The Role of Inflammation and Nutrition-Based Scoring in Low-Risk Myelodysplastic Syndrome

doi:10.21203/rs.3.rs-4991051/v1

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The Role of Inflammation and Nutrition-Based Scoring in Low-Risk Myelodysplastic Syndrome

https://doi.org/10.21203/rs.3.rs-4991051/v1

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While the pathogenesis of Myelodysplastic Syndrome (MDS) is diverse, growing evidence suggests that inflammation significantly influences disease development and progression. This study sought to assess the impact of inflammation and nutritional status on mortality, with a particular focus on patients with low-risk MDS. A retrospective analysis was conducted on 175 newly diagnosed low-risk MDS patients. A low Prognostic Nutritional Index (PNI) was significantly associated with poorer prognosis (p<0.001). The optimal PNI cut-off value for predicting mortality was identified as 47.47. Based on this cut-off, 92 patients had a low PNI score while 83 patients had a high PNI score. The comparison between these groups revealed a statistically significant difference in median overall survival (OS), with 45.5 months for the low PNI group and 75.1 months for the high PNI group (p<0.001). In the multivariate OS analysis, several factors were identified as independent predictors of prognosis, including a high Revised International Prognostic Scoring System (R-IPSS) score, low PNI, high systemic oxidative stress (SOS) score, advanced age, male gender, and transformation to acute myeloid leukemia. The PNI is a readily available and cost-effective marker that can be utilized to predict prognosis in patients with low-risk MDS.

Biological sciences/Cancer/Haematological cancer/Myelodysplastic syndrome

Health sciences/Medical research/Outcomes research

Low-risk Myelodysplastic Syndrome

inflammation

nutrition

mortality

cardiovascular disease

Myelodysplastic Syndrome (MDS) is a diverse group of clonal disorders marked by dysplastic changes in the bone marrow, ineffective hematopoiesis, peripheral cytopenias, recurrent genetic abnormalities, and the potential to progress to acute myeloid leukemia (AML) (1). Although MDS pathologies are varied, a growing body of evidence points to dysregulated innate inflammation as a key factor that drives both the disease phenotype and its progression through alterations in both the hematopoietic and stromal compartments (2–4).

The International Prognostic Scoring System (IPSS) (5) and its revised version (R-IPSS) (6) are essential tools for evaluating the prognosis of untreated MDS patients (7). However, it is important to also consider additional factors, such as comorbidities, when making treatment decisions. According to the IPSS, which assesses bone marrow blast percentage, karyotype, and the presence of cytopenias, patients with low-risk MDS typically experience a milder disease course. The R-IPSS, which includes cytogenetic analysis, bone marrow blast count, and the presence of cytopenias, classifies patients based on their disease risk. Patients with low-risk MDS generally experience a milder disease course. In contrast, those with high-risk MDS may progressively exhibit signs of bone marrow failure and have a higher likelihood of progressing to AML.

However, data on the prognostic value of this marker in low-risk MDS patients are quite limited in the literature. This has prompted the search for more accessible tests that can predict prognosis in MDS. Various studies have explored several factors influencing the prognosis of MDS, including comorbidities (8), high serum ferritin levels (9), the presence of bone marrow fibrosis (10), monocytosis (11), hypoalbuminemia (12), ALT levels (13), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) (14), and hyperfibrinogenemia (15), among others.

In recent years, there has been growing interest in how nutritional and immune status affect cancer prognosis. The Prognostic Nutritional Index (PNI), which is calculated using serum albumin concentration and peripheral blood lymphocyte count, serves as a marker for the nutritional and immune status of cancer patients (16).

Research has demonstrated that nutritional status can predict prognosis across various cancer types, including hematologic malignancies (17–21). However, there is limited data in the literature regarding the prognostic value of this marker specifically for low-risk MDS patients.

This study was conducted to identify new risk assessment markers, as well as to evaluate the impact of inflammation and nutritional status at diagnosis on the progression of low-risk MDS. The aim was to determine how these factors influence survival outcomes and whether they can be used alongside the IPSS and R-IPSS to enhance prognosis prediction. A key distinguishing feature of this study is its specific focus on low-risk MDS patients, setting it apart from other studies in the literature.

Patients’ Characteristics

Among the 175 low-risk MDS patients included in the study, 89 (50.9%) were classified as MDS with multilineage dysplasia (MDS-MLD), 59 (33.7%) as MDS with single-lineage dysplasia (MDS-SLD), 15 (8.6%) as MDS with excess blasts-1 (MDS-EB-1), 6 (3.4%) as hypocellular MDS, and 6 (3.4%) as MDS with 5q syndrome. The median age of the patients was 68 years, with a range of 29 to 88 years. The cohort consisted of 88 females (50.3%) and 87 males (49.7%). According to the IPSS score, 87 patients (49.7%) were classified in the low-risk group, and 88 patients (50.3%) were classified in the intermediate-1 risk group. The median OS for the patients was 63.1 months, with a 95% confidence interval (CI) of 49.4 to 76.7 months. During the follow-up period, 9 patients (5.1%) progressed to AML. At the end of the study, 59 patients (33.7%) were still alive, while 116 patients (66.3%) had deceased. The demographic characteristics of the patients are summarized in Table 1.

Table 1

Demographic and Clinical Characteristics of the Patients
Variables	n = 175	%
Age (years)
Median (min-max)	68 (29–88)
≤ 60	50	28.6
61–70	62	35.4
> 70	63	36.0
Gender
Male	87	49.7
Female	88	50.3
MDS Subtypes
Hypocellular MDS	6	3.4
MDS-5Q syndrome	6	3.4
MDS-EB1	15	8.6
MDS-MLD	89	50.9
MDS-SLD	59	33.7
IPSS score
Median (min-max)	0.5 (0–1)
Low	87	49.7
Intermediate-1	88	50.3
R-IPSS score
Median (min-max)	2.5 (0-4.5)
Very low	28	16
Low	113	64.6
Intermediate	34	19.4
Transfusion dependence
None	108	61.7
Present	67	38.3
AML conversion
None	166	94.9
Present	9	5.1
Mortality
Alive	59	33.7
Deceased	116	66.3
Cardiovascular death
None	53	45.7
Present	63	54.3

Abbreviations: MDS: Myelodysplastic Syndrome, MDS-EB1: MDS with Excess Blasts-1, MDS-MLD: MDS with Multilineage Dysplasia, MDS-SLD: MDS with Single-Lineage Dysplasia MDS, AML: Acute Myeloid Leukemia, IPSS: International Prognostic Scoring System, R-IPSS: Revised International Prognostic Scoring System

Nutrition-Based and Inflammation Scores Analysis

The cut-off value for the PNI was established at 47.47, representing the highest Youden index on the ROC curve, which indicates the optimal balance between sensitivity and specificity. ROC analysis result table is shown in Table 2. Based on this cut-off, patients were classified into two groups: those with a low PNI (≤ 47.47) and those with a high PNI (> 47.47). The low PNI group comprised 92 patients, while the high PNI group comprised 83 patients. When comparing the two groups, the low PNI group exhibited higher R-IPSS scores (p = 0.013) and lower levels of leukocytes (p < 0.001), lymphocytes (p < 0.001), hemoglobin (p = 0.029), MCV (p = 0.020), platelets (p < 0.001), albumin (p < 0.001), total protein (p < 0.001), NSII (p < 0.001), and LMR (p < 0.001). Additionally, this group showed higher levels of creatinine (p = 0.007), LDH-to-albumin ratio (p < 0.001), LDH-to-lymphocyte ratio (p < 0.001), SIRI (p = 0.005), PLR (p = 0.029), NLR (p < 0.001), and SOS (p = 0.038). The low PNI group was also associated with a lower overall survival (OS) (p < 0.001). Clinical and laboratory characteristics of the two groups and all patients are summarized in Table 3.

Table 2

Analysis of the Predictive Value of PNI in Distinguishing Mortality
	AUC	95% CI	Cut-off	Sensitivity	Specificity	p value
PNI	0.629	0.538–0.720	47.47	60	63	0.005
AUC: Area Under the Curve; 95% CI: Confidence Interval

Table 3

Clinical and Laboratory Characteristics of the Patients
Variables, median (min-max)	PNI ≤ 47.47 (n = 92)	PNI > 47.47 (n = 83)	Total (n = 175)	p
Gender
Female	44	44	88	0.493^x2
Male	48	39	87	0.493^x2
Age (years)	69 (31–88)	66 (29–83)	68 (29–88)	0.052^m
MDS subtype
Hypoplastic MDS	6	0	6
5-Q syndrome	2	4	6
MDS-EB-1	9	6	15	0.139^x2
MDS-SLD	46	43	89
MDS-MLD	29	30	59
Leukocytes, /µl	3895 (500-12000)	4950 (2200–10800)	4560 (500-12000)	< 0.001^m
Lymphocytes, /µl	955 (250–2510)	1890 (862–5100)	1400 (250–5100)	< 0.001^m
Monocytes, /µl	349.5 (20-4300)	452 (26-1190)	415 (20-4300)	0.154^m
Neutrophils, /µl	2355 (330-10000)	2450 (150–8350)	2400 (150-10000)	0.893^m
Hemoglobin, g/dL	8.75 ± 1.58	9.27 ± 1.49	9 (4.0-12.8)	0.029^t
MCV, fL	91.5 (64–117)	96 (62–120)	93 (62–120)	0.020^m
Platelets,×10⁹	136 (5.3–457)	233 (5.9–714)	165 (5.9–714)	< 0.001^m
IPSS	0.5 (0–1)	0 (0–1)	0.5 (0–1)	0.109^m
R-IPSS	2.5 (1-4.5)	2 (0-4.5)	2.5 (0-4.5)	0.013^m
Transfusion dependence
None	54	54	108	0.387^x2
Present	38	29	67	0.387^x2
Ferritin, µg/L	292 (5-6800)	312 (4.5–2412)	297 (4.5–6800)	0.948^m
LDH, U/L	223 (114–983)	202 (114–612)	205 (114–983)	0.232^m
Albumin, g/L	37 (10–43)	43 (35–49)	40 (10–49)	< 0.001^m
Total protein, g/L	68 (10–84)	72 (61–85)	70 (10–85)	< 0.001^m
Cholesterol, mg/dL	143 (85–363)	161 (97–275)	158 (85–363)	0.117^m
Total bilirubin, mg/dL	0.67 (0.15–5.2)	0.66 (0.12–2.65)	0.66 (0.12–5.2)	0.815^m
Creatinine, mg/dL	0.91 (0.5-7)	0.80 (0.5–1.72)	0.84 (0.5-7)	0.007^m
BUN, mg/dL	21 (7–81)	18 (9–45)	20 (7–81)	0.006^m
LDH/Albumin	6.22 (0-44.68)	4.72 (0-15.3)	5.26 (0-44.68)	< 0.001^m
LDH/Lymphocytes	0.22 (0-1.89)	0.11 (0-0.35)	0.14 (0-1.89)	< 0.001^m
SIRI	0.92 (0.03–18.5)	0.54 (0-4.07)	0.68 (0-18.5)	0.005^m
NSII	51.0 (1.2–612)	156 (11.2–3360)	81.4 (1.2–3360)	< 0.001^m
SII	326.5 (20.2–2935)	248.8 (2.11–2700)	297.5 (2.11–2935)	0.201^m
LMR	2.97 (0.06–38.5)	4.49 (1.36–160)	3.71 (0.06–160)	< 0.001^m
PLR	132.7 (5.1–808)	105.3 (2.11–432)	118.6 (2.11–808)	0.029^m
NLR	2.82 (0.37–15.6)	1.39 (0.03–4.99)	1.83 (0.03–15.6)	< 0.001^m
SOS	184.8 (102-847.7)	157 (79.6–514)	168 (79.6-847.7)	0.038^m
AML Transformation
Present	5	4	9	0.854^x2
None	87	79	166	0.854^x2
Final status
Alive	22	37	59	0.004^x2
Deceased	70	46	116	0.004^x2
OS (months)	45.46 (33.2–57.6)	75.1 (56.6–93.5)	63.1 (49.4–76.7)	< 0.001^k
Abbreviations: MDS: Myelodysplastic Syndrome, MDS-EB1: MDS with Increased Blasts-1, MDS-MLD: MDS with Multilineage Dysplasia, MDS-SLD: MDS with Single Lineage Dysplasia, MCV: Mean Corpuscular Volume, IPSS: International Prognostic Scoring System, R-IPSS: Revised International Prognostic Scoring System, LDH: Lactate Dehydrogenase, AML: Acute Myeloid Leukemia, SOS Systemic Oxidative Stress Score, OS: Overall Survival ^m: Mann Whitney u, ^x2: Pearson Chi-square, ^k: Kaplan-Meier Survival Analysis, ^t : Independent Samples t-test, p < 0.05 considered statistically significant

Univariate and Multivarate Analysis of Nutrition-Based and Inflammation Scores

In the univariate analysis, the following factors were identified as statistically significant predictors of poor prognosis: age, gender, IPSS and R-IPSS scores, transfusion dependency, AML transformation, leukocyte count, lymphocyte count, hemoglobin level, MCV, platelet count, LDH, albumin, total protein, ferritin, NLR, SIRI, SOS, PNI, the LDH-to-albumin ratio, and the LDH-to-lymphocyte ratio.

In the multivariate analysis, age, gender, R-IPSS, AML transformation, PNI score, and SOS score were identified as independent risk factors for OS in low-risk MDS patients. Detailed results of the univariate and multivariate data analyses are summarized in Table 4. Advanced age (HR 1.057, 95% CI 1.036–1.078, p < 0.001), male gender (HR 0.529, 95% CI 0.359–0.779, p = 0.001), high R-IPSS (HR 1.532, 95% CI 1.242–1.889, p < 0.001), low PNI (HR 0.954, 7 > 95%CI 0.931–0.978, p < 0.001), and high SOS (HR 1.003, 95%CI 1.001–1.006, p = 0.002), and AML transformation (HR 6.381, 95% CI 2.966–13.727, p < 0.001) were associated with poor prognosis.

Table 4

Univariate and Multivariate Prognostic Data Analysis for OS
	Univariate Cox Regression
Risk Factors	HR	95% CI	p	HR	95% CI	p value
Age	1.046	1.027–1.066	< 0.001	1.057	1.036–1.078	< 0.001
Gender	0.575	0.397–0.832	0.003	0.529	0.359–0.779	0.001
IPSS	3.020	1.710–5.333	< 0.001
R-IPSS	1.469	1.215–1.777	< 0.001	1.532	1.242–1.889	< 0.001
Transfusion dependence	2.101	1.456–3.031	< 0.001
AML Transformation	3.265	1.623–6.568	< 0.001	6.381	2.966–13.727	< 0.001
Leukocyte,/µl	1.000	1.000–1.000	0.032
Lymphocyte, /µl	1.000	0.999-1.000	0.011
Hemoglobin ,g/dL	0.846	0.753–0.950	0.005
MCV,fL	1.016	1.000-1.033	0.049
Platelet,×109	1.000	1.000–1.000	0.018
LDH, U/L	1.002	1.000-1.004	0.026
Albumin, g/L	0.952	0.926–0.979	< 0.001
Ferritin, µg/L	1.000	1.000–1.000	0.024
PNI	0.957	0.937–0.979	< 0.001	0.954	0.931–0.978	< 0.001
NLR	1.085	1.000-1.177	0.049
SIRI	1.087	1.015–1.164	0.016
SOS	1.003	1.001–1.005	0.009	1.003	1.001–1.006	0.002
LDH/Lymphocytes	2.277	1.259–4.118	0.007
LDH/Albumin	1.076	1.031–1.123	< 0.001
Abbreviations: IPSS: International Prognostic Scoring System, R-IPSS: Revised International Prognostic Scoring System, AML: Acute Myeloid Leukemia, MCV: Mean Corpuscular Volume, LDH: Lactate Dehydrogenase, PNI: Prognostic Nutritional Index, NLR: Neutrophil/Lymphocyte Ratio, SIRI: Systemic Inflammatory Response Index, SOS: Systemic Oxidative Stress Score, HR: Hazard Ratio, CI: Confidence Interval

Survival Analysis

The optimal cut-off value for age to predict mortality was found to be 65.5 years. The area under the curve (AUC) for this cut-off was 0.675 (95% CI 0.590–0.761), with a sensitivity of 68% and a specificity of 61% for ages at or below this value. In addition, neutrophil and monocyte counts, RDW, PDW, cholesterol, LMR, PLR, SII, NSII, creatinine, BUN, GFR, total bilirubin, LDH-ferritin ratio, albumin-NLR ratio, neutrophil-albumin ratio, PIV, FAR, and NPS did not show a significant statistical impact on prognosis. ROC analysis determined the optimal cut-off value for PNI to distinguish mortality as 47.47. The AUC was 0.629 (95% CI 0.538–0.720), with a sensitivity of 60% and specificity of 63% for values at or below this cut-off (see Fig. 1). Kaplan-Meier survival analysis comparing low PNI and high PNI groups showed that the OS was significantly lower in the low PNI group compared to the high PNI group (45.46 months vs. 75.1 months, p < 0.001, see Fig. 2).

This study represents the largest series to date evaluating PNI in low-risk MDS patients. A low PNI value was significantly associated with poor prognosis, and PNI at diagnosis was identified as an independent risk factor for overall survival in these patients.

In recent years, numerous studies have suggested that inflammation and nutrition can serve as prognostic indicators in cancer patients (20, 22, 23). Additionally, recent research supports the activation of inflammatory pathways in the bone marrow of low-risk MDS patients (24–26).

The PNI reflects the interaction between host immunity, as indicated by lymphocyte count, tumor-induced inflammation, and nutritional status. Several studies have proposed that PNI can predict prognosis across various malignancies (20, 22, 27, 28). However, whether PNI is an effective nutritional and immunological marker for predicting prognosis in low-risk MDS remains unclear.

In a recent study involving 121 MDS patients, the high PNI cohort demonstrated significantly better OS (p = 0.020), with an optimal cut-off value of 44. Subgroup analysis revealed that the impact of PNI on outcomes was most pronounced in intermediate-risk patients (29).

Similarly, in this study, the low PNI group was associated with poorer OS, with an optimal cut-off value of 47.47. For this cut-off, the sensitivity was 60% and the specificity was 63%.

The SOS score is calculated using five biomarkers: serum creatinine, albumin, total bilirubin, LDH, and BUN. Excessive oxidative stress can cause DNA damage and genomic instability, leading to a loss of cellular integrity, function, and viability (30). The prognostic significance of SOS has been studied in various cancers, including breast, lung, and gastric cancers, where it has been identified as an independent risk factor (23, 31, 32). To the best of the researchers’ knowledge, this study is the first to establish a relationship between SOS and MDS prognosis. The study found that patients with high SOS had worse survival outcomes.

Clonal hematopoiesis not only raises the risk of cancer and mortality but also increases the likelihood of cardiovascular disease, particularly due to mutations in genes like TET2, DNMT3A, ASXL1, and JAK2. Although the role of inflammatory signaling in the relationship between clonal hematopoiesis and aging is not fully understood, chronic age-related inflammation likely influences hematopoietic stem cells, contributing to both cardiovascular disease and immune responses (33). Regional differences in cardiovascular disease mortality have been observed, typically ranging from 30–40% in individuals over 60. In this study, after excluding deaths from AML transformation, secondary malignancies, infection, and bleeding, cardiovascular disease accounted for 54.3% of deaths. Given that MDS predominantly affects an elderly population, pre-existing cardiovascular conditions may worsen due to inflammation. Early assessment, close monitoring, and appropriate treatment at diagnosis could help reduce the risk of cardiovascular-related mortality.

In our cohort, several markers that have been evaluated in different studies including neutrophil and monocyte counts, RDW, PDW, cholesterol, NLR, PLR, SII, NSII, creatinine, BUN, GFR, total bilirubin, LDH-ferritin ratio, albumin-NLR ratio, neutrophil/albumin ratio, PIV, FAR, and NPS did not show a significant statistical impact on the prognosis of MDS.

The limitations of our study include its retrospective design and being conducted at a single center.

The PNI is a simple, inexpensive, and easily accessible measure that reflects a patient's immune function, inflammation, and nutritional status at diagnosis. It may serve as a predictor of mortality in low-risk MDS. Moreover, the use of anti-inflammatory therapies and treatments targeting oxidative stress in MDS patients might help slow disease progression. While prognostic models like ours offer valuable insights into MDS, they require further refinement. Larger, prospective studies incorporating new molecular data are needed to enhance their accuracy and applicability.

The study included 175 low-risk MDS patients diagnosed between January 2008 and December 2022 at the Adult Hematology Department of Uludağ University, Bursa. These patients were followed retrospectively until January 2024. At the time of diagnosis, data were collected on leukocytes (WBC), hemoglobin, mean corpuscular volume (MCV), platelets, neutrophils, lymphocytes, monocytes, red cell distribution width (RDW), platelet distribution width (PDW), ferritin, creatinine, blood urea nitrogen (BUN), glomerular filtration rate (GFR), lactate dehydrogenase (LDH), albumin, total protein, cholesterol, and total bilirubin levels. The following indices were calculated: PNI [serum albumin (g/L) + 5 × lymphocyte count (x10^9/L)], Neutrophil-to-Lymphocyte Ratio (NLR) (neutrophil/lymphocyte), Lymphocyte-to-Monocyte Ratio (LMR) (lymphocyte/monocyte), Platelet-to-Lymphocyte Ratio (PLR) (platelet/lymphocyte), Systemic Immune-Inflammation Index (SIRI) (neutrophil × monocytes / lymphocytes), Systemic Immune Index (SII) (neutrophil × platelet / lymphocyte), NSII (lymphocyte × platelet / neutrophil), Systemic Oxidative Stress Score (SOS) (-0.64 × creatinine + 0.56 × total bilirubin + 0.86 × LDH + 0.70× BUN − 0.68 × albumin), Pan-Immune Inflammation Value (PIV) (platelet × monocyte × neutrophil / lymphocyte), Ferritin-to-Albumin Ratio (FAR), and Naples Prognostic Score (NPS). Patients were classified into subtypes according to the World Health Organization (WHO) 2016 classification [hypocellular MDS, MDS with 5q syndrome, MDS with single-lineage dysplasia (SLD), MDS with multilineage dysplasia (MLD), MDS with ring sideroblasts (RS), MDS with excess blasts-1 (EB1), MDS with excess blasts-2 (EB2), MDS unclassified (U)] and into risk groups according to IPSS and R-IPSS. High-risk patients (medium-2 and high-risk according to IPSS, high and very high-risk according to R-IPSS) were not included in the study. The primary endpoint of the study was OS, defined as the duration from diagnosis to death from any cause or until the last clinical follow-up.

Statıstıcal Analyses

Statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were presented as counts and percentages for categorical variables, and as mean ± standard deviation (SD) and median (range) for continuous variables. Receiver Operating Characteristic (ROC) analysis was performed to assess the ability of various clinical parameters to predict mortality. Survival times across different clinical parameter groups were compared using the Kaplan-Meier method. Additionally, multivariate Cox regression analysis was carried out. A p-value of less than 0.05 was considered statistically significant.

Ethical approval: Ethical approval for this study was obtained from the Bursa Uludağ University Faculty of Medicine Clinical Research Ethics Committee (Decision No. 2023-21/17, dated October 24, 2023). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects.

Author Contributions

T.E. and V.Ö. wrote the main manuscript. F.Ç.H., F.Ö. and S.Ç. contributed to planning methodology and preparing Tables and Figures. Ş.Y, E.E, T.G.K. contributed to data collection and data management. T.E., V.Ö, and F.Ö. reviewed the manuscript before submission. All authors reviewed the manuscript.

Competing Interests

Authors declare no conflict of interest.

Additional Information

Correspondence and requests for materials should be addressed to T.E

Data Availability Statement: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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No competing interests reported.

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The Role of Inflammation and Nutrition-Based Scoring in Low-Risk Myelodysplastic Syndrome

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Abstract

Figures

INTRODUCTION

RESULTS

Patients’ Characteristics

Nutrition-Based and Inflammation Scores Analysis

Univariate and Multivarate Analysis of Nutrition-Based and Inflammation Scores

Survival Analysis

DISCUSSION

CONCLUSIONS

MATERIALS AND METHODS

Statıstıcal Analyses

Declarations

References

Additional Declarations

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Version 1