Sage Journals: Discover world-class research

Abstract

BACKGROUND:

The Controlling Nutritional Status (CONUT) score, a novel immuno-nutritional index, was reported as a predictor of overall survival (OS) in some tumors.

OBJECTIVE:

We aimed to investigate the association between baseline CONUT Score and clinical outcomes in patients with multiple myeloma (MM).

METHODS:

We performed a retrospective analysis of 245 patients with MM. The CONUT score was determined prior to therapy.

RESULTS:

Among the entire cohort, the complete remission rate was markedly higher in the low-CONUT ( $\leqslant$ 3) group compared to the mid-CONUT (4–9) group or high-CONUT ( $>$ 9) group (44.1% vs 25.8%, $P=$ 0.039; 44.1% vs 12.5%, $P=$ 0.002). Patients with CONUT score $>$ 9 had significant poor prognosis, and CONUT score $\leqslant$ 3 group showed better survival outcome than other groups in OS ( $P<$ 0.001). Besides, we stratified the patients by combining International Staging System (ISS) stage and CONUT score in a model, and found that CONUT score could improve the prognostic impact of ISS stages on OS. In multivariate analysis, older age ( $\geqslant$ 70 years) and a high CONUT score ( $\geqslant$ 4) were independent prognostic risk factors for OS.

CONCLUSIONS:

The CONUT score was a predictor of OS in MM patients especially in cases with both low ISS staging and CONUT score. The baseline CONUT score may be an early and practical indicator of the efficacy of anti-myeloma treatment.

Keywords

Multiple myeloma controlling nutritional status score prognostic

1. Introduction

Multiple myeloma (MM) is a malignant disorder of plasma cells that mainly occurs in elderly patients, being diagnosed at a median age 70 years [1]. With the rapid evolution of very active new therapies for MM in recent decades, the survival of affected patients has significantly improved [2]. However, there are patients who experience disease progression and relapse.

The prognosis of patients with MM is highly variable according to different tumor characteristics, the microenvironment and host factors. Several prognostic indices such as the International Staging System (ISS) and revised ISS (R-ISS) have been established and have stratified prognosis of patients with MM [3, 4]. Nevertheless, the current prognostic staging systems do not take into account the role of the patient’s immune-nutritional status in MM treatment.

Malnutrition is known to be associated with worse clinical outcomes and the severity of chemotherapy-related toxicity in cancer patients [5, 6]. The Controlling Nutritional Status (CONUT) score is now used as an immuno-nutritional index to detect undernourished patients in the hospitalized population [7]. The score is based on the values of serum albumin, total cholesterol (TC) and lymphocyte count. It has been reported to associate with poor survival in patients with solid tumors and hematological malignancies [8, 9, 10, 11, 12]. Recently, two studies from Japan have reported that CONUT score is a useful prognostic indicator in patients with MM [13, 14]. This needs to be confirmed in other patient cohorts. Furthermore, the impact of the CONUT score on the response to treatment in MM patients is very limited. Therefore, we performed a retrospective study to investigate the association between baseline CONUT score and clinical outcomes of patients with MM.

2. Patients and methods

2.1 Patients

We performed a single-center retrospective analysis of 376 consecutive patients with symptomatic MM who were diagnosed between January 1, 2007 and December 31, 2017. Patients who refused or abandoned any treatment ( $n=$ 26), those received only palliative care ( $n=$ 56), those lost to follow-up ( $n=$ 15), and those with amyloidosis at the time of diagnosis ( $n=$ 4), malignancies (e.g., urogenital, gastrointestinal cancers, $n=$ 5) or missing data ( $n=$ 25) were excluded. Finally, 245 patients were eligible for the analysis and were followed until December 31, 2019. This study was approved by the Institutional Review Board of Wuxi People’s Hospital and carried in accordance with the Helsinki Declaration. Informed consent was waived because of the retrospective design of the data collection.

2.2 Clinical and laboratory information

All clinical data regarding patients recruited were obtained from medical charts, electronic records and telephone interviews by the trained staff members. Data were collected with the following items: age, gender, M protein type, ISS stage, white blood cell count, hemoglobin, lymphocyte count, TC, World Health Organization (WHO) performance status, lactate dehydrogenase (LDH), albumin, and creatinine at diagnosis, and information concerning treatment and response. The CONUT scores, used to assess immuno-nutritional status, were calculated from the serum albumin concentration, total peripheral lymphocyte count, and TC concentration, as previously described [7]. Treatment for each patient was determined by the physician, the patient, or their family. Treatment responses were evaluated according to IMWG criteria [15].

2.3 Statistical analyses

All analyses were performed using SPSS statistical software (version 19.0; IBM Corp., Armonk, NY, USA). $P$ -values ${<}$ 0.05 were considered statistically significant. Continuous variables were summarized as median and extreme values; categorical data as absolutes and percentages. The significant differences between the groups were compared using the chi-square test or Fisher’s exact test and one-way ANOVA as appropriate. X-tile software (version 3.6.1; Yale University, New Haven, CT, USA) was utilized to identify the optimal cut-off value of CONUT score regarding Overall survival (OS) [16]. We classified patients into the low-CONUT group ( ${\leqslant}$ 3), the mid-CONUT group (3 ${<}$ CONUT score ${\leqslant}$ 9), and the high-CONUT group ( ${>}$ 9), using 3 and 9 as the cutoff values. OS was calculated from the time of diagnosis of symptomatic myeloma to the time of death or last follow-up. Survival analysis was constructed according to the Kaplan-Meier method, and differences between survival curves were analyzed using the log-rank test. Binary logistic regression model was used to assess the independent prognostic factors for OS identified in univariate and multivariate analyses.

Table 1
Patients characteristics of patients between three CONUT score groups

Characteristics	CONUT score ${\leqslant}$ 3	3 ${<}$ CONUT score ${\leqslant}$ 9	CONUT score ${>}$ 9	$P$ value
Number of patients	34	163	48
Median age (year)	63 (37–75)	65 (33–83)	68 (47–82)	0.009
Male, $n$ (%)	18/52.9	96/58.9	31/64.6	0.567
Myeloma type, $n$ (%)				0.001
IgG	9/26.5	81/49.7	23/47.9
IgA	10/29.4	36/22.1	22/45.8
Light chain	11/32.3	34/20.8	2/4.2
Non-secretory	4/11.8	8/4.9	0/0
Others	0/0	4/2.5	1/2.1
ISS stage, $n$ (%)
I	0/0	2/1.2	0/0	0.656
II	19/55.9	72/44.2	20/41.7
III	15/44.1	89/54.6	28/58.3
WBC ( $\times$ 10 ${}^{9}$ /L, Median, range)	5.71 (2.8–19.1)	4.7 (0.7–17.1)	3.9 (0.8–9.8)	${<}$ 0.001
Lymphocyte ( $\times$ 10 ${}^{9}$ /L, Median, range) protein	1.7 (0.85–3.03)	1.19 (0.16–3.95)	0.93 (0.21–1.55)	${<}$ 0.001
Hemoglobin (g/L, Median, range)	102.5 (54–148)	85 (37–158)	71.5 (45–111)	${<}$ 0.001
T-Chol (mg/dl, Median, range)	167 (95–260)	132 (40–247)	80 (45–160)	${<}$ 0.001
Albumin (g/dL, Median, range)	3.5 (3.2–4.3)	2.9 (1.2–4.2)	2.2 (1.4–2.9)	${<}$ 0.001
LDH ${>}$ NUL, $n$ (%)	1/2.9	33/20.2	7/14.6	0.029
Creatinine ${>}$ 177 $\mu$ mmol/L, $n$ (%)	7/20.6	52/31.9	17/35.4	0.327
WHO performance status ${\geqslant}$ 3, $n$ (%)	7/20.6	54/33.1	18/37.5	0.246
Bortezomib as initial therapy (%)	22/64.7	107/65.6	23/47.9	0.084

Ig, Immunoglobulin; ISS, International Staging System; WBC, White blood cell count; T-Chol, Total cholesterol; LDH, lactate dehydrogenase; NUL, normal upper limit; CONUT score, Controlling Nutritional score; WHO, World Health Organization.

3. Results

3.1 Patient characteristics

A total of 245 patients with newly diagnosed MM were included in the analysis. The median age of patients in this study was 65 (range, 33–83) years. There were more men ( $n=$ 145, 59.2%) and the major type of MM included IgG ( $n=$ 112, 45.7%), IgA ( $n=$ 26, 27.8%). Regarding ISS stage, 53.9% of patients had stage III, and 45.3% had stage II, and 0.8% had stage I disease. The median CONUT score was 7 (range, 0–12). The median follow-up of all patients was 38 (range, 1–154) months, and 79 of 245 (32.2%) patients were alive at the time of data collection. Among the 166 (67.8%) patients who died, the causes of death were categorized as follows: progressive disease and multiorgan failure ( $n=$ 54, 32.5%), infection ( $n=$ 52, 31.3%), renal failure ( $n=$ 28, 16.9%), congestive heart failure ( $n=$ 14, 8.5%), central nervous system involvement ( $n=$ 2, 1.2%), second malignancy ( $n=$ 3, 1.8%), stroke ( $n=$ 7, 4.2%), intracranial bleeding ( $n=$ 2, 1.2%), sudden death ( $n=$ 3, 1.8%), and cirrhosis ( $n=$ 1, 0.6%).

The baseline characteristics of the patients stratified according to the CONUT score are presented in Table 1. High CONUT scores were associated with lower white blood cell count, lower lymphocyte, lower hemoglobin level, lower albumin level, lower TC level, and elevated LDH. Patients in the highest CONUT score group were older ( $P=$ 0.009) and had higher prevalence of IgA ( $P=$ 0.001).

3.2 Response and CONUT score

Most patients received bortezomib ( $n=$ 152, 62%) as initial therapy. Among the entire cohort, complete remission (CR) after initial therapy was observed in 63 (25.7%) patients, very good partial remission (VGPR) in 82 (33.5%) patients and partial remission (PR) in 83 patients (33.9%). We explored the impact of CONUT scores on the response to first-line treatment. The patients with a low-CONUT score or mid- CONUT score at diagnosis had better overall response rates (ORR) when compared to those with a high-CONUT score (100% vs 83.3%, $P=$ 0.018; 94.5% vs 83.3%, $P=$ 0.029, Fig. 1). CR rate was markedly higher in the low-CONUT group compared to the mid-CONUT group or high-CONUT group (44.1% vs 25.8%, $P=$ 0.039; 44.1% vs 12.5%, $P=$ 0.002, Fig. 1).

Figure 1.

Treatment response in patients with different baseline CONUT score ( $N=$ 245). CONUT, controlling nutritional status.

3.3 Overall survival and CONUT score

The median OS of the entire cohort was estimated as 48 months (95% CI: 41.2–54.8). We analyzed the impact of CONUT score on the survival of patients. The 3-year OS estimates for the low-CONUT group, mid-CONUT group and high-CONUT group were 81.7%, 59.8%, and 33.5%, respectively. The 5-year OS estimates of the three groups were 65.1%, 38.9% and 16.6%, respectively. The CONUT score is an important prognosis factor for MM, and patients with CONUT score ${>}$ 9 had significant poor prognosis, and CONUT score ${\leqslant}$ 3 group showed better survival outcome than other groups in OS ( $P<$ 0.001, Fig. 2).

Figure 2.

Overall survival according to the CONUT score at diagnosis. CONUT, controlling nutritional status.

3.4 CONUT score improves the risk assessment of ISS staging

Among the whole cohort, the ISS stage was stage I in 2 patients, stage II in 111 patients, and stage III in 132 patients. Although the patients with an ISS ${\leqslant}$ II at diagnosis had better median OS when compared to those with an ISS $=$ III (57 and 37 months, respectively), the differences did not have any statistically significance ( $P=$ 0.079, Fig. 3a). ISS alone was unable to separate out cohorts based on OS.

We then examined the effect of the CONUT score on OS in the different ISS stages patients. CONUT score was assessed separately for ISS stages II and III, as shown in Fig. 3. We did not analyze the OS for the ISS I stage due to the few cases (only two). The median OS of patients with CONUT score ${\leqslant}$ 3, 4–9 and ${>}$ 9 in stages II was not reached, 60 months and 30 months ( $P<$ 0.001, Fig. 3b). The median OS of patients with CONUT score ${\leqslant}$ 3, 4–9 and ${>}$ 9 in stages III was 117 months, 36 months and 25 months, respectively ( $P=$ 0.006, Fig. 3c).

We further analyzed whether the CONUT score could improve the prognostic impact of ISS stages. We stratified the patients according to combining ISS stage and CONUT score into four different groups. The ISS-CONUT very low-risk group was defined as ISS ${\leqslant}$ II and CONUT score ${\leqslant}$ 3; ISS-CONUT low-risk group was defined as ISS $=$ III and CONUT score ${\leqslant}$ 3 or ISS ${\leqslant}$ II and 3 ${<}$ CONUT score ${\leqslant}$ 9; ISS-CONUT standard-risk was defined by 3 ${<}$ CONUT score ${\leqslant}$ 9 and ISS $=$ III, or ISS ${\leqslant}$ II and CONUT score ${>}$ 9. Finally, ISS-CONUT high-risk group was defined by ISS $=$ III and CONUT score ${>}$ 9. In all, 7.8% of the patients were classified in the very low-risk group, 36.3% low risk, 44.5% standard risk and 11.4% high risk. The 3-year OS estimates were 81, 74.5, 45.5 and 36.6% for ISS-CONUT very low-risk, low-risk, standard-risk and high-risk groups, respectively. The 5-year OS estimates were 67, 51.8, 26.5 and 18.6% for ISS-CONUT very low-risk, low-risk, standard-risk and high-risk groups, respectively ( $P<$ 0.001, Fig. 3d).

Table 2
Univariate and multivariate analysis of factors associated with overall survival

Variable	Univariate analysis		Multivariate analysis
	OR (95% CI)	$P$ value	OR (95% CI)	$P$ value
Age (year) ( ${\geqslant}$ 70 vs. ${<}$ 70)	3.079 (1.542–6.149)	0.001	2.261 (1.095–4.669)	0.027
Gender (male vs. female)	1.678 (0.976–2.887)	0.061	1.712 (0.951–3.085)	0.073
M protein type (others vs. IgG or IgA)	0.605 (0.335–1.095)	0.097	0.894 (0.455–1.757)	0.746
ISS (III vs. I–II)	0.832 (0.485–1.427)	0.504
WHO performance status ( ${\geqslant}$ 3 vs. ${<}$ 3)	1.483 (0.82–2.682)	0.192
Hemoglobin (g/L) ( ${<}$ 10 vs. ${\geqslant}$ 10)	1.559 (0.859–2.832)	0.144
Albumin (g/dL) ( ${<}$ 3.5 vs. ${\geqslant}$ 3.5)	3.962 (1.706–9.2)	0.001	1.573 (0.585–4.233)	0.369
LDH ( ${\geqslant}$ NUL vs. ${<}$ NUL)	2.649 (1.118–6.278)	0.027	2.381 (0.949–5.974)	0.064
Creatinine ( $\mu$ mmol/L) ( ${\geqslant}$ 177 vs. ${<}$ 177)	1.651 (0.9–3.031)	0.105
CONUT score		${<}$ 0.001		0.001
${\leqslant}$ 3	reference		reference
4–9	4.256 (1.957–9.295)	${<}$ 0.001	3.276 (1.469–7.306)	0.004
${>}$ 9	9.167 (3.257–25.802)	${<}$ 0.001	6.786 (2.351–19.591)	${<}$ 0.001

Abbreviations: ISS, international staging system; WHO, World Health Organization; LDH, lactate dehydrogenase; NUL, normal upper limit; CONUT, controlling nutritional status; OS, overall survival; OR, odds ratio; CI, confidence interval.

Figure 3.

Analysis of survival in patients with multiple myeloma. (a) OS according to the ISS. (b–c) OS by ISS stages II and III with the CONUT score ${\leqslant}$ 3, 3 ${<}$ CONUT score ${\leqslant}$ 9 and CONUT score ${>}$ 9. (d) OS by ISS-CONUT risk stratification. The ISS-CONUT very low-risk group was defined as ISS ${\leqslant}$ II and CONUT score ${\leqslant}$ 3; ISS-CONUT low-risk group was defined as ISS $=$ III and CONUT score ${\leqslant}$ 3 or ISS ${\leqslant}$ II and 3 ${<}$ CONUT score ${\leqslant}$ 9; ISS-CONUT standard-risk was defined by 3 ${<}$ CONUT score ${\leqslant}$ 9 and ISS $=$ III, or ISS ${\leqslant}$ II and CONUT score ${>}$ 9. Finally, ISS-CONUT high-risk group was defined by ISS $=$ III and CONUT score ${>}$ 9. ISS, international staging system; OS, Overall survival; CONUT, controlling nutritional status.

3.5 Prognostic factors

Results of the univariate and multivariate analyses for factors influencing OS in newly diagnosed patients with MM were reported in Table 2. In univariate analysis, older age ( ${\geqslant}$ 70 years), high LDH level, low albumin level and a high CONUT score ( ${\geqslant}$ 4) were risk factors poorly impacting OS. All variables with $P<$ 0.1 in univariate analysis were included in multivariate analysis. Results were expressed as Odds ratios (OR) and 95% confidence interval (CI). In multivariate analysis, older age ( ${\geqslant}$ 70 years) and CONUT score ${\geqslant}$ 4 were independent prognostic factors for survival in the present patients.

4. Discussion

In recent years, the CONUT score as a simple, objective and inexpensively measured parameter of immuno-nutritional status has attracted attention. By studying the characteristics of 245 real-world patients, we found that the OS rate of the low-CONUT group was significantly higher than that of the mid-CONUT group or high-CONUT group, and the CONUT score was an independent prognosis in newly diagnosed MM patients, which is in agreement with previous investigations [13, 14].

Okamoto et al. [13] retrospectively analyzed the CONUT score in 64 Japanese patients with symptomatic MM. In this study, the median age at diagnosis was 66 years, and the CONUT score ${\geqslant}$ 5 was identified as factor independently predicting OS. Among younger patients (age ${<}$ 65 years) who received autologous peripheral blood stem cell transplantation (auto-PBSCT), CONUT score ${<}$ 4 showed a significantly better survival outcome. Another study [14] that investigated the CONUT score values of 178 MM patients in Japan concluded that patients with CONUT score ${\geqslant}$ 5 had significantly poorer OS when compared to those with a CONUT score ${<}$ 5. The higher CONUT score was an independent risk factor, especially in low-ISS-score patients with MM.

Recent reports showed that the prevalence of malnutrition and its risk among cancer patients were positively associated with the disease stages, about 60% of advanced cancer patients were malnourished [17, 18]. In the present study, 55.9% patients had CONUT scores ${>}$ 6, which is different from the values reported in the literature [13, 14]. Our patients had not only higher prevalence of malnutrition based on the CONUT score, but also a higher proportion of ISS stage III compared with those of the previous reports (53.9% vs. 20.3%, 46.6%) [13, 14]. There are some possible explanations for an unequal distribution of ISS stage – lower ISS stage I vs. higher ISS stage III in our study. First, this discrepancy is partially related to the different sources of patients, which reflects the heterogeneity of MM in the real-life clinical setting. Our results represent the real patient cohort of MM in developing countries where healthcare resources are limited. MM screening procedures and early diagnosis cannot be implemented well. In addition, older patients usually have more complications and can simulate more complications symptoms, many of them do not come to the clinic until the disease becomes more severe [19].

Poor status of nutrition is associated with the response to chemotherapy and the development of side effects and toxicity of therapy-related, which also are factors associated with the rise of healthcare costs for managing cancer patients [17, 18, 20]. Therefore, early nutritional status screening and assessment may be important to provide comprehensive algorithms for individualized clinical treatment in cancer patients. In our study, the CR rate of initial therapy was markedly higher in the low-CONUT group compared to the mid-CONUT group or high-CONUT group, suggesting that the baseline CONUT score could become an early and practical indicator of the efficacy of anti-myeloma treatment.

The OS rate of our patients tended to be relatively shorter than those previous reports [21, 22, 23]. Our cohort had a very lower transplantation rate and short course novel agents use [24], which likely better reflected the variation in the systems of care provision, financing and clinical practice patterns for MM patients in the real-world setting. Interestingly, the ISS was unable to identify any clear value in the outcomes for OS in our series. This was possibly due to small number and uneven distribution of the patients. In contrast, the addition of CONUT score to the ISS further defined prognosis particularly in stage II and III patients. We thus combined the ISS stage and CONUT score to propose an algorithm, which could possibly improve the predictive value of the ISS staging system and assist in individualized treatment.

Our study had some limitations. First, it was a collection of retrospective data from a single center with possible selection bias. Second, we did not evaluate any treatment differences. Third, the data of fluorescence in situ hybridization (FISH) or cytogenetics were lacking in our cohort. In addition, the CONUT score did not have a standardized cut off, and the cut-off values of CONUT score as a prognostic marker for cancer patients were varied with reports.

In conclusion, the CONUT score was a useful predictor of OS and treatment response in MM patients. The CONUT score could be combined with ISS staging system to identify patients with survival outcome. Further investigations focused on this subject are warranted.

Footnotes

Acknowledgments

The authors thank all of the doctors and nursing staff of Department of Hematology in Wuxi people’s Hospital for their dedicated patient care.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Conception: Hongfeng Guo.

Interpretation or analysis of data: Xin Zhou, Yan Lu, Jun Xia, Jingjue Mao, Jing Wang.

Preparation of the manuscript: Hongfeng Guo, Xin Zhou, Yan Lu.

Revision for important intelectual content: Hongfeng Guo, Xin Zhou, Yan Lu.

Supervision: Hongfeng Guo.

References

Palumbo

and Anderson

, Multiple myeloma, N Engl J Med 364 (2011), 1046–1060.

Landgren

and Iskander

, Modern multiple myeloma therapy: deep, sustained treatment response and good clinical outcomes, J Intern Med 281 (2017), 365–382.

Greipp

P.R.

San Miguel

Durie

B.G.

et al., International staging system for multiple myeloma, J Clin Oncol 23 (2005), 3412–3420.

Rajkumar

S.V.

Dimopoulos

M.A.

Palumbo

et al., International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma, Lancet Oncol 15 (2014), e538–548.

Fearon

K.C.

Voss

A.C.

Hustead

D.S.

and Cancer Cachexia Study G, Definition of cancer cachexia: effect of weight loss, reduced food intake, and systemic inflammation on functional status and prognosis, Am J Clin Nutr 83 (2006), 1345–1350.

Dewys

W.D.

Begg

Lavin

P.T.

et al., Prognostic effect of weight loss prior to chemotherapy in cancer patients. Eastern cooperative oncology group, Am J Med 69 (1980), 49–497.

Ignacio de Ulibarri

Gonzalez-Madrono

de Villar

N.G.

et al., CONUT: a tool for controlling nutritional status, First validation in a hospital population, Nutr Hosp 20 (2005), 38–45.

Takagi

Buettner

and Ijzermans

J.N.M.

, Prognostic significance of the controlling nutritional status (CONUT) score in patients with colorectal cancer: a systematic review and meta-analysis, Int J Surg 78 (2020), 91–96.

Kang

H.W.

Seo

S.P.

Kim

W.T.

et al., Prognostic impact of nutritional status assessed by the controlling nutritional status (CONUT) score in patients with surgically treated renal cell carcinoma, Nutr Cancer 70 (2018), 886–894.

10.

Nagata

Kanemasa

Sasaki

et al., Clinical impact of controlling nutritional status score on the prognosis of patients with diffuse large B-cell lymphoma, Hematol Oncol 38 (2020), 309–317.

11.

Ureshino

Kusaba

Kidoguchi

et al., Clinical impact of the CONUT score and mogamulizumab in adult T cell leukemia/lymphoma, Ann Hematol 98 (2019), 465–471.

12.

Araie

Kawaguchi

Okabe

et al., Prediction of clinical outcome by controlling nutritional status (CONUT) before allogeneic hematopoietic stem cell transplantation in myeloid malignancies, Int J Hematol 110 (2019), 599–605.

13.

Okamoto

Ureshino

Kidoguchi

et al., Clinical impact of the CONUT score in patients with multiple myeloma, Ann Hematol 99 (2020), 113–119.

14.

Kamiya

Ito

Fujita

et al., The prognostic value of the controlling nutritional status score in patients with multiple myeloma, Leuk Lymphoma 19 (2020), 1–7.

15.

Durie

B.G.

Harousseau

J.L.

Miguel

J.S.

et al., International uniform response criteria for multiple myeloma, Leukemia 20 (2006), 1467–1473.

16.

Camp

R.L.

Dolled-Filhart

and Rimm

D.L.

, X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization, Clinical Cancer Research 10 (2004), 7252–7259.

17.

Laviano

Di Lazzaro

and Koverech

, Nutrition support and clinical outcome in advanced cancer patients, Proc Nutr Soc 77 (2018), 388–393.

18.

Muscaritoli

Lucia

Farcomeni

et al., Prevalence of malnutrition in patients at first medical oncology visit: the PreMiO study, Oncotarget 8 (2017), 79884–79896.

19.

Friese

C.R.

Abel

G.A.

Magazu

L.S.

et al., Diagnostic delay and complications for older adults with multiple myeloma, Leuk Lymphoma 50 (2009), 392–400.

20.

Wiernikowski

J.T.

and Bernhardt

M.B.

, Review of nutritional status, body composition, and effects of antineoplastic drug disposition, Pediatr Blood Cancer 21 (2020), e28207.

21.

Kumar

S.K.

Dispenzieri

Lacy

M.Q.

et al., Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients, Leukemia 28 (2014), 1122–1128.

22.

Pozzi

Marcheselli

Bari

et al., Survival of multiple myeloma patients in the era of novel therapies confirms the improvement in patients younger than 75 years: a population-based analysis, Br J Haematol 163 (2013), 40–46.

23.

Palumbo

Avet-Loiseau

Oliva

et al., Revised international staging system for multiple myeloma: a report from international myeloma working group, J Clin Oncol 33 (2015), 2863–2869.

24.

Qian

Chen

Xia

et al., Real-world clinical outcomes in elderly chinese patients with multiple myeloma: a single-center experience, Med Sci Monit 24 (2018), 5887–5893.

Association between baseline Controlling Nutritional Status score and clinical outcomes of patients with multiple myeloma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Patients and methods

2.1 Patients

2.2 Clinical and laboratory information

2.3 Statistical analyses

Table 1 Patients characteristics of patients between three CONUT score groups

3.1 Patient characteristics

3.2 Response and CONUT score

Table 2 Univariate and multivariate analysis of factors associated with overall survival

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

Author contributions

References

Table 1
Patients characteristics of patients between three CONUT score groups

Table 2
Univariate and multivariate analysis of factors associated with overall survival