Systems biology as a conceptual framework for research in family medicine; use in predicting response to influenza vaccination

To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording,...

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Bibliographic Details
Published in:Primary health care research & development 2011-10, Vol.12 (4), p.310-321
Main Authors: Majnarić-Trtica, Ljiljana, Vitale, Branko
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Algorithms
Concept Formation
Confidence Intervals
Decision making
Family Practice - methods
Female
Health Services Research - methods
Humans
Influenza
Influenza Vaccines
Influenza virus
Influenza, Human - prevention & control
Logistic Models
Male
Medical research
Middle Aged
Odds Ratio
Patient Satisfaction
Preventive Medicine - methods
Studies
Systems Biology - methods
Treatment Outcome
Vaccines
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Summary:To introduce systems biology as a conceptual framework for research in family medicine, based on empirical data from a case study on the prediction of influenza vaccination outcomes. This concept is primarily oriented towards planning preventive interventions and includes systematic data recording, a multi-step research protocol and predictive modelling. Factors known to affect responses to influenza vaccination include older age, past exposure to influenza viruses, and chronic diseases; however, constructing useful prediction models remains a challenge, because of the need to identify health parameters that are appropriate for general use in modelling patients' responses. The sample consisted of 93 patients aged 50-89 years (median 69), with multiple medical conditions, who were vaccinated against influenza. Literature searches identified potentially predictive health-related parameters, including age, gender, diagnoses of the main chronic ageing diseases, anthropometric measures, and haematological and biochemical tests. By applying data mining algorithms, patterns were identified in the data set. Candidate health parameters, selected in this way, were then combined with information on past influenza virus exposure to build the prediction model using logistic regression. A highly significant prediction model was obtained, indicating that by using a systems biology approach it is possible to answer unresolved complex medical uncertainties. Adopting this systems biology approach can be expected to be useful in identifying the most appropriate target groups for other preventive programmes.
ISSN:1463-4236
1477-1128
DOI:10.1017/S1463423611000089