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Randomized Controlled Trial
. 2012 Jul;29(7):937-44.
doi: 10.1111/j.1464-5491.2012.03644.x.

Use of the Michigan Neuropathy Screening Instrument as a measure of distal symmetrical peripheral neuropathy in Type 1 diabetes: results from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications

W H Herman  1 , R Pop-Busui, B H Braffett, C L Martin, P A Cleary, J W Albers, E L Feldman; DCCT/EDIC Research Group
Affiliations
Randomized Controlled Trial

Use of the Michigan Neuropathy Screening Instrument as a measure of distal symmetrical peripheral neuropathy in Type 1 diabetes: results from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications

W H Herman et al. Diabet Med. 2012 Jul.

Erratum in

  • Corrigendum.
    [No authors listed] [No authors listed] Diabet Med. 2022 Apr;39(4):e14765. doi: 10.1111/dme.14765. Epub 2022 Feb 20. Diabet Med. 2022. PMID: 35184321 No abstract available.

Abstract

Aims: The Michigan Neuropathy Screening Instrument (MNSI) is used to assess distal symmetrical peripheral neuropathy in diabetes. It includes two separate assessments: a 15-item self-administered questionnaire and a lower extremity examination that includes inspection and assessment of vibratory sensation and ankle reflexes. The purpose of this study was to evaluate the performance of the MNSI in detecting distal symmetrical peripheral neuropathy in patients with Type 1 diabetes and to develop new scoring algorithms.

Methods: The MNSI was performed by trained personnel at each of the 28 Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications clinical sites. Neurologic examinations and nerve conduction studies were performed during the same year. Confirmed clinical neuropathy was defined by symptoms and signs of distal symmetrical peripheral neuropathy based on the examination of a neurologist and abnormal nerve conduction findings in ≥ 2 anatomically distinct nerves among the sural, peroneal and median nerves.

Results: We studied 1184 subjects with Type 1 diabetes. Mean age was 47 years and duration of diabetes was 26 years. Thirty per cent of participants had confirmed clinical neuropathy, 18% had ≥ 4 and 5% had ≥ 7 abnormal responses on the MNSI questionnaire, and 33% had abnormal scores (≥ 2.5) on the MNSI examination. New scoring algorithms were developed and cut points defined to improve the performance of the MNSI questionnaire, examination and the combination of the two.

Conclusions: Altering the cut point to define an abnormal test from ≥ 7 abnormal to ≥ 4 abnormal items improves the performance of the MNSI questionnaire. The MNSI is a simple, non-invasive and valid measure of distal symmetrical peripheral neuropathy in Type 1 diabetes.

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Figures

Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
Figure 1
Figure 1
The performance of the Michigan Neuropathy Screening Instrument (MNSI) components for prediction of confirmed clinical neuropathy: (a) the MNSI questionnaire using the clinical scoring algorithm; (b) the MNSI examination using the clinical scoring algorithm; (c) the MNSI questionnaire index; (d) the MNSI examination index; (e) the MNSI combined questionnaire and examination index; (f) the MNSI questionnaire index; (g) the MNSI examination index; (h) the MNSI combined questionnaire and examination index. Graphs (c) to (h) identify the optimal cut-offs from Table 2 such that the proportion correctly classified is maximized. Additionally, cut-offs with approximately 80% sensitivity and 80% specificity are labelled.
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References

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