Usefulness of subclassification of adult diabetes mellitus among inpatients in Japan. Issue 4 (7th December 2021) - Saito, Kohei; Inoue, Tatsuhide; Ariyasu, Hiroyuki; Shimada, Toshio; Itoh, Hiroshi; Tanaka, Issei; Terao, Chikashi
Abstract: Aims/Introduction: We aimed to replicate a new diabetes subclassification based on objective clinical information at admission in a diabetes educational inpatient program. We also assessed the educational outcomes for each cluster. Methods: We included diabetes patients who participated in the educational inpatient program during 2009–2020 and had sufficient clinical information for the cluster analysis. We applied a data‐driven clustering method proposed in a previous study and further evaluated the clinical characteristics of each cluster. We investigated the association between the clusters and changes in hemoglobin A1c level from the start of the education program. We also assessed the risk of re‐admission for the educational program. Results: We divided a total of 651 patients into five clusters. Their clinical characteristics followed the same pattern as in previous studies. The intercluster ranking of the cluster center coordinates showed strong correlation coefficients with those of the previous studies (mean ρ = 0.88). Patients classified as severe insulin‐resistant diabetes (cluster 3) showed a more pronounced progression of renal dysfunction than patients classified as the other clusters. The patients classified as severe insulin‐deficient diabetes (cluster 2) had the highest rate of reduction in hemoglobin A1c level from the start of the program ( P  < 0.01) and a tendency toward a lower risk of re‐admission for the education program (hazard ratio 0.47, P  = 0.09). Conclusion: We successfully replicated the diabetes subclassification using objective clinical information at admission for the education program. In addition, we showed that severe insulin‐deficient diabetes patients tended to have better educational outcomes than patients classified as the other clusters. Abstract : We applied a novel data‐driven clustering method previously proposed to patients with diabetes in educational admission. As a result, we obtained clustering results quite similar to those in the original paper, indicating that clinical information at onset might not be essential for the clustering. Furthermore, we found a trend toward a greater educational effect in a specific cluster, suggesting clinical usefulness of the clustering.