Modeling longitudinal data in acute illness
Biomarkers of sepsis could allow early identification of high-risk patients, in whom aggressive interventions can be life-saving. Among those interventions are the immunomodulatory therapies, which will hopefully become increasingly available to clinicians. However, optimal use of such interventions...
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| Format: | Online |
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BioMed Central
2007
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2206522/ |
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pubmed-22065222008-01-19 Modeling longitudinal data in acute illness Clermont, Gilles Commentary Biomarkers of sepsis could allow early identification of high-risk patients, in whom aggressive interventions can be life-saving. Among those interventions are the immunomodulatory therapies, which will hopefully become increasingly available to clinicians. However, optimal use of such interventions will probably be patient specific and based on longitudinal profiles of such biomarkers. Modeling techniques that allow proper interpretation and classification of these longitudinal profiles, as they relate to patient characteristics, disease progression, and therapeutic interventions, will prove essential to the development of such individualized interventions. Once validated, these models may also prove useful in the rational design of future clinical trials and in the interpretation of their results. However, only a minority of mathematicians and statisticians are familiar with these newer techniques, which have undergone remarkable development during the past two decades. Interestingly, critical illness has the potential to become a key testing ground and field of application for these emerging modeling techniques, given the increasing availability of point-of-care testing and the need for titrated interventions in this patient population. BioMed Central 2007 2007-08-02 /pmc/articles/PMC2206522/ /pubmed/17688677 http://dx.doi.org/10.1186/cc5968 Text en Copyright © 2007 BioMed Central Ltd |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Clermont, Gilles |
| spellingShingle |
Clermont, Gilles Modeling longitudinal data in acute illness |
| author_facet |
Clermont, Gilles |
| author_sort |
Clermont, Gilles |
| title |
Modeling longitudinal data in acute illness |
| title_short |
Modeling longitudinal data in acute illness |
| title_full |
Modeling longitudinal data in acute illness |
| title_fullStr |
Modeling longitudinal data in acute illness |
| title_full_unstemmed |
Modeling longitudinal data in acute illness |
| title_sort |
modeling longitudinal data in acute illness |
| description |
Biomarkers of sepsis could allow early identification of high-risk patients, in whom aggressive interventions can be life-saving. Among those interventions are the immunomodulatory therapies, which will hopefully become increasingly available to clinicians. However, optimal use of such interventions will probably be patient specific and based on longitudinal profiles of such biomarkers. Modeling techniques that allow proper interpretation and classification of these longitudinal profiles, as they relate to patient characteristics, disease progression, and therapeutic interventions, will prove essential to the development of such individualized interventions. Once validated, these models may also prove useful in the rational design of future clinical trials and in the interpretation of their results. However, only a minority of mathematicians and statisticians are familiar with these newer techniques, which have undergone remarkable development during the past two decades. Interestingly, critical illness has the potential to become a key testing ground and field of application for these emerging modeling techniques, given the increasing availability of point-of-care testing and the need for titrated interventions in this patient population. |
| publisher |
BioMed Central |
| publishDate |
2007 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2206522/ |
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1611433691571027968 |