Literature DB >> 19057804

A subgroup discovery approach for scrutinizing blood glucose management guidelines by the identification of hyperglycemia determinants in ICU patients.

B Nannings1, R-J Bosman, A Abu-Hanna.   

Abstract

OBJECTIVE: Despite the wide use of blood glucose management guidelines in intensive care (IC), hyperglycemia is still common. The aim of this study was the discovery of possible hyperglycemia determinants by applying the Patient Rule Induction Method (PRIM) to routinely collected data within the first 24 hours of admission, and to relate them to the literature.
METHODS: PRIM was applied in two set-ups to data of 2001 IC patients including 50,021 records of blood glucose levels and other variables. The independent predictors of blood glucose measurements were variables whose value is known before the time of the corresponding measurement. Subgroups were validated using a random split design, and time-sensitivity of performance was analyzed.
RESULTS: PRIM was able to identify relatively large subgroups having markedly high mean glucose values. PRIM also discovered possible determinants of which less is known about their relationship to hyperglycemia. Some possible determinants reported in the literature were not found by PRIM.
CONCLUSIONS: We demonstrated for the first time the utility of using subgroup discovery to uncover possible determinants for non-responsiveness to treatment. This implies the possible use of this technology to scrutinize the effects of various guidelines in clinical medicine on patient outcomes without requiring the development of a global predictive model. We hypothesize that by focusing on the identified subgroups, clinical guidelines may be improved. Further research is required to test this hypothesis.

Entities:  

Mesh:

Substances:

Year:  2008        PMID: 19057804     DOI: 10.3414/me0531

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  3 in total

1.  Patient subgroup identification for clinical drug development.

Authors:  Xin Huang; Yan Sun; Paul Trow; Saptarshi Chatterjee; Arunava Chakravartty; Lu Tian; Viswanath Devanarayan
Journal:  Stat Med       Date:  2017-02-01       Impact factor: 2.373

2.  Data mining technologies for blood glucose and diabetes management.

Authors:  Riccardo Bellazzi; Ameen Abu-Hanna
Journal:  J Diabetes Sci Technol       Date:  2009-05-01

3.  Efficient identification of context dependent subgroups of risk from genome-wide association studies.

Authors:  Greg Dyson; Charles F Sing
Journal:  Stat Appl Genet Mol Biol       Date:  2014-04-01
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.