagnosis: What symptoms come first in Degenerative Cervical Myelopathy?

Colin F Munro et al. PLoS One. .

Abstract

Background: Degenerative cervical myelopathy (DCM) is a common and disabling condition. Early effective treatment is limited by late diagnosis. Conventional descriptions of DCM focus on motor and sensory limb disability, however, recent work suggests the true impact is much broader. This study aimed to characterise the symptomatic presentation of DCM from the perspective of people with DCM and determine whether any of the reported symptoms, or groups of symptoms, were associated with early diagnosis.

Methods: An internet survey was developed, using an established list of patient-reported effects. Participants (N = 171) were recruited from an online community of people with DCM. Respondents selected their current symptoms and primary presenting symptom. The relationship of symptoms and their relationship to time to diagnosis were explored. This included symptoms not commonly measured today, termed 'non-conventional' symptoms.

Results: All listed symptoms were experienced by >10% of respondents, with poor balance being the most commonly reported (84.2%). Non-conventional symptoms accounted for 39.7% of symptomatic burden. 55.4% of the symptoms were reported as an initial symptom, with neck pain the most common (13.5%). Non-conventional symptoms accounted for 11.1% of initial symptoms. 79.5% of the respondents were diagnosed late (>6 months). Heavy legs was the only initial symptom associated with early diagnosis.

Conclusions: A comprehensive description of the self-reported effects of DCM has been established, including the prevalence of symptoms at disease presentation. The experience of DCM is broader than suggested by conventional descriptions and further exploration of non-conventional symptoms may support earlier diagnosis.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: CFM has declared that no competing interests exist. RY has declared that no competing interests exist. ZCM has declared that no competing interests exist. MGF currently serves as an academic editor at PLOS ONE. RRP has declared that no competing interests exist. JM has declared that no competing interests exist. KM has declared that no competing interests exist. MRNK has declared that no competing interests exist. BMD is supported by NIHR POLYFIX DCM and NIHR Clinical Doctoral Research Fellowship grants. BMD is a founder of MoveMed (a digital therapeutics platform which develops assessments and treatments using software). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Flowchart of the study design.
The findings from previous semi-structured (Round 1) interviews [20] and paired internet survey (Round 2) [16], were used to produce this survey (Round 3).
Fig 2
Fig 2. Bar chart of the prevalence of symptoms.
The number of respondents reporting each symptom are displayed at the end of the bar.
Fig 3
Fig 3. Pie chart illustrating the proportion of overall symptoms reported that were attributable to each symptom category.
Conventional symptom segments are illustrated with lines and non-conventional symptom segments with dots.
Fig 4
Fig 4. Bar chart of the prevalence of initial symptoms.
The number of respondents reporting each initial symptom are displayed at the end of the bar.
Fig 5
Fig 5. Pie chart illustrating the proportion of initial symptoms that were attributable to each symptom category.
Conventional symptom segments are illustrated with lines and non-conventional symptom segments with dots. Categories with N ≤ 1 have been left out for the purposes of clear illustration.
Fig 6
Fig 6. Odds of early diagnosis using individual symptoms.
A forest plot of individual odds ratios for each initial symptom. Error bars represent 95% confidence intervals of the odds ratio.
Fig 7
Fig 7. Odds of early diagnosis using symptom categories.
A forest plot of individual odds ratios for each symptom category. Error bars represent 95% confidence intervals of the odds ratio. Categories with N ≤ 1 have been left out for the purposes of clear illustration.
See this image and copyright information in PMC

References

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