Changes in inflammatory biomarkers related to C-reactive protein and albumin in patients with terminal cancer receiving palliative care: a longitudinal study.

ABSTRACT

Background: Evidence about how inflammatory biomarkers vary during the end-stage cancer trajectory is lacking. This study investigates the longitudinal changes in albumin and C-reactive protein (CRP) levels, and CRP/albumin ratio (CAR) in patients with terminal cancer receiving palliative care in the last three months of life.

Methods: This is a retrospective analysis of variables extracted from a prospective cohort study that included admitted patients to the exclusive Palliative Care Unit of the National Cancer Institute in Brazil. Routine blood examination results of albumin and CRP were recorded at 0-15 (T1), 16-30 (T2), 31-45 (T3), 46-60 (T4), 61-75 (T5), and 76-90 (T6) days before death and only patients with at least two measurements were included. Crude and adjusted linear mixed-effects regression models were performed to verify the relationships between the longitudinal trajectories of biomarkers and death.

Results: A total of 1,635 patients were included. Median albumin was 3.00g/dL across the whole time-period analyzed (interquartile range, IQR: 2.50-3.60) and decreased with the approach of death, while median CRP was 9.31mg/L (IQR: 4.42-17.30) and CAR was 3.22 (IQR: 1.42-6.68), and both increased. The albumin (slope: all 0.01; p <0.001), CRP (slope: -0.10 to -0.13; p <0.001), and CAR (slope: -0.05 to -0.07; p <0.001) showed a linear doseresponse relationship with death in crude and adjusted models tested.

Conclusions: The longitudinal change levels of inflammatory biomarkers worsen with the approach of death and could be used to predict end-stage in patients with terminal cancer.

Keywords:

C-reactive protein - albumin - biomarkers - inflammation - terminal cancer - palliative care

Publication History

Received: 28 April 2022

Accepted: 09 May 2022

Article published online:
30 August 2022

• REFERENCES

• 1 Hui D, Paiva CE, Del Fabbro EG. et al. (2019). Prognostication in advanced cancer: update and directions for future research. Support Care Cancer 27: 19731984
• 2 Hui D, Nooruddin Z, Didwaniya N. et al. (2014). Concepts and definitions for “actively dying,” “end of life,” “terminally ill,” “terminal care,” and “transition of care”: a systematic review. J Pain Symptom Manage 47: 77-89
• 3 Simmons CPL, McMillan DC, McWilliams K. et al. (2017). Prognostic tools in patients with advanced cancer: a systematic review. J Pain Symptom Manage 53: 962-970.e10
• 4 Reid VL, McDonald R, Nwosu AC. et al. (2017). A systematically structured review of biomarkers of dying in cancer patients in the last months of life; An exploration of the biology of dying. PLoS ONE 12: e0175123
• 5 Dolan RD, McSorley ST, Horgan PG, Laird B, McMillan DC. (2017). The role of the systemic inflammatory response in predicting outcomes in patients with advanced inoperable cancer: systematic review and meta-analysis. Crit Rev Oncol Hematol 116: 134-146
• 6 McMillan DC. (2013). The systemic inflammation-based Glasgow Prognostic Score: a decade of experience in patients with cancer. Cancer Treat Rev 39: 534-540
• 7 Li N, Tian GW, Wang Y, Zhang H, Wang ZH, Li G. (2017). Prognostic role of the pretreatment C-reactive protein/albumin ratio in solid cancers: a meta-analysis. Sci Rep 41298
• 8 Gray S, Axelsson B. (2018). The prevalence of deranged C-reactive protein and albumin in patients with incurable cancer approaching death. PLoS ONE 13: e0193693
• 9 Ju SY, Ma SJ. (2020). High C-reactive protein to albumin ratio and the short-term survival prognosis within 30 days in terminal cancer patients receiving palliative care in a hospital setting: a retrospective analysis. Medicine 99: e19350
• 10 Schag CC, Heinrich RL, Ganz PA. (1984). Karnofsky Performance Status revisited: reliability, validity, and guidelines. JCO 2: 187-193
• 11 Wiegert EVM, de Oliveira LC, Calixto-Lima L, Mota e, Silva Lopes MSD, Peres WAF. (2020). Cancer cachexia: Comparing diagnostic criteria in patients with incurable cancer. Nutrition 79-80: 110945
• 12 Silva GAD, Wiegert EVM, Calixto-Lima L, Oliveira LC. (2020). Clinical utility of the modified Glasgow Prognostic Score to classify cachexia in patients with advanced cancer in palliative care. Clin Nutr 39 (05) 1587-1592
• 13 de Oliveira LC, Abreu GT, Lima LC, Aredes MA, Wiegert EVM. (2020). Quality of life and its relation with nutritional status in patients with incurable cancer in palliative care. Support Care Cancer
• 14 Wiegert EVM, da Silva NF, de Oliveira LC, Calixto-Lima L. (2021). Reference values for handgrip strength and their association with survival in patients with incurable cancer. Eur J Clin Nutr
• 15 Trajkovic-Vidakovic M, Graeff A, Voest EE. et al. (2021). Symptoms tell it all: a systematic review of the value of symptom assessment to predict survival in advanced cancer patients. Crit Rev Oncol Hematol Oct; 84 (01) 130-148
• 16 Chumlea WMC, Guo SS, Steinbaugh ML. (1994). Prediction of stature from knee height for black and white adults and children with application to mobility-impaired or handicapped persons. J Am Diet Assoc 94: 1385-1391
• 17 Chumlea WC, Guo S, Roche AF, Steinbaugh ML. (1988). Prediction of body weight for the nonambulatory elderly from anthropometry. J Am Diet Assoc 88: 564-568
• 18 Ottery FD. Scored Patient-Generated Subjective Global Assessment (PG-SGA). Accessed April 30, 2016 http://pt-global.org
• 19 Pinheiro JC. Mixed-Effects Models in S and S-PLUS. New York: Springer; 2000. Accessed September 8, 2020. http://link.springer.com/10.1007/b98882
• 20 Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurrence. New York: Oxford University Press;; 2003: 672
• 21 Taylor P, Crouch S, Howell DA, Dowding DW, Johnson MJ. (2015). Change in physiological variables in the last 2 weeks of life: an observational study of hospital in-patients with cancer. Palliat Med 29: 120-127
• 22 Shalapour S, Karin M. (2015). Immunity, inflammation, and cancer: an eternal fight between good and evil. J Clin Invest 125: 3347-3355
• 23 Munn LL. (2017). Cancer and inflammation. Wiley Interdiscip Rev Syst Biol Med 9: e1370
• 24 Negus RPM, Balkwill FR. (1966). Cytokines in tumour growth, migration and metastasis. World J Urol 14: 157-165
• 25 Dunlop RJ, Campbell CW. (2000). Cytokines and advanced cancer. J Pain Symptom Manage 20: 214-232
• 26 Baba M, Maeda I, Morita T. et al. (2015). Independent validation of the modified Prognosis Palliative Care Study predictor models in three palliative care settings. J Pain Symptom Manage 49: 853-860
• 27 Gwilliam B, Keeley V, Todd C. et al. (2011). Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study. BMJ 343: d4920
• 28 Gradel KO, Póvoa P, Garvik OS. et al. (2020). Longitudinal trajectory patterns of plasma albumin and C-reactive protein levels around diagnosis, relapse, bacteraemia, and death of acute myeloid leukaemia patients. BMC Cancer 20: 249
• 29 Cunha GDC, Rosa KSDC, Wiegert EVM, de Oliveira LC. Clinical Relevance and Prognostic Value of Inflammatory Biomarkers: A prospective Study in Terminal Cancer Patients Receiving Palliative Care. J Pain Symptom Manage 2021; 62 (05) 978-986