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

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.

Keywords

co-morbidity data curating decision trees guidelines lung cancer

References

Scottish Executive Health Department. Cancer scenarios: an aid to planning cancer services in Scotland in the next decade . Edinburgh: Scottish Executive , 2001.

Coleman MP , et al. Trends and socioeconomic inequalities in cancer survival in England and Wales up to 2001. Br J Cancer 2004; 90: 1367-1373.

Sant M. , et al. EUROCARE-3 survival of cancer patients diagnosed 1990-94: results and commentary. Ann Oncol 2003; 14 suppl 5: 61-118.

Jack RH , et al. Geographic inequalities in lung cancer management and survival in South East England: evidence of variation in access to oncology services . Br J Cancer 2003; 88: 1025-1031.

NHSs Executive. National performance indicators for the NHS. London, 2000 .

Mitchell T. Machine learning. McGraw Hill, 1997.

Scottish Intercollegiate Guidelines Network (SIGN). Management of patients with lung cancer 2005. http://www.sign.ac.uk/pdf/sign80.pdf (accessed April 2009).

Ten Teije A , Miksch S and Lucas P (eds). Computer-based medical guidelines and protocols: a primer and current trends. Amsterdam: IOS Press, 2008.

Shahar Y. , Miksch S. and Johnson P. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artific Intell Med 1998; 14 (1-2): 29-51.

10.

Peleg M. , Boxwala AA , Ogunyemi O. , et al. Glif3: the evolution of a guideline representation format. In: Proceedings of the American Medical Informatics Association Annual Symposium 2000, 2000, p.645-649.

11.

Fox J. , Johns N. , Rahmanzadeh A. , Thomson R. Disseminating medical knowledge: the PROforma approach. Artific Intell Med 1998; 14 (1-2): 157-182.

12.

http://cossac.org/groups/oxford/teaching/cogsys/tallis/ (accessed 25 March 2010).

13.

Abidi SR and Abidi SSR. Towards the merging of multiple clinical protocols and guidelines via ontology-driven modeling. In: 12th International Conference on Artificial Intelligence in Medicine Proceedings, 2009, Springer, p.81-85.

14.

Bottrighi A. , Giordano L. , Molino G. , Montani S. , Terenziani P. , Torchio M. Adopting model checking techniques for clinical guidelines verification. Artific Intell Med 2010; 48 (1): 1-19.

15.

Shiffman R. Representation of clinical practice guidelines in conventional and augmented decision tables. J Am Med Inform Assoc 1997 ; 4: 382-393.

16.

Duftschmid G. and Miksch S. Knowledge-based verification of clinical guidelines by detection of anomalies. Artific Intell Med 2001; 22 (1): 23-41.

17.

Bottrighi A. , Chesani F. , Mello P. , et al. A hybrid approach to clinical guideline and to basic medical knowledge conformance. In: 12th International Conference on Artificial Intelligence in Medicine Proceedings, 2009, Springer, p.91-95.

18.

Groot P. , Hommersom A. , Lucas P. , Serban R. , ten Teije A. , van Harmelen F. The role of model checking in critiquing based on clinical guidelines . In Bellazzi R, Abu-Hanna A, Hunter J (eds), Proceedings of the AIME 2007 Conference, Springer , 2007, p.411-420.

19.

Advani A. , Kinkoi L. , Kinkoi L. M, Shahar Y.

Intention-based critiquing of guideline-oriented medical care: the Asgaard Project at

Stanford. In: Proceedings AMIA Annual Symposium, 1998, p.483-487.

20.

Berka P. , Rauch J. and Zighed D. Data mining and medical knowledge management: cases and applications . Medical Information Science Reference, 2009.

21.

Sleeman D. , Fluck N. , Gyftodimos E. , Moss L. , Christie G. An intelligent aide for interpreting a patient’s dialysis dataset . In Bellazzi R, Abu-Hanna A, Hunter J (eds), Proceedings of the AIME 2007 Conference, Springer, 2007, p.57-66.

22.

McQuatt A. , Andrews PJD , Sleeman D. , Corruble V. , Jones PA The analyses of head injury data using decision tree techniques. In Horn W, et al. (eds). Artificial intelligence in medicine: Proceedings of AIMDM’99 Conference , Aalborg, June 1999, Springer, p.336-345.

23.

Erkurt E. , Tunali C. and Erkisi M. Primary therapeutic decision-making in inoperable non-small cell lung cancer . Int J Rad Oncology 2000; 46: 439-444.

24.

Wigren T. , and Kolari P. Evaluation of a decision-support system for inoperable non-small cell lung cancer. Methods Inform Med 1994; 33: 397-401.

25.

Sanders GD , Nease RF Jr and Owens DK Design and pilot evaluation of a system to develop computer-based site-specific practice guidelines from decision models. Med Decision Making 2000; 20: 145-159.

26.

WHO/ECOG Performance status. www.ecog.org/general/perf_stat.html 2006 (accessed June 2009).

27.

Mountain CF A new international staging system for lung cancer. Chest 1986; 89 (suppl 4): 225S-233S.

28.

Witten IH and Frank E. Data mining: practical machine learning tools and techniques. Morgan Kaufmann, 2005.

29.

Moss L. , Sleeman D. , Kinsella J. , Sim M. ACHE: an architecture for clinical hypothesis examination. In: Proceedings of 21st IEEE International Symposium on Computer-Based Medical Systems (CBMS 2008), Jyvaskyla, Finland, p.158-160.

30.

Sleeman D. , Aiken A. , Moss L. , Kinsella J. , Sim M. A system to detect inconsistencies between a domain expert’s different perspectives on (classification) tasks. In: Kacprzyk J (ed.) Advances in Machine Learning II: Studies in Computational Intelligence Springer, 2009, p.293-314.