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Construction and validation of a risk-prediction model for chemotherapy-related cognitive impairment in patients with breast cancer

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Abstract

Purpose

To identify risk factors of chemotherapy-related cognitive impairment (CRCI) and construct and validate a visual prediction model of such for patients with breast cancer.

Methods

A multicenter, descriptive, and cross-sectional design was adopted. Data were collected from ten public tertiary hospitals in China. Cognitive function was assessed by using Functional Assessment of Cancer Therapy—cognitive function. Socio-demographic, clinical, psychological, and physical indicators were also assessed. The logistic prediction model was constructed by fivefold cross-validation. Then, a nomogram was utilized to visualize the prediction model, which was also evaluated via discrimination, calibration, and decision curve analysis.

Results

A total of 71 breast cancer patients had CRCI with a prevalence of 9.58%. This visual prediction model was constructed based on education background, exercise frequency, chemotherapy times, and fatigue and demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.882. The calibration curve indicated good agreement between experimental and projected values, and the decision curve proved good clinical applicability.

Conclusion

Education background, exercise frequency, chemotherapy times, and fatigue were associated with high incidence of CRCI. The prediction model exhibits superior performance and has promise as a useful instrument for assessing the likelihood of CRCI in breast cancer patients.

Implications for cancer survivors

Our findings could provide breast cancer survivors with risk screening based on CRCI predictors to implement prevention and early intervention, and help patients integrate into society and achieve comprehensive recovery.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Niksic M, Bonaventure A, Valkov M, Johnson CJ, Esteve J, Ogunbiyi OJ, Azevedo ESG, Chen WQ, Eser S, Engholm G, Stiller CA, Monnereau A, Woods RR, Visser O, Lim GH, Aitken J, Weir HK, Coleman MP, Group CW. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet. 2018;391:1023–75. https://doi.org/10.1016/S0140-6736(17)33326-3.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Emanuel EJ, Gudbranson E, Van Parys J, Gortz M, Helgeland J, Skinner J. Comparing health outcomes of privileged US citizens with those of average residents of other developed countries. JAMA Intern Med. 2021;181:339–44. https://doi.org/10.1001/jamainternmed.2020.7484.

    Article  PubMed  Google Scholar 

  3. Lacourt TE, Heijnen CJ. Mechanisms of neurotoxic symptoms as a result of breast cancer and its treatment: considerations on the contribution of stress, inflammation, and cellular bioenergetics. Curr Breast Cancer Rep. 2017;9:70–81. https://doi.org/10.1007/s12609-017-0245-8.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ono M, Ogilvie JM, Wilson JS, Green HJ, Chambers SK, Ownsworth T, Shum DH. A meta-analysis of cognitive impairment and decline associated with adjuvant chemotherapy in women with breast cancer. Front Oncol. 2015;5:59. https://doi.org/10.3389/fonc.2015.00059.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bender CM, Merriman JD, Sereika SM, Gentry AL, Casillo FE, Koleck TA, Rosenzweig MQ, Brufsky AM, McAuliffe P, Zhu Y, Conley YP. Trajectories of cognitive function and associated phenotypic and genotypic factors in breast cancer. Oncol Nurs Forum. 2018;45:308–26. https://doi.org/10.1188/18.ONF.308-326.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gao WXSY, Yang F. Correlation study on cognitive impairment and perceived stress in breast cancer patients undergoing chemotherapy. Chin Nurs Res. 2020;34(09):1618–21. https://doi.org/10.12102/j.issn.1009-6493.2020.09.027.

  7. Janelsins MC, Heckler CE, Peppone LJ, Kamen C, Mustian KM, Mohile SG, Magnuson A, Kleckner IR, Guido JJ, Young KL, Conlin AK, Weiselberg LR, Mitchell JW, Ambrosone CA, Ahles TA, Morrow GR. Cognitive complaints in survivors of breast cancer after chemotherapy compared with age-matched controls: an analysis from a nationwide, multicenter, prospective longitudinal study. J Clin Oncol. 2017;35:506–14. https://doi.org/10.1200/jco.2016.68.5826.

    Article  PubMed  Google Scholar 

  8. Koppelmans V, Breteler MM, Boogerd W, Seynaeve C, Gundy C, Schagen SB. Neuropsychological performance in survivors of breast cancer more than 20 years after adjuvant chemotherapy. J Clin Oncol. 2012;30:1080–6. https://doi.org/10.1200/jco.2011.37.0189.

    Article  PubMed  Google Scholar 

  9. Yamada TH, Denburg NL, Beglinger LJ, Schultz SK. Neuropsychological outcomes of older breast cancer survivors: cognitive features ten or more years after chemotherapy. J Neuropsychiatry Clin Neurosci. 2010;22:48–54. https://doi.org/10.1176/jnp.2010.22.1.48.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ng T, Dorajoo SR, Cheung YT, Lam YC, Yeo HL, Shwe M, Gan YX, Foo KM, Loh WK, Koo SL, Jain A, Lee GE, Dent R, Yap YS, Ng R, Chan A. Distinct and heterogeneous trajectories of self-perceived cognitive impairment among Asian breast cancer survivors. Psychooncology. 2018;27:1185–92. https://doi.org/10.1002/pon.4635.

    Article  PubMed  Google Scholar 

  11. Schmidt ME, Scherer S, Wiskemann J, Steindorf K. Return to work after breast cancer: the role of treatment-related side effects and potential impact on quality of life. Eur J Cancer Care (Engl). 2019;28:e13051. https://doi.org/10.1111/ecc.13051.

    Article  PubMed  Google Scholar 

  12. Reid-Arndt SA, Yee A, Perry MC, Hsieh C. Cognitive and psychological factors associated with early posttreatment functional outcomes in breast cancer survivors. J Psychosoc Oncol. 2009;27:415–34. https://doi.org/10.1080/07347330903183117.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tager FA, McKinley PS, Schnabel FR, El-Tamer M, Cheung YK, Fang Y, Golden CR, Frosch ME, Habif U, Mulligan MM, Chen IS, Hershman DL. The cognitive effects of chemotherapy in post-menopausal breast cancer patients: a controlled longitudinal study. Breast Cancer Res Treat. 2010;123:25–34. https://doi.org/10.1007/s10549-009-0606-8.

    Article  PubMed  Google Scholar 

  14. Ahles TA, Root JC. Cognitive effects of cancer and cancer treatments. Annu Rev Clin Psychol. 2018;14:425–51. https://doi.org/10.1146/annurev-clinpsy-050817-084903.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Boscher C, Joly F, Clarisse B, Humbert X, Grellard JM, Binarelli G, Tron L, Licaj I, Lange M (2020) Perceived cognitive impairment in breast cancer survivors and its relationships with psychological factors. Cancers (Basel) 12 https://doi.org/10.3390/cancers12103000

  16. Van Dyk K, Bower JE, Crespi CM, Petersen L, Ganz PA. Cognitive function following breast cancer treatment and associations with concurrent symptoms. NPJ Breast Cancer. 2018;4:25. https://doi.org/10.1038/s41523-018-0076-4.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cui H, Shi X, Song X, Zhang W. Changes and influencing factors of cognitive impairment in patients with breast cancer. Evid Based Complement Alternat Med. 2021;2021:7278853. https://doi.org/10.1155/2021/7278853.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Park JH, Jung YS, Jung YM, Bae SH. The role of depression in the relationship between cognitive decline and quality of life among breast cancer patients. Support Care Cancer. 2019;27:2707–14. https://doi.org/10.1007/s00520-018-4546-x.

    Article  PubMed  Google Scholar 

  19. Gullett JM, Cohen RA, Yang GS, Menzies VS, Fieo RA, Kelly DL, Starkweather AR, Jackson-Cook CK, Lyon DE. Relationship of fatigue with cognitive performance in women with early-stage breast cancer over 2 years. Psychooncology. 2019;28:997–1003. https://doi.org/10.1002/pon.5028.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170:W1–33. https://doi.org/10.7326/M18-1377.

    Article  PubMed  Google Scholar 

  21. Henneghan AM, Van Dyk K, Zhou X, Moore RC, Root JC, Ahles TA, Nakamura ZM, Mandeblatt J, Ganz PA. Validating the PROMIS cognitive function short form in cancer survivors. Breast Cancer Res Treat. 2023;201:139–45. https://doi.org/10.1007/s10549-023-06968-2.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Cheung YT, Foo YL, Shwe M, Tan YP, Fan G, Yong WS, Madhukumar P, Ooi WS, Chay WY, Dent RA, Ang SF, Lo SK, Yap YS, Ng R, Chan A. Minimal clinically important difference (MCID) for the functional assessment of cancer therapy: cognitive function (FACT-Cog) in breast cancer patients. J Clin Epidemiol. 2014;67:811–20. https://doi.org/10.1016/j.jclinepi.2013.12.011.

    Article  PubMed  Google Scholar 

  23. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162:55–63. https://doi.org/10.7326/M14-0697.

    Article  PubMed  Google Scholar 

  24. Wagner LI, Sweet J, Butt Z, Lai J-s, Cella D. Measuring patient self-reported cognitive function: development of the functional assessment of cancer therapy-cognitive function instrument. J Support Oncol. 2009;7:W32–9.

    Google Scholar 

  25. Jia M, Zhang X, Wei L, Gao J. Measurement, outcomes and interventions of cognitive function after breast cancer treatment: a narrative review. Asia Pac J Clin Oncol. 2021;17:321–9. https://doi.org/10.1111/ajco.13484.

    Article  PubMed  Google Scholar 

  26. Henneghan AM, Van Dyk K, Kaufmann T, Harrison R, Gibbons C, Heijnen C, Kesler SR. Measuring self-reported cancer-related cognitive impairment: recommendations from the Cancer Neuroscience Initiative Working Group. J Natl Cancer Inst. 2021;113:1625–33. https://doi.org/10.1093/jnci/djab027.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wefel JS, Vardy J, Ahles T, Schagen SB. International Cognition and Cancer Task Force recommendations to harmonise studies of cognitive function in patients with cancer. Lancet Oncol. 2011;12:703–8. https://doi.org/10.1016/S1470-2045(10)70294-1.

    Article  PubMed  Google Scholar 

  28. Lange M, Licaj I, Clarisse B, Humbert X, Grellard JM, Tron L, Joly F. Cognitive complaints in cancer survivors and expectations for support: results from a web-based survey. Cancer Med. 2019;8:2654–63. https://doi.org/10.1002/cam4.2069.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Buysse DJ, Reynolds CF 3rd, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. https://doi.org/10.1016/0165-1781(89)90047-4.

    Article  CAS  PubMed  Google Scholar 

  30. Carpenter JS, Andrykowski MA. Psychometric evaluation of the Pittsburgh Sleep Quality Index. J Psychosom Res. 1998;45:5–13. https://doi.org/10.1016/s0022-3999(97)00298-5.

    Article  CAS  PubMed  Google Scholar 

  31. Okuyama T, Akechi T, Kugaya A, Okamura H, Imoto S, Nakano T, Mikami I, Hosaka T, Uchitomi Y. Factors correlated with fatigue in disease-free breast cancer patients: application of the Cancer Fatigue Scale. Support Care Cancer. 2000;8:215–22. https://doi.org/10.1007/s005200050288.

    Article  CAS  PubMed  Google Scholar 

  32. Spitzer RL, Kroenke K, Williams JB, Lowe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166:1092–7. https://doi.org/10.1001/archinte.166.10.1092.

    Article  PubMed  Google Scholar 

  33. Plummer F, Manea L, Trepel D, McMillan D. Screening for anxiety disorders with the GAD-7 and GAD-2: a systematic review and diagnostic metaanalysis. Gen Hosp Psychiatry. 2016;39:24–31. https://doi.org/10.1016/j.genhosppsych.2015.11.005.

    Article  PubMed  Google Scholar 

  34. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16:606–13. https://doi.org/10.1046/j.1525-1497.2001.016009606.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Levis B, Benedetti A, Thombs B, DEPRESsion screening data (DEPRESSD) collaboration. Accuracy of patient health questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. BMJ. 2019;365:l1781. https://doi.org/10.1136/bmj.l1476

  36. Taylor JM, Wang L, Li Z. Analysis on binary responses with ordered covariates and missing data. Stat Med. 2007;26:3443–58. https://doi.org/10.1002/sim.2815.

    Article  PubMed  Google Scholar 

  37. Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Ser B: Stat Methodol. 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.

  38. Schneeweiss S, Eddings W, Glynn RJ, Patorno E, Rassen J, Franklin JM. Variable selection for confounding adjustment in high-dimensional covariate spaces when analyzing healthcare databases. Epidemiology. 2017;28:237–48. https://doi.org/10.1097/EDE.0000000000000581.

    Article  PubMed  Google Scholar 

  39. Alba AC, Agoritsas T, Walsh M, Hanna S, Iorio A, Devereaux PJ, McGinn T, Guyatt G. Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA. 2017;318:1377–84. https://doi.org/10.1001/jama.2017.12126.

    Article  PubMed  Google Scholar 

  40. Von Ah D, Crouch A. Relationship of perceived everyday cognitive function and work engagement in breast cancer survivors. Support Care Cancer. 2021;29:4303–9. https://doi.org/10.1007/s00520-020-05950-8.

    Article  Google Scholar 

  41. Yarkoni T, Westfall J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect Psychol Sci. 2017;12:1100–22. https://doi.org/10.1177/1745691617693393.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Perrier J, Viard A, Levy C, Morel N, Allouache D, Noal S, Joly F, Eustache F, Giffard B. Longitudinal investigation of cognitive deficits in breast cancer patients and their gray matter correlates: impact of education level. Brain Imaging Behav. 2020;14:226–41. https://doi.org/10.1007/s11682-018-9991-0.

    Article  PubMed  Google Scholar 

  43. Vemuri P, Weigand SD, Przybelski SA, Knopman DS, Smith GE, Trojanowski JQ, Shaw LM, Decarli CS, Carmichael O, Bernstein MA, Aisen PS, Weiner M, Petersen RC, Jack CR Jr, Alzheimer’s Disease Neuroimaging I. Cognitive reserve and Alzheimer’s disease biomarkers are independent determinants of cognition. Brain. 2011;134:1479–92. https://doi.org/10.1093/brain/awr049.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Antkiewicz-Michaluk L, Krzemieniecki K, Romanska I, Michaluk J, Krygowska-Wajs A. Acute treatment with doxorubicin induced neurochemical impairment of the function of dopamine system in rat brain structures. Pharmacol Rep. 2016;68:627–30. https://doi.org/10.1016/j.pharep.2016.01.009.

    Article  CAS  PubMed  Google Scholar 

  45. Kim J, Park J, Mikami T. Regular low-intensity exercise prevents cognitive decline and a depressive-like state induced by physical inactivity in mice: a new physical inactivity experiment model. Front Behav Neurosci. 2022;16:866405. https://doi.org/10.3389/fnbeh.2022.866405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Courneya KS, McKenzie DC, Mackey JR, Gelmon K, Friedenreich CM, Yasui Y, Reid RD, Vallerand JR, Adams SC, Proulx C, Dolan LB, Wooding E, Segal RJ. Subgroup effects in a randomised trial of different types and doses of exercise during breast cancer chemotherapy. Br J Cancer. 2014;111:1718–25. https://doi.org/10.1038/bjc.2014.466.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Courneya KS, Segal RJ, Gelmon K, Mackey JR, Friedenreich CM, Yasui Y, Reid RD, Proulx C, Trinh L, Dolan LB, Wooding E, Vallerand JR, McKenzie DC. Predictors of adherence to different types and doses of supervised exercise during breast cancer chemotherapy. Int J Behav Nutr Phys Act. 2014;11:85. https://doi.org/10.1186/s12966-014-0085-0.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Perri MG, Anton SD, Durning PE, Ketterson TU, Sydeman SJ, Berlant NE, Kanasky WF Jr, Newton RL Jr, Limacher MC, Martin AD. Adherence to exercise prescriptions: effects of prescribing moderate versus higher levels of intensity and frequency. Health Psychol. 2002;21:452–8 https://www.ncbi.nlm.nih.gov/pubmed/12211512.

    Article  PubMed  Google Scholar 

  49. Koleck TA, Bender CM, Sereika SM, Ahrendt G, Jankowitz RC, McGuire KP, Ryan CM, Conley YP. Apolipoprotein E genotype and cognitive function in postmenopausal women with early-stage breast cancer. Oncol Nurs Forum. 2014;41:E313-325. https://doi.org/10.1188/14.ONF.E313-E325.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ahles TA, Saykin AJ, Noll WW, Furstenberg CT, Guerin S, Cole B, Mott LA. The relationship of APOE genotype to neuropsychological performance in long-term cancer survivors treated with standard dose chemotherapy. Psychooncology. 2003;12:612–9. https://doi.org/10.1002/pon.742.

    Article  PubMed  Google Scholar 

  51. Gibson EM, Monje M. Emerging mechanistic underpinnings and therapeutic targets for chemotherapy-related cognitive impairment. Curr Opin Oncol. 2019;31:531–9. https://doi.org/10.1097/CCO.0000000000000578.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Wu QC, Hu XH. Progression of chemotherapy-related cognitive impairment. Chin J Cancer Prev Treat. 2014;21:2012–6. https://doi.org/10.16073/j.cnki.cjcpt.2014.24.016.

  53. Rodriguez N, Fawcett JM, Rash JA, Lester R, Powell E, MacMillan CD, Garland SN. Factors associated with cognitive impairment during the first year of treatment for nonmetastatic breast cancer. Cancer Med. 2021;10:1191–200. https://doi.org/10.1002/cam4.3715.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Arya N, Vaish A, Zhao K, Rao H. Neural mechanisms underlying breast cancer related fatigue: a systematic review of neuroimaging studies. Front Neurosci. 2021;15:735945. https://doi.org/10.3389/fnins.2021.735945.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Fox RS, Ancoli-Israel S, Roesch SC, Merz EL, Mills SD, Wells KJ, Sadler GR, Malcarne VL. Sleep disturbance and cancer-related fatigue symptom cluster in breast cancer patients undergoing chemotherapy. Support Care Cancer. 2020;28:845–55. https://doi.org/10.1007/s00520-019-04834-w.

    Article  PubMed  Google Scholar 

  56. Van Dyk K, Ganz PA. Cancer-related cognitive impairment in patients with a history of breast cancer. JAMA. 2021;326:1736–7. https://doi.org/10.1001/jama.2021.13309.

    Article  PubMed  Google Scholar 

  57. He CC, Lin DM, Liu HZ, Wang FF, Guo XF, Zhang XB, Ai YQ, Meng LM. Nonpharmacological interventions for management of the pain-fatigue-sleep disturbance symptom cluster in breast cancer patients: a systematic review and network meta-analysis of randomized controlled trials. J Pain Res. 2023;16:2713–28. https://doi.org/10.2147/JPR.S409798.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We highly appreciate the patients with BC who participated in this study and the nurses of the Department of Breast Oncology, who contributed significantly time and dedication to this work.

Funding

This study was provided by the Natural Science Foundation of Beijing Municipality, China (grant no. 7222008).

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Y.L. and J.-E.L. designed the study, are involved in the study conception, and wrote and revised the manuscript. R.-L.L., L.C., F.-Y.Z., and Y.-L.S. performed data collection and curation. Y.L. and S.J. analyzed the data. All authors read and approved the final manuscript.

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Correspondence to Jun-E Liu.

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Liu, Y., Li, RL., Chen, L. et al. Construction and validation of a risk-prediction model for chemotherapy-related cognitive impairment in patients with breast cancer. J Cancer Surviv (2024). https://doi.org/10.1007/s11764-024-01566-7

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