Top

Tip

Swipe om te navigeren naar een ander artikel

Gepubliceerd in:

16-04-2021

The development and initial validation of the Breast Cancer Recurrence instrument (BreastCaRe)—a patient-reported outcome measure for detecting symptoms of recurrence after breast cancer

Auteurs: Beverley Lim Høeg, Lena Saltbæk, Karl Bang Christensen, Randi Valbjørn Karlsen, Christoffer Johansen, Susanne Oksbjerg Dalton, Antonia Bennett, Pernille Envold Bidstrup

Gepubliceerd in: Quality of Life Research | Uitgave 9/2021

Abstract

Purpose

Patient-reported outomes (PRO) may facilitate prompt treatment. We describe the development and psychometric properties of the first instrument to monitor for symptoms of breast cancer (BC) recurrence.

Methods

This study is nested in the MyHealth randomized trial of nurse-led follow-up based on electronically-collected PROs. We constructed items assessing symptoms of potential recurrence through expert interviews with six BC specialists in Denmark. Semi-structured cognitive interviews were carried out with a patient panel to assess acceptability and comprehensibility. Items were subsequently tested in a population of 1170 women 1–10 years after completing BC treatment. We carried out multiple-groups confirmatory factor analysis (CFA) and Rasch analysis to test dimensionality, local dependence (LD) and differential item functioning (DIF) according to sociodemographic and treatment-related factors. Clinical data was obtained from the Danish Breast Cancer Group registry.

Results

Twenty-two items were generated for the Breast Cancer Recurrence instrument (BreastCaRe). Cognitive testing resulted in clearer items. Seven subscales based on general, bone, liver, lung, brain, locoregional and contralateral recurrence symptoms were proposed. Both CFA and Rasch models confirmed the factor structure. No DIF was identified. Five item pairs showed LD but all items were retained to avoid loss of clinical information. Rasch models taking LD into account were used to generate a standardized scoring table for each subscale.

Conclusions

The BreastCaRe has good content and structural validity, patient acceptability and measurement invariance. We are preparing to examine the predictive validity of this new instrument.

vorige artikel A psychometric evaluation of the Functional assessment of cancer therapy—kidney symptom index (FKSI-19) among renal cell carcinoma patients suggesting an alternative two-factor structure

volgende artikel Psychometric properties of the Dutch-Flemish PROMIS® pediatric item banks Anxiety and Depressive Symptoms in a general population

Alleen toegankelijk voor geautoriseerde gebruikers

Yang, L. Y., Manhas, D. S., Howard, A. F., & Olson, R. A. (2018). Patient-reported outcome use in oncology: a systematic review of the impact on patient-clinician communication. Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer., 26(1), 41–60. https://doi.org/10.1007/s00520-017-3865-7. CrossRef

Basch, E. M., Deal, A. M., Dueck, A. C., Bennett, A. V., Atkinson, T. M., Scher, H. I., et al. (2017). Overall survival results of a randomized trial assessing patient-reported outcomes for symptom monitoring during routine cancer treatment. Journal of Clinical Oncology, 318(2), 197–198. https://doi.org/10.1200/JCO.2017.35.18_suppl.LBA2. CrossRef

Chen, J., Ou, L., & Hollis, S. J. (2013). A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Services Research, 13, 211. https://doi.org/10.1186/1472-6963-13-211. CrossRefPubMedPubMedCentral

Runowicz, C. D., Leach, C. R., Henry, N. L., Henry, K. S., Mackey, H. T., Cowens-Alvarado, R. L., et al. (2016). American Cancer Society/American Society of Clinical Oncology breast cancer survivorship care guideline. CA: A Cancer Journal for Clinicians, 66(1), 43–73. https://doi.org/10.3322/caac.21319. CrossRef

Howell, D., Molloy, S., Wilkinson, K., Green, E., Orchard, K., Wang, K., et al. (2015). Patient-reported outcomes in routine cancer clinical practice: a scoping review of use, impact on health outcomes, and implementation factors. Annals of Oncology, 26(9), 1846–1858. https://doi.org/10.1093/annonc/mdv181. CrossRefPubMed

Coulter A, Potter C, Peters M, Fitzpatrick R. Cancer PROMs: a scoping study: Macmillan Cancer Support2015.

WHO. Breast cancer burden. 2018. http://www.who.int/cancer/detection/breastcancer/en/index1.html. Accessed 08/08/2018.

Colleoni, M., Sun, Z., Price, K. N., Karlsson, P., Forbes, J. F., Thürlimann, B., et al. (2016). Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the international breast cancer study group trials I to V. Journal of Clinical Oncology, 34(9), 927–935. https://doi.org/10.1200/JCO.2015.62.3504. CrossRefPubMedPubMedCentral

Pan, H., Gray, R., Braybrooke, J., Davies, C., Taylor, C., McGale, P., et al. (2017). 20-year risks of breast-cancer recurrence after stopping endocrine therapy at 5 years. New England Journal of Medicine, 377(19), 1836–1846. https://doi.org/10.1056/NEJMoa1701830. CrossRef

10.

Ahmad, A. (2013). Pathways to breast cancer recurrence. ISRN Oncol, 2013, 16. https://doi.org/10.1155/2013/290568. CrossRef

11.

Wu, X., Baig, A., Kasymjanova, G., Kafi, K., Holcroft, C., Mekouar, H., et al. (2016). Pattern of local recurrence and distant metastasis in breast cancer by molecular subtype. Cureus., 8(12), e924. https://doi.org/10.7759/cureus.924. CrossRefPubMedPubMedCentral

12.

Taggart, F., Donnelly, P., & Dunn, J. (2012). Options for early breast cancer follow-up in primary and secondary care - a systematic review. BMC Cancer, 12(1), 238. https://doi.org/10.1186/1471-2407-12-238. CrossRefPubMedPubMedCentral

13.

Pivot, X., Asmar, L., Hortobagyi, G. N., Theriault, R., Pastorini, F., & Buzdar, A. (2000). A retrospective study of first indicators of breast cancer recurrence. Oncology, 58(3), 185–190. https://doi.org/10.1159/000012098. CrossRefPubMed

14.

Saltbæk, L., Horsboel, T. A., Offersen, B. V., Andersson, M., Friberg, A. S., Skriver, S. K., et al. (2020). Patterns in detection of recurrence among patients treated for breast cancer. Breast Cancer Research and Treatment. https://doi.org/10.1007/s10549-020-05847-4. CrossRefPubMed

15.

Denis, F., Lethrosne, C., Pourel, N., Molinier, O., Pointreau, Y., Domont, J., Bourgeois, H., Senellart, H., Trémolières, P., Lizée, T., Bennouna, J., Urban, T., El Khouri, C., Charron, A., Septans, A.-L., Balavoine, M., Landry, S., Solal-Céligny, P., & Letellier, C. (2017). Randomized trial comparing a web-mediated follow up with routine surveillance in lung cancer patients. Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/djx029. CrossRefPubMed

16.

Denis, F., Viger, L., Charron, A., Voog, E., Dupuis, O., Pointreau, Y., et al. (2014). Detection of lung cancer relapse using self-reported symptoms transmitted via an Internet Web-application: pilot study of the sentinel follow-up. Supportive Care in Cancer, 22(6), 1467–1473. https://doi.org/10.1007/s00520-013-2111-1. CrossRefPubMed

17.

Dima, A. L. (2018). Scale validation in applied health research: tutorial for a 6-step R-based psychometrics protocol. Health Psychology and Behavioral Medicine., 6(1), 136–161. https://doi.org/10.1080/21642850.2018.1472602. CrossRefPubMedPubMedCentral

18.

Mokkink, L. B., Terwee, C. B., Patrick, D. L., Alonso, J., Stratford, P. W., Knol, D. L., et al. (2010). The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation., 19(4), 539–549. https://doi.org/10.1007/s11136-010-9606-8. CrossRef

19.

ClinicalTrials.gov. MyHealth: Follow-up After Breast Cancer Treatment (MyHealth). 2016. https://clinicaltrials.gov/ct2/show/NCT02949167.

20.

Saltbæk, L., Karlsen, R. V., Bidstrup, P. E., Høeg, B. L., Zoffmann, V., Horsbøl, T. A., et al. (2019). MyHealth: specialist nurse-led follow-up in breast cancer. A randomized controlled trial – development and feasibility. Acta Oncologica. https://doi.org/10.1080/0284186X.2018.1563717. CrossRefPubMed

21.

Bast, R. C., Croce, C. M., Hait, W. N., Hong, W. K., Kufe, D. W., Piccart-Gebhart, M., et al. (Eds.). (2017). Cancer Medicine. (9th ed.). Wiley-Blackwell: Hoboken, NJ.

22.

Høeg, B. L., Tjørnhøj-Thomsen, T., Skaarup, J. A., Langstrup, H., Zoffmann, V., Saltbaek, L., et al. (2019). Whose perspective is it anyway? Dilemmas of patient involvement in the development of a randomized clinical trial – a qualitative study. Acta Oncologica, 58(5), 1–8. https://doi.org/10.1080/0284186X.2019.1566776. CrossRef

23.

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(5), 365–376. CrossRef

24.

Pedersen, C. B., Gøtzsche, H., Møller, J. Ø., & Mortensen, P. B. (2006). The Danish civil registration system. A cohort of eight million persons. Danish Medical Bulletin, 53, 441–449. PubMed

25.

Jensen, M. B., Laenkholm, A. V., Offersen, B. V., Christiansen, P., Kroman, N., Mouridsen, H. T., et al. (2018). The clinical database and implementation of treatment guidelines by the Danish Breast Cancer Cooperative Group in 2007–2016. Acta Oncologica, 57(1), 13–18. https://doi.org/10.1080/0284186x.2017.1404638. CrossRefPubMed

26.

Christensen, K. B., Makransky, G., & Horton, M. (2017). Critical values for Yen’s Q3: Identification of local dependence in the rasch model using residual correlations. Applied Psychological Measurement, 41(3), 178–194. https://doi.org/10.1177/0146621616677520. CrossRefPubMed

27.

Scott NW, Fayers PM, Aaronson PM, Bottomley A, de Graeff A, Groenvold A et al. EORTC QLQ-C30 Reference Values 2008.

28.

Rosseel Y. lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software; Vol 1, Issue 2 (2012). 2012.

29.

Svetina, D., Rutkowski, L., & Rutkowski, D. (2020). Multiple-group invariance with categorical outcomes using updated guidelines: An illustration using mplus and the lavaan/semtools packages. Structural Equation Modeling: A Multidisciplinary Journal., 27(1), 111–130. https://doi.org/10.1080/10705511.2019.1602776. CrossRef

30.

Braeken, J., & van Assen, M. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450–466. https://doi.org/10.1037/met0000074. CrossRefPubMed

31.

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: a review. The Journal of Educational Research., 99(6), 323–338. https://doi.org/10.3200/JOER.99.6.323-338. CrossRef

32.

Christensen KB, Kreiner S, Mesbah M, editors. Rasch Models in Health. Wiley; 2012.

33.

Kreiner, S., & Christensen, K. B. (2002). Graphical Rasch models. In M. Mesbah, B. F. Cole, & M. L. T. Lee (Eds.), Statistical Methods for Quality of Life Studies: Design, Measurements and Analysis. (pp. 187–203). Boston: Springer. CrossRef

34.

Smith, R. M., Schumacker, R. E., & Bush, M. J. (1998). Using item mean squares to evaluate fit to the Rasch model. Journal of Outcome Measurement, 2(1), 66–78. PubMed

35.

Smith, A. B., Rush, R., Fallowfield, L. J., Velikova, G., & Sharpe, M. (2008). Rasch fit statistics and sample size considerations for polytomous data. BMC Medical Research Methodology, 8, 33. https://doi.org/10.1186/1471-2288-8-33. CrossRefPubMedPubMedCentral

36.

Christensen K, Kreiner S. Item Fit Statistics. In: Christensen K, Kreiner S, Mesbah M, editors. Rasch Models in Health. 2013. p. 83–104.

37.

Kreiner, S., & Christensen, K. B. (2013). Two Tests of Local Independence. In K. B. Christensen, S. Kreiner, & M. Mesbah (Eds.), Rasch Models in Health Hoboken. (pp. 131–136). NJ: John Wiley & Sons. CrossRef

38.

Kreiner, S., & Christensen, K. B. (2011). Item screening in graphical loglinear rasch models. Psychometrika, 76(2), 228–256. https://doi.org/10.1007/s11336-011-9203-y. CrossRef

39.

Kreiner S, Christensen KB. Overall Tests of the Rasch Model. In: Kreiner S, Christensen KB, Mesbah M, editors. Rasch Models in Health. 2013. p. 105–10.

40.

Warm, T. A. (1989). Weighted likelihood estimation of ability in item response theory. Psychometrika, 54(3), 427–450. https://doi.org/10.1007/BF02294627. CrossRef

41.

Christensen, K. B. (2013). Conditional maximum likelihood estimation in polytomous rasch models using SAS. ISRN Computational Mathematics, 2013, 1–8. CrossRef

42.

Christensen, K. B. (2006). Fitting polytomous Rasch models in SAS. Journal of Applied Measurement, 7(4), 407–417. PubMed

43.

Kreiner S, Nielsen T. Item analysis in DIGRAM 3.04: Part I: Guided tours. Department of Biostastistics, University of Copenhagen 2013.

44.

Martinková, P., Drabinová, A., Liaw, Y.-L., Sanders, E. A., McFarland, J. L., & Price, R. M. (2017). Checking equity: why differential item functioning analysis should be a routine part of developing conceptual assessments. CBE Life Sciences Education. https://doi.org/10.1187/cbe.16-10-0307. CrossRefPubMedPubMedCentral

45.

Bradley, C., & Massof, R. W. (2018). Method of successive dichotomizations: An improved method for estimating measures of latent variables from rating scale data. PLoS ONE. https://doi.org/10.1371/journal.pone.0206106. CrossRefPubMedPubMedCentral

46.

Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika, 34(1), 1–97. https://doi.org/10.1007/BF03372160. CrossRef

47.

Müller, M. (2020). Item fit statistics for Rasch analysis: can we trust them? Journal of Statistical Distributions and Applications., 7(1), 5. https://doi.org/10.1186/s40488-020-00108-7. CrossRef

48.

Montgomery, D. A., Krupa, K., & Cooke, T. G. (2007). Alternative methods of follow up in breast cancer: a systematic review of the literature. British Journal of Cancer, 96(11), 1625–1632. https://doi.org/10.1038/sj.bjc.6603771. CrossRefPubMedPubMedCentral

49.

DeSantis, C., Ma, J., Bryan, L., & Jemal, A. (2014). Breast cancer statistics, 2013. CA: A Cancer Journal for Clinicians, 64(1), 52.

Titel: The development and initial validation of the Breast Cancer Recurrence instrument (BreastCaRe)—a patient-reported outcome measure for detecting symptoms of recurrence after breast cancer
Auteurs: Beverley Lim Høeg
Lena Saltbæk
Karl Bang Christensen
Randi Valbjørn Karlsen
Christoffer Johansen
Susanne Oksbjerg Dalton
Antonia Bennett
Pernille Envold Bidstrup
Publicatiedatum: 16-04-2021
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 9/2021
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-021-02841-1

Bohn Stafleu van Loghum

Tip

Swipe om te navigeren naar een ander artikel

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Purpose

Methods

Results

Conclusions

Log in om toegang te krijgen

Andere artikelen Uitgave 9/2021

Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness

The relationship between emotional intelligence and quality of life in schizophrenia and bipolar I disorder

The predictive ability of EQ-5D-3L compared to the LACE index and its association with 30-day post-hospitalization outcomes

Diabetes and health-related quality of life among American Indians: the role of psychosocial factors

Health-related quality of life in primary hepatic cancer: a systematic review assessing the methodological properties of instruments and a meta-analysis comparing treatment strategies

Correction to: Prospective analysis of disability and quality of life in patients with chronic inflammatory demyelinating polyradiculoneuropathy