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Open Access Research article

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

GM Hafen15, C Hurst24, J Yearwood24, J Smith1, Z Dzalilov2 and PJ Robinson13*

Author Affiliations

1 Department of Respiratory Medicine, Royal Children's Hospital Melbourne, Parkville, Victoria, Australia

2 School of Information Technology and Mathematical Science, University of Ballarat, Ballarat, Victoria, Australia

3 Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia

4 Centre for Informatics and Applied Optimization, University of Ballarat, Ballarat, Victoria, Australia

5 Respiratory Unit, Department of Pediatrics, University Hospital CHUV, Lausanne, Switzerland

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BMC Medical Informatics and Decision Making 2008, 8:44  doi:10.1186/1472-6947-8-44

Published: 5 October 2008

Abstract

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.