Poster

Column

A Data-Driven Approach At Characterizing Heterogeneity In Neuropathy Assessments

Luke W. Johnston1,2, Signe Toft Andersen1, Morten Charles1, Marit E. Jørgensen3,4, Troels S. Jensen1,5, Lasse Bjerg1,2,3, Daniel R. Witte1,2
1 Aarhus University, Denmark; 2 Danish Diabetes Academy, Denmark; 3 Steno Diabetes Center Copenhagen, Denmark; 4 University of Southern Denmark, Denmark; 5 Aarhus University Hospital, Denmark

  • Aims: Identify potential clusters/groupings within neuropathy assessment items and responses that may characterize neuropathy in those with type 2 diabetes, using data-driven methods.
  • Study sample: 10-year exam of ADDITION-DK.
  • Assessment: Diabetic neuropathy assessed by several clinical scoring systems (TCSS, mTCSS, UENS, MNSI, DN4), Sural nerve conduction studies, and heart rate variability measurements.
  • Analysis: Cross-sectional. Hierarchical clustering to find clusters between individuals’ responses. Factor analysis of mixed data to find groupings in assessment items. Used data on complete cases of all nerve assessments (n=183).
  • Results: For 3 fixed clusters (Fig. A): 1 was dominated by “no”, “pass”, or “decreased” responses; 2 was dominated by responses for reduced sensation and greater pain in feet; 3 was dominated by responses for reduced touch, temperature, and vibration sensation and more pain up to the ankle. Factor analysis of first 3 components (11.1%, 6.5%, 5.3% mean explained variances, respectively) showed top contributor assessment items grouped into touch sensation, feeling of pain in feet, and ankle reflexes (Fig. B).

Column

Individuals can be classified into clusters by their specific responses to the neuropathy assessments and only a few assessment items may be needed to identify neuropathy cases. Using data-driven clustering and feature extraction algorithms may help arrive at a consensus on assessing neuropathy.

Individuals can be classified into clusters by their specific responses to the neuropathy assessments and only a few assessment items may be needed to identify neuropathy cases. Using data-driven clustering and feature extraction algorithms may help arrive at a consensus on assessing neuropathy.

Appendix

Column

Additional details on methods

  • Study design: We performed a cross-sectional analysis on those from the 10-year clinical examination of ADDITION-Denmark after a screening-based diagnosis of type 2 diabetes.
  • Sequence of steps in analysis:
    • Data was split into two sets, a training set (85%) and a testing set (15%) for later cross-validation (CV) of the findings.
    • Hierarchical cluster analysis (HCA) was used cluster of individuals based on their responses to the neuropathy assessments.
    • Factor analysis of mixed data (FAMD) was used to determine the contribution of each assessment item to the underlying variance in the data.
    • The training data set was split into 10-fold CV sets, and this was repeated 20 times, for a total of 200 randomly sampled sets of data.
    • HCA and FAMD analyses were applied separately to each individual sampled set.
    • For each HCA computation, the individual’s cluster number was extracted. For each FAMD computation, the assessment item’s contribution to the components (as well as the explained variance) was extracted.
    • Results from all 200 analyses were combined together, providing for a distribution of possible values of the findings.
    • From the results, the most frequent/common assessment responses (for each HCA cluster) and items (for each FAMD component) were extracted.
    • Full analytic steps can be found in the code of the project repository, specifically in the distance-hclust.R, famd.R, and plan.R files.

Likelihood of a participant being assigned to a given cluster number derived from the responses to all measured neuropathy clinical assessments

Column

(A) Explained variance of each FAMD component and (B) the frequency of a specific neuropathy assessment item being a top contributor to the FAMD component