A comparison between clinical decisions made about lung cancer patients and those inherent in the corresponding Scottish Intercollegiate Guidelines Network (SIGN) guideline

Derek Sleeman, Laura Elizabeth Moss, Elias Gyftodimos, Marianne Nicolson, Graham Devereux

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Treatment and survival for patients with lung cancer vary between and within countries. We have undertaken a multifaceted study of a clinical dataset of 635 patients, to see if clinician treatment decisions were being made consistently and in accordance with the appropriate Scottish Intercollegiate Guidelines Network (SIGN) document. Subsequently, we created a dataset of 117 patients who should have undergone surgery according to the SIGN guideline. As analyses of this dataset did not provide clear distinctions between the main treatment groups, a clinician reviewed the case notes and dataset, checking for inconsistencies. The revised dataset was processed by a decision tree algorithm which suggests clinically plausible decisions. Further, statistical analyses compared the 54 patients offered surgery with the 52 who were not. These analyses suggest that there are significant differences: the most discriminating feature is significant co-morbidity (p < 0.001). The article concludes with suggestions for how future guidelines might be enhanced.

Original languageEnglish
Pages (from-to)260-273
Number of pages14
JournalHealth Informatics Journal
Volume16
Issue number4
DOIs
Publication statusPublished - Dec 2010

Keywords

  • co-morbidity
  • data curating
  • decision trees
  • guidelines
  • lung cancer
  • I

Cite this

A comparison between clinical decisions made about lung cancer patients and those inherent in the corresponding Scottish Intercollegiate Guidelines Network (SIGN) guideline. / Sleeman, Derek; Moss, Laura Elizabeth; Gyftodimos, Elias; Nicolson, Marianne; Devereux, Graham.

In: Health Informatics Journal, Vol. 16, No. 4, 12.2010, p. 260-273.

Research output: Contribution to journalArticle

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