BMC Neurology (May 2023)

Development and validation of the scale for symptom clusters in patients with myasthenia gravis

  • Fan Shen,
  • Lu-Hong Hu,
  • Hai-Shan Huang,
  • Ling Li

DOI
https://doi.org/10.1186/s12883-023-03240-4
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 8

Abstract

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Abstract Background Patients with myasthenia gravis(MG)often experience multiple symptoms concurrently, which can have an adverse effect on their quality of life(QOL). However, a specific, systemic and reliable scale for symptom clusters in MG is lacking. Aims To develop reliable assessment scale for symptom clusters in patients with MG. Design A cross-sectional descriptive study. Methods Based on the unpleasant symptom theory(TOUS), the first draft of the scale was developed through review literature, qualitative interview, and Delphi expert correspondence, the items of the scale were presented and adjusted through cognitive interviews with 12 patients. To conveniently assess the validity and reliability of the scale, a cross-sectional survey was conducted in 283 patients with MG who were recruited from Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, from June to September 2021. Results The final symptom cluster scale for patients with MG consisted of 19 items(MGSC-19), with a content validity index ranging from 0.828 to 1.000 for each item and the content validity index was 0.980. Four common variables (ocular muscle weakness, general muscular weakness, treatment-related side effects, and psychiatric problems) were identified by exploratory factor analysis, which explained 70.187% of the total variance. The correlation coefficients between the scale dimension and the overall score ranged from 0.395 to 0.769 (all P < 0.01), while the correlation coefficients between dimensions varied from 0.324 to 0.510 (all P < 0.01). The Cronbach’s alpha, retest reliability, and half reliability were 0.932, 0.845, and 0.837, respectively. Conclusion The validity and reliability of MGSC-19 were generally good. This scale can be employed to identify the symptom clusters to help healthcare givers develop individualized symptom management measures for patients with MG.

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