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Gepubliceerd in:

08-12-2017

Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer

Auteurs: Pierre Michel, Zeinab Hamidou, Karine Baumstarck, Badih Ghattas, Noémie Resseguier, Olivier Chinot, Fabrice Barlesi, Sébastien Salas, Laurent Boyer, Pascal Auquier

Gepubliceerd in: Quality of Life Research | Uitgave 2/2018

Abstract

Background

Studies have suggested that clinicians do not feel comfortable with the interpretation of symptom severity, functional status, and quality of life (QoL). Implementation strategies of these types of measurements in clinical practice imply that consensual norms and guidelines regarding data interpretation are available. The aim of this study was to define subgroups of patients according to the levels of symptom severity using a method of interpretable clustering that uses unsupervised binary trees.

Methods

The patients were classified using a top-down hierarchical method: Clustering using Unsupervised Binary Trees (CUBT). We considered a three-group structure: “high”, “moderate”, and “low” level of symptom severity. The clustering tree was based on three stages using the 9-symptom scale scores of the EORTC QLQ-C30: a maximal tree was first developed by applying a recursive partitioning algorithm; the tree was then pruned using a criterion of minimal dissimilarity; finally, the most similar clusters were joined together. Inter-cluster comparisons were performed to test the sample partition and QoL data.

Results

Two hundred thirty-five patients with different types of cancer were included. The three-cluster structure classified 143 patients with “low”, 46 with “moderate”, and 46 with “high” levels of symptom severity. This partition was explained by cut-off values on Fatigue and Appetite Loss scores. The three clusters consistently differentiated patients based on the clinical characteristics and QoL outcomes.

Conclusion

Our study suggests that CUBT is relevant to define the levels of symptom severity in cancer. This finding may have important implications for helping clinicians to interpret symptom profiles in clinical practice, to identify individuals at risk for poorer outcomes and implement targeted interventions.

vorige artikel A preliminary prediction model for potentially guiding patient choices between breast conserving surgery and mastectomy in early breast cancer patients; a Dutch experience

volgende artikel Involvement in care in pediatric cancer patients: implications for treatment compliance, mental health and health-related quality of life

Alleen toegankelijk voor geautoriseerde gebruikers

Miaskowski, C., Aouizerat, B. E., Dodd, M., & Cooper, B. Conceptual issues in symptom clusters research and their implications for quality-of-life assessment in patients with cancer. Journal of the National Cancer Institute Monographs 2007:39–46. https://doi.org/10.1093/jncimonographs/lgm003.

Fan, G., Filipczak, L., & Chow, E. (2007). Symptom clusters in cancer patients: A review of the literature. Current Oncology, 14, 173–179.CrossRefPubMedPubMedCentral

Chen, M.-L., & Tseng, H.-C. (2006). Symptom clusters in cancer patients. Supportive Care in Cancer, 14, 825–830. https://doi.org/10.1007/s00520-006-0019-8.CrossRefPubMed

Miaskowski, C., Cooper, B. A., Paul, S. M., Dodd, M., Lee, K., Aouizerat, B. E., et al. (2006). Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: A cluster analysis. Oncology Nursing Forum, 33, E79–E89. https://doi.org/10.1188/06.ONF.E79-E89.CrossRefPubMed

Ferreira, K. A. S. L., Kimura, M., Teixeira, M. J., Mendoza, T. R., da Nóbrega, J. C. M., Graziani, S. R., et al. (2008). Impact of cancer-related symptom synergisms on health-related quality of life and performance status. Journal of Pain and Symptom Management, 35, 604–616. https://doi.org/10.1016/j.jpainsymman.2007.07.010.CrossRefPubMed

Gwede, C. K., Small, B. J., Munster, P. N., Andrykowski, M. A., & Jacobsen, P. B. (2008). Exploring the differential experience of breast cancer treatment-related symptoms: A cluster analytic approach. Supportive Care in Cancer, 16, 925–933. https://doi.org/10.1007/s00520-007-0364-2.CrossRefPubMed

Pud, D., Ben Ami, S., Cooper, B. A., Aouizerat, B. E., Cohen, D., Radiano, R., et al. (2008). The symptom experience of oncology outpatients has a different impact on quality-of-life outcomes. Journal of Pain and Symptom Management, 35, 162–170. https://doi.org/10.1016/j.jpainsymman.2007.03.010.CrossRefPubMed

Illi, J., Miaskowski, C., Cooper, B., Levine, J. D., Dunn, L., West, C., et al. (2012). Association between pro- and anti-inflammatory cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression. Cytokine, 58, 437–447. https://doi.org/10.1016/j.cyto.2012.02.015.CrossRefPubMedPubMedCentral

Reese, J. B., Blackford, A., Sussman, J., Okuyama, T., Akechi, T., Bainbridge, D., et al. (2015). Cancer patients’ function, symptoms and supportive care needs: A latent class analysis across cultures. Quality of Life Research, 24, 135–146. https://doi.org/10.1007/s11136-014-0629-4.CrossRefPubMed

10.

Snyder, C. F., Garrett-Mayer, E., Blackford, A. L., Brahmer, J. R., Carducci, M. A., Pili, R., et al. (2009). Concordance of cancer patients’ function, symptoms, and supportive care needs. Quality of Life Research, 18, 991–998.CrossRefPubMed

11.

Miaskowski, C., Cooper, B. A., Melisko, M., Chen, L.-M., Mastick, J., West, C., et al. (2014). Disease and treatment characteristics do not predict symptom occurrence profiles in oncology outpatients receiving chemotherapy. Cancer, 120, 2371–2378. https://doi.org/10.1002/cncr.28699.CrossRefPubMedPubMedCentral

12.

Miaskowski, C., Dunn, L., Ritchie, C., Paul, S. M., Cooper, B., Aouizerat, B. E., et al. (2015). Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics. Journal of Pain and Symptom Management, 50, 28–37. https://doi.org/10.1016/j.jpainsymman.2014.12.011.CrossRefPubMedPubMedCentral

13.

Langford, D. J., Paul, S. M., Cooper, B., Kober, K. M., Mastick, J., Melisko, M., et al. (2016). Comparison of subgroups of breast cancer patients on pain and co-occurring symptoms following chemotherapy. Supportive Care in Cancer, 24, 605–614. https://doi.org/10.1007/s00520-015-2819-1.CrossRefPubMed

14.

Doong, S.-H., Dhruva, A., Dunn, L. B., West, C., Paul, S. M., Cooper, B. A., et al. (2015). Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery. Biological Research for Nursing, 17, 237–247. https://doi.org/10.1177/1099800414550394.CrossRefPubMed

15.

Miaskowski, C., Cooper, B., Aouizerat, B., Melisko, M., Chen, L.-M., Dunn, L., et al. (2017). The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy. European Journal of Cancer Care. https://doi.org/10.1111/ecc.12437.PubMed

16.

Astrup, G. L., Hofsø, K., Bjordal, K., Guren, M. G., Vistad, I., Cooper, B., et al. (2017). Patient factors and quality of life outcomes differ among four subgroups of oncology patients based on symptom occurrence. Acta Oncologica, 56, 462–470. https://doi.org/10.1080/0284186X.2016.1273546.CrossRefPubMed

17.

Dodd, M. J., Cho, M. H., Cooper, B. A., Petersen, J., Bank, K. A., Lee, K. A., et al. (2011). Identification of latent classes in patients who are receiving biotherapy based on symptom experience and its effect on functional status and quality of life. Oncology Nursing Forum, 38, 33–42. https://doi.org/10.1188/11.ONF.33-42.CrossRefPubMed

18.

Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied latent class analysis. Cambridge: Cambridge University Press.CrossRef

19.

Fraiman, R., Ghattas, B., & Svarc, M. (2013). Interpretable clustering using unsupervised binary trees. Advances in Data Analysis and Classification, 7, 125–145. https://doi.org/10.1007/s11634-013-0129-3.CrossRef

20.

Michel, P., Baumstarck, K., Boyer, L., Fernandez, O., Flachenecker, P., Pelletier, J., et al. (2014). Defining quality of life levels to enhance clinical interpretation in multiple sclerosis: Application of a novel clustering method. Medical Care. https://doi.org/10.1097/MLR.0000000000000117.

21.

Michel, P., Baumstarck, K., Auquier, P., Loundou, A., Ghattas, B., Lançon, C., et al. (2015). How to interpret multidimensional quality of life questionnaires for patients with schizophrenia? Quality of Life Research. https://doi.org/10.1007/s11136-015-0982-y.

22.

Leplege, A., Ecosse, E., Pouchot, J., Coste, J., & Perneger, T. (2001). MOS SF36 questionnaire. Manual and guidelines for scores’ interpretation. Paris: Editions Estem.

23.

Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30, 473–483.CrossRefPubMed

24.

Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85, 365–376.CrossRefPubMed

25.

Carver, C. S. (1997). You want to measure coping but your protocol’s too long: Consider the brief COPE. International Journal of Behavioral Medicine, 4, 92–100. https://doi.org/10.1207/s15327558ijbm0401_6.CrossRefPubMed

26.

Muller, L., & Spitz, E. (2003). Multidimensional assessment of coping: Validation of the Brief COPE among French population. L’Encephale, 29, 507–518.PubMed

27.

Baumstarck, K., Alessandrini, M., Hamidou, Z., Auquier, P., Leroy, T., & Boyer, L. (2017) Assessment of coping: A new French four-factor structure of the brief COPE inventory. Health and Quality of Life Outcomes. https://doi.org/10.1186/s12955-016-0581-9.

28.

Halyard, M. Y., Frost, M. H., Dueck, A., & Sloan, J. A. (2006). Is the use of QOL data really any different than other medical testing? Current Problems in Cancer, 30, 261–271. https://doi.org/10.1016/j.currproblcancer.2006.08.004.CrossRefPubMed

29.

Halyard, M. Y., Frost, M. H., & Dueck, A. (2006). Integrating QOL assessments for clinical and research purposes. Current Problems in Cancer, 30, 319–330. https://doi.org/10.1016/j.currproblcancer.2006.08.009.CrossRefPubMed

30.

Luckett, T., Butow, P. N., & King, M. T. (2009). Improving patient outcomes through the routine use of patient-reported data in cancer clinics: Future directions. Psychooncology, 18, 1129–1138. https://doi.org/10.1002/pon.1545.CrossRefPubMed

31.

Hennig, C. (2015). Clustering strategy and method selection. ArXiv150302059 Stat 2015.

32.

Hennig, C. (2005). A method for visual cluster validation. In: P. D. C. Weihs & P. D. W. Gaul (Eds.) Classification—The ubiquitous challenge (pp. 153–160). Berlin: Springer. https://doi.org/10.1007/3-540-28084-7_15.

33.

Hennig, C., Meila, M., Murtagh, F., & Rocci, R. (Eds.). (2015). Handbook of cluster analysis (1st ed.). Boca Raton: Chapman and Hall/CRC.

34.

Holahan, C. J., & Moos, R. H. (1987). Personal and contextual determinants of coping strategies. Journal of Personality and Social Psychology, 52, 946–955.CrossRefPubMed

35.

Brown, G. K., & Nicassio, P. M. (1987). Development of a questionnaire for the assessment of active and passive coping strategies in chronic pain patients. Pain, 31, 53–64.CrossRefPubMed

36.

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer.

37.

Ghattas, B., Michel, P., & Boyer, L. (2017). Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methods. Pattern Recognition, 67, 177–185. https://doi.org/10.1016/j.patcog.2017.01.031.CrossRef

38.

Ascher-Svanum, H., Novick, D., Haro, J. M., Aguado, J., & Cui, Z. (2012). Empirically driven definitions of “good,” “moderate,” and “poor” levels of functioning in the treatment of schizophrenia. Quality of Life Research. https://doi.org/10.1007/s11136-012-0335-z.PubMedPubMedCentral

39.

Lipkovich, I. A., Deberdt, W., Csernansky, J. G., Buckley, P., Peuskens, J., Kollack-Walker, S., et al. (2009). Defining “good” and “poor” outcomes in patients with schizophrenia or schizoaffective disorder: A multidimensional data-driven approach. Psychiatry Research, 170, 161–167. https://doi.org/10.1016/j.psychres.2008.09.004.CrossRefPubMed

Titel: Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer
Auteurs: Pierre Michel
Zeinab Hamidou
Karine Baumstarck
Badih Ghattas
Noémie Resseguier
Olivier Chinot
Fabrice Barlesi
Sébastien Salas
Laurent Boyer
Pascal Auquier
Publicatiedatum: 08-12-2017
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 2/2018
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-017-1760-9

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Abstract

Background

Methods

Results

Conclusion

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