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A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report

Hafen, GM, Hurst, C, Yearwood, J, Smith, J, Dzalilov, Z and Robinson, PJ 2008, A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report, BMC medical informatics and decision making, vol. 8, pp. 1-11, doi: 10.1186/1472-6947-8-44.

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Title A new scoring system in Cystic Fibrosis: statistical tools for database analysis - a preliminary report
Author(s) Hafen, GM
Hurst, C
Yearwood, JORCID iD for Yearwood, J orcid.org/0000-0002-7562-6767
Smith, J
Dzalilov, Z
Robinson, PJ
Journal name BMC medical informatics and decision making
Volume number 8
Article ID 44
Start page 1
End page 11
Total pages 11
Publisher BioMed Central
Place of publication England
Publication date 2008
ISSN 1472-6947
Keyword(s) Child
Cystic Fibrosis
Data Interpretation, Statistical
Databases, Factual
Female
Humans
Male
Severity of Illness Index
Summary Background
Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system.

Methods
The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets.

Results
(1) Feature selection: CAP has a more effective "modelling" focus than DA.

(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males.
Conclusion

Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.
Language eng
DOI 10.1186/1472-6947-8-44
Field of Research 0806 Information Systems
1103 Clinical Sciences
0909 Geomatic Engineering
HERDC Research category CN.1 Other journal article
Copyright notice ©2008, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30101492

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.