nach oben

Quality of Life Research

Erschienen in:

01.11.2013

Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy

verfasst von: Qiuling Shi, Tito R. Mendoza, G. Brandon Gunn, Xin Shelley Wang, David I. Rosenthal, Charles S. Cleeland

Erschienen in: Quality of Life Research | Ausgabe 9/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Treatment-related symptom burden varies significantly among patients undergoing radiotherapy or chemoradiotherapy, yet such variation is typically not reflected in the results from single-group studies. We applied group-based trajectory modeling (GBTM) to describe the heterogeneity of symptom burden among patients with head and neck cancer and to identify subgroups with distinct symptom-development trajectories.

Methods

Patients (n = 130) were recruited pretherapy and rated multiple symptoms weekly for 10 weeks via the M. D. Anderson Symptom Inventory. With the mean of five most severe symptoms over time as an outcome measure, GBTM was used to identify patient subgroups with distinct symptom trajectories. Linear mixed-effects modeling (LMM) was applied to compare with GBTM’s ability to describe the longitudinal symptom data.

Results

The five most severe symptoms were problems with taste, difficulty swallowing or chewing, problems with mucus, fatigue, and dry mouth. A two-group GBTM model identified 68 % of patients as having high symptom burden, associated with older age, worse baseline performance status, and chemoradiotherapy treatment. A four-group GBTM model generated one stable group (4 % of patients) and three groups varying in symptom severity with both linear and quadratic functions over time. LMM revealed symptom-change patterns similar to that produced by GBTM but was inferior in identifying risk factors for high symptom burden.

Conclusions

For cancer patients undergoing aggressive therapy, GBTM is capable of identifying various symptom-burden trajectories and provides severity groupings that will aid research and may be of clinical utility. These results may be generalizable to other cancer types and treatments.

Vorheriger Artikel Pattern of impact of femoroacetabular impingement upon health-related quality of life: the determinant role of extra-articular factors

Nächster Artikel Satisfaction with oncology care among patients with advanced cancer and their caregivers

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Haigentz, M, Jr, Corry, J., Strojan, P., & Ferlito, A. (2012). Easing acceleration of head and neck chemoradiotherapy. Lancet Oncology, 13(2), 113–115.PubMedCrossRef

Rosenthal, D. I., Mendoza, T., & Cleeland, C. (2009). Identifying head and neck patients at risk for high symptom burden during treatment [abstract]. American society of clinical oncology 2009 annual meeting, Orlando, FL, May 29–Jun 2, 2009. Journal of Clinical Oncology, 27(15 Suppl). Abstract #6066.

Dodd, M. J., Cho, M. H., Cooper, B. A., & Miaskowski, C. (2010). The effect of symptom clusters on functional status and quality of life in women with breast cancer. European Journal of Oncology Nursing, 14(2), 101–110.PubMedCrossRef

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(5), E79–E89.PubMedCrossRef

Shi, Q., Smith, T. G., Michonski, J. D., Stein, K. D., Kaw, C., & Cleeland, C. S. (2011). Symptom burden in cancer survivors 1 year after diagnosis: a report from the American Cancer Society’s Studies of Cancer Survivors. Cancer, 117(12), 2779–2790.PubMedCrossRef

Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007). Subtypes, severity, and structural stability of peer victimization: what does latent class analysis say? Child Development, 78(6), 1706–1722.PubMedCrossRef

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(1), 33–42.PubMedCrossRef

Wang, X. S., Fairclough, D. L., Liao, Z., Komaki, R., Chang, J. Y., Mobley, G. M., et al. (2006). Longitudinal study of the relationship between chemoradiation therapy for non-small-cell lung cancer and patient symptoms. Journal of Clinical Oncology, 24(27), 4485–4491.PubMedCrossRef

Langford, D. J., Tripathy, D., Paul, S. M., West, C., Dodd, M. J., Schumacher, K., et al. (2011). Trajectories of pain and analgesics in oncology outpatients with metastatic bone pain. Journal of pain, 12(4), 495–507.PubMedCrossRef

10.

Merriman, J. D., Jansen, C., Koetters, T., West, C., Dodd, M., Lee, K., et al. (2010). Predictors of the trajectories of self-reported attentional fatigue in women with breast cancer undergoing radiation therapy. Oncology Nursing Society, 37(4), 423–432.

11.

Klepstad, P., Dale, O., Kaasa, S., Zahlsen, K., Aamo, T., Fayers, P., et al. (2003). Influences on serum concentrations of morphine, M6G and M3G during routine clinical drug monitoring: a prospective survey in 300 adult cancer patients. Acta Anaesthesiologica Scandinavica, 47(6), 725–731.PubMedCrossRef

12.

Nagin, D. S. (2005). Group-based modeling of development. Cambridge, MA: Harvard University Press.

13.

Nagin, D. S., & Tremblay, R. E. (2001). Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychological Methods, 6(1), 18–34.PubMedCrossRef

14.

Brame, B., Nagin, D. S., & Tremblay, R. E. (2001). Developmental trajectories of physical aggression from school entry to late adolescence. Journal of Child Psychology and Psychiatry, 42(4), 503–512.PubMedCrossRef

15.

Côté, S. M., Boivin, M., Liu, X., Nagin, D. S., Zoccolillo, M., & Tremblay, R. E. (2009). Depression and anxiety symptoms: onset, developmental course and risk factors during early childhood. Journal of Child Psychology and Psychiatry, 50(10), 1201–1208.PubMedCrossRef

16.

Modi, A. C., Cassedy, A. E., Quittner, A. L., Accurso, F., Sontag, M., Koenig, J. M., et al. (2010). Trajectories of adherence to airway clearance therapy for patients with cystic fibrosis. Journal of Pediatric Psychology, 35(9), 1028–1037.

17.

Cleeland, C. S., Mendoza, T. R., Wang, X. S., Woodruff, J. F., Palos, G. R., Richman, S. P., et al. (2011). Levels of symptom burden during chemotherapy for advanced lung cancer: differences between public hospitals and a tertiary cancer center. Journal of Clinical Oncology, 29(21), 2859–2865.PubMedCrossRef

18.

Rosenthal, D. I., Asper, J. A., Barker, J. L, Jr, Garden, A. S., Chao, K. S., Morrison, W. H., et al. (2006). Importance of patient examination to clinical quality assurance in head and neck radiation oncology. Head and Neck, 28(11), 967–973.PubMedCrossRef

19.

Cleeland, C. S., Mendoza, T. R., Wang, X. S., Chou, C., Harle, M. T., Morrissey, M., et al. (2000). Assessing symptom distress in cancer patients: the M.D. Anderson Symptom Inventory. Cancer, 89(7), 1634–1646.PubMedCrossRef

20.

Rosenthal, D. I., Mendoza, T. R., Chambers, M. S., Asper, J. A., Gning, I., Kies, M. S., et al. (2007). Measuring head and neck cancer symptom burden: the development and validation of the M. D. Anderson symptom inventory, head and neck module. Head and Neck, 29(10), 923–931.PubMedCrossRef

21.

Jones, B. L. (2005). TRAJ: group-based modeling of longitudinal data. http://www.andrew.cmu.edu/user/bjones/. Accessed 22 May 2012.

22.

Lo, Y. T., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.CrossRef

23.

Rosenthal, D. I., Mendoza, T. R., Chambers, M. S., Burkett, V. S., Garden, A. S., Hessell, A. C., et al. (2008). The M. D. Anderson symptom inventory-head and neck module, a patient-reported outcome instrument, accurately predicts the severity of radiation-induced mucositis. International Journal of Radiation Oncology Biology Physics, 72(5), 1355–1361.CrossRef

24.

Budach, W., Hehr, T., Budach, V., Belka, C., & Dietz, K. (2006). A meta-analysis of hyperfractionated and accelerated radiotherapy and combined chemotherapy and radiotherapy regimens in unresected locally advanced squamous cell carcinoma of the head and neck. BMC Cancer, 6, 28.PubMedCrossRef

25.

Muthén, B. (2003). Statistical and substantive checking in growth mixture modeling: Comment on Bauer and Curran (2003). Psychological Methods, 8(3), 369–377.PubMedCrossRef

26.

Muthén, B. (2004). Latent variable analysis: growth mixture modeling and related techeniques for longitudinal data. In D. Kaplan (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 345–368). Thousand Oaks, CA: Sage Publications, Inc.

27.

Tarpey, T., Yun, D., & Petkova, E. (2008). Model misspecification: finite mixture or homogeneous? Statistical Modelling, 8(2), 199–218.PubMedCrossRef

28.

Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8(3), 338–363.PubMedCrossRef

Titel: Using group-based trajectory modeling to examine heterogeneity of symptom burden in patients with head and neck cancer undergoing aggressive non-surgical therapy
verfasst von: Qiuling Shi
Tito R. Mendoza
G. Brandon Gunn
Xin Shelley Wang
David I. Rosenthal
Charles S. Cleeland
Publikationsdatum: 01.11.2013
Verlag: Springer Netherlands
Erschienen in: Quality of Life Research / Ausgabe 9/2013
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-013-0380-2

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 9/2013

Health status and (health-related) quality of life during the recovery of distal radius fractures: a systematic review

Psychometric evaluation of the CASP-19 quality of life scale in an older Irish cohort

Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale

Deriving clinically meaningful cut-scores for fatigue in a cohort of breast cancer survivors: a Health, Eating, Activity, and Lifestyle (HEAL) Study

Changing values, changing outcomes: the influence of reprioritization response shift on outcome assessment after spine surgery

Psychometric evaluation of the EORTC computerized adaptive test (CAT) fatigue item pool

Premium Partner