Few studies have investigated water balance as a predictor of survival in cancer patients in the last days of life.
Objective:
To examine the association between extracellular water (ECW), intracellular water (ICW), ratio of ECW to ICW (ECW/ICW), and survival in nonedematous and edematous patients with advanced cancer admitted to a palliative care unit.
Design:
A prospective observational study.
Setting/Subjects:
Patients with advanced cancer admitted to a palliative care unit.
Measurements:
Upon enrollment, bioelectrical impedance analysis was used to assess ECW, ICW, and body composition. We conducted time-to-event analyses using the Kaplan–Meier method, log-rank test, and univariate and multivariate Cox regression analyses.
Results:
A total of 190 of 204 patients who participated in this study had ICW and ECW measures available for analysis. The median survival was 10 days (95% confidence interval [CI] 9–12) days. The median ECW, ICW, and ECW/ICW were 18.9 L, 19.1 L, and 1.0 for 72 nonedematous patients, and 21.9 L, 20.1 L, and 1.1 for 118 edematous patients, respectively. In univariate Cox regression analysis, ICW ≤20 L was associated with a shorter survival in the nonedematous patients (hazard ratio [HR] 1.92, 95% CI 1.10–3.34, p = 0.02) and a longer survival in the edematous patients (HR 0.61, 95% CI 0.41–0.90, p = 0.01). In multivariable analysis, ICW (≤20 L vs. >20 L) remained an independent prognostic factor in edematous patients (HR 0.64, 95% CI 0.43–0.95, p = 0.03).
Conclusions:
Greater ICW was an independent predictor of poorer survival in cancer patients with edema in the last days of life.
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References
1.
HuiD: Prognostication of survival in patients with advanced cancer: Predicting the unpredictable?. Cancer Control, 2015; 22:489–497.
2.
AmanoK, MaedaI, ShimoyamaS, et al.: The accuracy of physicians' clinical predictions of survival in patients with advanced cancer. J Pain Symptom Manage, 2015; 50:139–146.
3.
HuiD, ParkM, LiuD, et al.: Clinician prediction of survival versus the Palliative Prognostic Score: Which approach is more accurate?. Eur J Cancer, 2016; 64:89–95.
4.
MaltoniM, NanniO, PirovanoM, et al.: Successful validation of the palliative prognostic score in terminally ill cancer patients. Italian Multicenter Study Group on Palliative Care. J Pain Symptom Manage, 1999; 17:240–247.
5.
MoritaT, TsunodaJ, InoueS, et al.: The Palliative Prognostic Index: A scoring system for survival prediction of terminally ill cancer patients. Support Care Cancer, 1999; 7:128–133.
6.
HamanoJ, TakeuchiA, YamaguchiT, et al.: A combination of routine laboratory findings and vital signs can predict survival of advanced cancer patients without physician evaluation: A fractional polynomial model. Eur J Cancer, 2018; 105:50–60.
7.
HuiD, BansalS, MorgadoM, et al.: Phase angle for prognostication of survival in patients with advanced cancer: Preliminary findings. Cancer, 2014; 120:2207–2214.
8.
HuiD, DevR, PimentalL, et al.: Association between multi-frequency phase angle and survival in patients with advanced cancer. J Pain Symptom Manage, 2017; 53:571–576.
9.
HuiD, MooreJ, ParkM, et al.: Phase Angle and the diagnosis of impending death in patients with advanced cancer: Preliminary findings. Oncologist, 2019; 24:e365–e373.
10.
PereiraMME, QueirozMDSC, de AlbuquerqueNMC, et al.: The prognostic role of phase angle in advanced cancer patients: A systematic review. Nutr Clin Pract, 2018; 33:813–824.
11.
KyleUG, UngerP, DupertuisYM, et al.: Body composition in 995 acutely ill or chronically ill patients at hospital admission: A controlled population study. J Am Diet Assoc, 2002; 102:944–955.
12.
KyleUG, BosaeusI, De LorenzoAD, et al.: Bioelectrical impedance analysis—Part I: Review of principles and methods. Clin Nutr, 2004; 23:1226–1243.
13.
KyleUG, BosaeusI, De LorenzoAD, et al.: Bioelectrical impedance analysis part II: Utilization in clinical practice. Clin Nutr, 2004; 23:1430–1453.
14.
MalbrainML, HuyghJ, DabrowskiW, et al.: The use of bio-electrical impedance analysis (BIA) to guide fluid management, resuscitation and deresuscitation in critically ill patients: A bench-to-bedside review. Anaesthesiol Intensive Ther, 2014; 46:381–391.
15.
MialichMS, SicchieriJMF, Jordao JuniorAA: Analysis of body composition: A critical review of the use of bioelectrical impedance analysis. Int J Clin Nutr, 2014; 2:1–10.
16.
ParkJH, JoYI, LeeJH: Clinical usefulness of bioimpedance analysis for assessing volume status in patients receiving maintenance dialysis. Korean J Intern Med, 2018; 33:660–669.
17.
SakataM, KawaguchiT, TaniguchiE, et al.: Oxidized albumin is associated with water retention and severity of disease in patients with chronic liver diseases. E Spen Eur E J Clin Nutr Metab, 2010; 5:247–253.
18.
LiuMH, WangCH, HuangYY, et al.: Edema index established by a segmental multifrequency bioelectrical impedance analysis provides prognostic value in acute heart failure. J Cardiovasc Med, 2012; 13:299–306.
19.
LeeY, KwonO, ShinCS, LeeSM: Use of bioelectrical impedance analysis for the assessment of nutritional status in critically illpatients. Clin Nutr Res, 2015; 4:32–40.
20.
KimEJ, ChoiMJ, LeeJH, et al.: Extracellular fluid/intracellular fluid volume ratio as a novel risk indicator for all-cause mortality and cardiovascular disease in hemodialysis patients. PLoS One, 2017; 12:e0170272.
21.
NgJK, KwanBC, ChowKM, et al.: Asymptomatic fluid overload predicts survival and cardiovascular event in incident Chinese peritoneal dialysis patients. PLoS One, 2018; 13:e0202203.
22.
ShuY, LiuJ, ZengX, et al.: The effect of overhydration on mortality and technique failure among peritoneal dialysis patients: A systematic review and meta-analysis. Blood Purif, 2018; 46:350–358.
23.
CrawfordGB, RobinsonJA, HuntRW, et al.: Estimating survival in patients with cancer receiving palliative care: Is analysis of body composition using bioimpedance helpful?. J Palliat Med, 2009; 12:1009–1014.
24.
Dal CinS, BragaM, MolinariM, et al.: Role of bioelectrical impedance analysis in acutely dehydrated subjects. Clin Nutr, 1992; 11:128–133.
25.
HuiD, BrueraE: The Edmonton Symptom Assessment System 25 years later: Past, present and future developments. J Pain Symptom Manage, 2017; 53:630–643.
26.
FadulN, KaurG, ZhangT, et al.: Evaluation of the memorial delirium assessment scale (MDAS) for the screening of delirium by means of simulated cases by palliative care health professionals. Support Care Cancer, 2007; 15:1271–1276.
27.
MiuraT, MatsumotoY, KawaguchiT, et al.: Low phase angle is correlated with worse general condition in patients with advanced cancer. Nutr Cancer, 2019; 71:83–88.
28.
EarthmanC, TraughberD, DobratzJ, HowellW: Bioimpedance spectroscopy for clinical assessment of fluid distribution and body cell mass. Nutr Clin Pract, 2007; 22:389–405.
29.
Amano k, MaedaI, MoritaT, et al.: Clinical implications of C-reactive protein as a prognostic marker in advanced cancer patients in palliative settings. J Pain Symptom Manage, 2016; 51:860–867.
30.
AmanoK, MaedaI, MoritaT, et al.: C-reactive protein, symptoms and activity of daily living in patients with advanced cancer receiving palliative care. J Cachexia Sarcopenia Muscle, 2017; 8:457–465.
SadeghiM, Keshavarz-FathiM, BaracosV, et al.: Cancer cachexia: Diagnosis, assessment, and treatment. Crit Rev Oncol Hematol, 2018; 127:91–104.
33.
HaverkortEB, ReijvenPL, BinnekadeJM, et al.: Bioelectrical impedance analysis to estimate body composition in surgical and oncological patients: A systematic review. Eur J Clin Nutr, 2015; 69:3–13.
34.
SchagCC, HeinrichRL, GanzPA: Karnofsky performance status revisited: Reliability, validity, and guidelines. J Clin Oncol, 1984; 2:187–193.
