Temporal clustering for blood glucose analysis in the ICU: identification of groups of patients with different risk profile.
Blood Glucose (BG) analysis and control in critically ill patients became an important research challenge in the last few years. Despite the big improvements that have been achieved both in research and in clinical practice, there are still many aspects that need to be elucidated. A first step towards a better comprehension of the phenomena underlying BG dynamics is represented by the study of retrospectively collected data. In this paper we propose an analysis of blood glucose time series through a combined temporal clustering and standard statistical analysis approach. The ultimate goal of the analysis is the identification of groups of patients showing different BG dynamics and evaluate their risk profiles, which is a very important issue in the Intensive Care Units. The method is applied to a set of patients treated at the Mediterranean Institute for Transplantation and Advanced Specialized Therapies in Palermo, Italy. We show that it is possible to identify two groups based on the initial blood glucose trends, and that the two groups significantly differ in terms of their future BG behaviour.
Sacchi
Lucia
L
Computer Engineering Systems Science Department, University of Pavia, Pavia, Italy. lucia.sacchi@unipv.it
D'Ancona
Giuseppe
G
Bertuzzi
Federico
F
Bellazzi
Riccardo
R
D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D016000:Cluster Analysis; D016638:Critical Illness / Q000401:mortality; D006801:Humans; D007362:Intensive Care Units; D007558:Italy; D012307:Risk Factors
D001786:Blood Glucose