Prevalence of anemia in predialysis chronic kidney disease: Is the study center a significant factor?

OBJECTIVES: Anemia is highly prevalent in chronic kidney disease patients; however, its identification and management have been reported to be suboptimal. In this study we aimed to describe the prevalence, severity, risk factors, and treatment of anemia in different nephrology centers, among chronic kidney disease patients who were not given renal replacement therapy.
MATERIALS AND METHODS: We performed a multicenter cross-sectional study in three different nephrology clinics. Adult (>18 years of age) chronic kidney disease patients with an estimated glomerular filtration rate (eGFR) below 60 ml/min, and who were not started dialysis were recruited. Demographic, clinical and laboratory data regarding anemia and its management were collected using a standard data form. Anemia was defined as a hemoglobin level below 12g/dL and severe anemia as a hemoglobin level below 10g/dl.
RESULTS: A total of 1066 patients were enrolled in the study. Anemia and severe anemia were present in 55.9% and 14.9% of the patients, respectively. The mean hemoglobin level for the whole cohort was 11.8±1.8 g/dL. Univariate analyses revealed that the mean hemoglobin level was significantly different among the centers. Moreover, the frequency of the presence of anemia stratified by severity was also significantly different among the centers. According to binary logistic regression analysis, gender, levels of eGFR and iron, ferritin ≥ 100 ng/mL, and the nephrology center were independent determinants of severe anemia.
CONCLUSIONS: We found a high prevalence of anemia among chronic kidney disease patients who were not on renal replacement therapy. Each center should determine the treatment strategy according to the patient's characteristics. According to our results, the center-specific management of anemia seems to be important.

Introduction

Anemia is a highly prevalent and modifiable risk factor for many adverse events in patients with chronic kidney disease (CKD) [1]. Anemia also contributes to the progression of CKD [2]. The greatest declines in the hematocrit level are observed in the early stages of kidney disease, with the reductions getting smaller in moderate to advanced renal failure. Thus, early detection and monitoring of anemia are required in CKD patients [3].

A significant increase in the prevalence of anemia develops as the creatinine clearance falls below 70 mL/min in males or below 50 mL/min in females [2]. The correction of anemia has been shown to improve cardiac and cognitive functions, quality of life, physical activity, shorten the hospitalization period and decrease mortality [4-8]. Despite these benefits, identification, and management of anemia among patients with CKD has been reported to be suboptimal. Anemia in CKD patients on dialysis has been extensively studied. However, in CKD patients who are not yet on hemodialysis, there is a paucity of large-scale studies [1,2]. Moreover, optimal management of anemia in predialysis patients remains uncertain [9]. According to a large-scale randomized control trial, performed in predialysis CKD patients, hemoglobin (Hgb) normalization (Hgb≥13 g/dL) was associated with increased mortality [10]. However, a recent meta-analysis favors a higher Hgb target in predialysis patients [11]. Additionally, predialysis management of anemia with erythropoiesis-stimulating agents (ESA) was found to be associated with reduced all cause and cardiovascular mortality in patients attaining a Hgb level of >9 g/dL.

According to a recent large-scale multicenter multinational study, there is a striking difference between different countries regarding the frequency of predialysis anemia [12]. However, to the best of our knowledge, center-based differences were not extensively studied previously. We performed a study to describe the prevalence, severity, risk factors, and treatment of anemia among CKD patients who were not given renal replacement therapy in different nephrology centers. We also aimed to analyze the center-based differences regarding those parameters.

Patients and methods

The study was approved by the Clinical Research Ethics Committee of Cerrahpasa Medical Faculty (approval number: 117945/2018). All participants gave written informed consent. We performed a multicenter cross-sectional study in three different nephrology clinics located in the same geographical region (Marmara) of Turkey. Center A and B (Old Town) are located in Istanbul and Center C is located in Kocaeli. The number of inpatient bed for nephrology were 10 in Center A, 15 in Center B and 29 in Center C. The total number of inpatient beds for all departments were 500 in Center A, 1350 in Center B and 730 in Center C. The number of patients who applied to the outpatient nephrology clinic in a month were approximately 1300 in Center A, 1350 in Center B and 1130 in Center C. A total of 1066 CKD patients who were >18 years of age, had an estimated glomerular filtration rate (eGFR) below 60 ml/min, were not started dialysis and were under regular follow-up at the outpatient clinics were included in this study. The study was conducted between February 2018 and August 2018.

All consecutive patients who met the inclusion criteria of the study during the enrollment period were included and data were collected using a standard data form. Baseline data included sex, age, causes of kidney disease, presence of menopause, diabetes mellitus, primary hematologic disease, and malignancy. Blood samples were analyzed at the respective laboratories of the participating centers. Laboratory data, including complete blood cell counts, serum creatinine, C-reactive protein (CRP), vitamin B12, folate, ferritin, iron, total iron-binding capacity (TIBC), transferrin saturation ratio, and intact parathyroid hormone (iPTH) were collected from the medical records. Current use of iron supplements, ESA, folate, and vitamin B12 supplements were also recorded. Anemia was defined as a Hgb level below 12 g/dL and severe anemia was defined as a Hgb level below 10 g/dl [6]. Glomerular filtration rate was estimated using the abbreviated version of the Modification of Diet in Renal Disease (MDRD) formula [13].

Our study had the following potential bias inheriting to the cross-sectional study design. First, there is a possibility of information bias, since we collected the data on drug use not just through the medical records but with patient interviews. Second, there is a possibility of selection bias, because we examined patients during a time period of seven months. We probably missed a proportion of the patients who attended to outpatient clinics less frequently, such as, once a year. The study was conducted in accordance with the principles of the 1975 Declaration of Helsinki (as revised in 1983).

Statistical analysis

The characteristics of the patients were described using descriptive statistics; categorical data were stated as counts and proportions, and continuous data as mean standard deviation (SD), median and minimum-maximum values. The statistical differences between the groups were calculated using the chi-square test for nominal variables. The distribution normality of quantitative variables was calculated with the Shapiro-Wilk test. We compared the groups using one-way ANOVA for normally distributed variables, or otherwise using the Kruskal-Wallis test. Post-hoc multiple comparison analysis was performed with significant values adjusted by the Bonferroni correction. Binary logistic regression analysis was used to predict the association of covariate variables with severe anemia. The presence of malignancy and primary hematological disease (PHD) have been associated with anemia [14]. Therefore, we excluded the patients with malignancy and PHD from the logistic regression analysis, and accordingly, this analysis was carried out in 971 subjects. We constructed a multivariate model using the variables selected according to the P value (<0.05) of univariate analysis (S1 Table). The following variables were selected: gender, nephrology center, presence of diabetes, ferritin ≥ 100 ng/mL, levels of eGFR, CRP, iPTH, iron, and TIBC. The stage of CKD and creatinine is reflected by eGFR, therefore, this parameter was not included in the binary logistic regression analysis. Additionally, since ESA and iron were used because of anemia, these parameters were also not included in the binary logistic regression analysis. Statistical analysis was performed using the IBM SPSS v.24 for Windows software and was reported with 95% confidence intervals (CI). Values of p<0.05 were considered significant.

Results

General characteristics of the study population

The general characteristics of the patients are shown in Table 1. Study subjects were generally old patients (median age: 68.0, range: 18–97) with a nearly equal gender distribution. Most of the females were in menopause, and diabetes was present in nearly half of the patients. Primary etiologies of CKD were diabetes mellitus (47.1%) and hypertension (27.8%), followed by glomerulonephritis (3.3%), and polycystic kidney disease (2.1%). Etiology could not be detected in 11.0% of the patients. Various causes of CKD, such as nephrolithiasis, vasculitis, uric acid nephropathy, vesicoureteral reflux, pyelonephritis, Alport syndrome, renal tumor, and amyloidosis were reported in the remaining 8.7% of the patients.

Table 1

General characteristics of the patients according to the center.

	All Centers n = 1066	Center A n = 447	Center B n = 398	Center C n = 221	p-value
Age (years)	65.5±13.7	67.3±12.3	65.9±13.4	61.0±15.8	<0.001
Gender (male), n (%)	517 (48.5)	219 (49.0)	167 (42.0)	131 (59.3)	<0.001
DM, n (%)	502 (47.1)	237 (53.0)	182 (45.7)	83 (37.6)	0.001
Malignancy, n (%)	77 (7.2)	25 (5.6)	30 (7.5)	22 (10.0)	0.117
Menopause, n (%)	486 (88.5)	207 (90.8)	209 (90.5)	70 (77.8)	0.002
PHD, n (%)	24 (2.3)	5 (1.1)	11 (2.8)	8 (3.6)	0.084
Hgb (g/dL)	11.8±1.8	11.6±1.9	12.0±1.7	11.7±1.8	<0.001
Htc (%)	36.0±5.4	36.3±5.7	36.3±5.0	35.1±5.4	0.024
MCV (fL)	86.1±6.0	87.0±6.1	85.6±5.6	84.9±6.3	<0.001
Hgb (g/dL)
Hgb<10, n (%)	159 (14.9)	86 (19.2)	38 (9.5)	35 (15.8)
10≤Hgb<12, n (%)	437 (41.0)	184 (41.2)	160 (40.2)	93 (42.1)	<0.001
Hgb≥12, n (%)	470 (44.1)	177 (39.6)	200 (50.3)	93 (42.1)
Creatinine (mg/dL)	2.2±1.2	2.3±1.3	1.9±1.1	2.3±1.4	<0.001
eGFR (mL/min/1.73m2)	35.8±14.2	33.4±13.7	38.6±13.6	35.7±15.2	<0.001
eGFR
Stage 3 (30–59)	694 (65.1)	262 (58.6)	294 (73.9)	138 (62.4)	<0.001
Stage 4 (15–29)	279 (26.2)	138 (30.9)	82 (20.6)	59 (26.7)
Stage 5 (<15)	93 (8.7)	47 (10.5)	22 (5.5)	24 (10.9)
CRP (mg/L)	12.9±23.3	14.6±25.6	8.6±13.7	17.3±29.9	<0.001
iPTH (pg/mL)	137.8±151.6	144.1±137.4	115.2±108.2	175.2±243.4	<0.001
Iron (μg/dL)	64.8±30.2	62.9±29.5	67.8±30.4	62.1±31.5	0.012
TIBC (μg/dL)	282.8±80.1	254.1±82.8	310.7±63.4	296.0±83.3	<0.001
TSAT (%)	25.8±20.5	29.9±27.1	22.6±11.2	21.8±12.4	<0.001
TSAT<%20, n (%)	423 (43.6)	175 (39.4)	180 (45.8)	68 (50.7)	0.035
Ferritin (ng/mL)	148.3±225.2	122.6±141.7	164.7±244.9	175.6±321.7	0.001
Ferritin<100 (ng/mL), n (%)	585 (57.5)	266 (59.9)	209 (53.3)	110 (60.8)	0.097
Vit B12 (pg/mL)	369.5±280.6	332.5±292.2	435.2±282.3	312.6±194.9	<0.001
Folate (ng/mL)	8.6±5.4	8.2±4.5	8.7±4.8	9.6±8.7	0.671
Vit B12 use, n (%)	191 (17.9)	85 (19.0)	54 (13.6)	52 (23.5)	0.006
Folate use, n (%)	75 (7.0)	34 (7.6)	28 (7.0)	13 (5.9)	0.715
Iron use, n (%)	278 (26.1)	145 (32.4)	75 (18.8)	58 (26.2)	<0.001
ESA use, n (%)	117 (11.0)	79 (17.7)	9 (2.3)	29 (13.1)	<0.001

Values are presented as mean±standard deviation for the continuous variables and frequency (percentage) for the categorical variables.

CRP: C-reactive protein, DM: diabetes mellitus, eGFR: estimated glomerular filtration rate, ESA: erythropoiesis- stimulating agent, Hgb: hemoglobin, Htc: hematocrit, iPTH: intact parathyroid hormone, MCV: mean corpuscular volume, PHD: primary hematological disease, TIBC: total iron binding capacity, TSAT: transferrin saturation ratio, Vit B12: vitamin B12.

Most of the patients had Stage 3 or 4 CKD. Patient characteristics stratified by the study center are shown in Table 1. There were statistically significant differences between the study centers regarding study parameters. However, the frequency of malignancy, primary hematological disease (PHD), ferritin below 100 ng/mL, folate use, and folate levels were similar between the study centers.

Anemia parameters and the use of erythropoiesis-stimulating agents and iron

Anemia and severe anemia were present in 55.9% and 14.9% of the patients, respectively. The mean Hgb level for the whole cohort was found 11.8±1.8 g/dL. The baseline characteristics of the patients according to Hgb levels are shown in Table 2.

Table 2

Baseline characteristics of the patients according to hemoglobin levels.

	Hg<10 g/dL (n = 159)	10≤Hg<12 g/dL (n = 437)	Hg≥12 g/dL (n = 470)	p-value
Age (years)	66.1±14.2	65.6±13.1	65.2±14.0	0.676
Gender (male), n (%)	63 (39.6)	178 (40.7)	276 (58.7)	<0.001
DM, n (%)	85 (53.5)	223 (51.0)	194 (41.3)	0.003
Malignancy, n (%)	21 (13.2)	27 (6.2)	29 (6.2)	0.007
Menopause, n (%)	84 (87.5)	232 (89.6)	170 (87.6)	0.766
PHD, n (%)	10 (6.3)	8 (1.8)	6 (1.3)	0.005
Hgb (g/dL)	9.2±0.7	11.0±0.6	13.4±1.2	<0.001
Htc (%)	28.4±2.6	33.8±2.1	40.7±3.7	<0.001
MCV (fL)	85.9±7.6	85.4±6.1	86.7±5.3	0.001
Creatinine (mg/dL)	3.0±1.6	2.2±1.3	1.8±0.8	<0.001
eGFR (mL/min/1.73m2)	25.6±13.8	34.3±13.9	40.8±12.2	<0.001
eGFR
Stage 3 (30–59)	48 (30.2)	264 (60.4)	382 (81.3)	<0.001
Stage 4 (15–29)	74 (46.5)	132 (30.2)	73 (15.5)
Stage 5 (<15)	37 (23.3)	41 (9.4)	15 (3.2)
CRP (mg/L)	22.5±31.7	13.2±25.3	9.3±15.5	<0.001
iPTH (pg/mL)	196.2±157.2	140.2±136.5	115.8±158.0	<0.001
Iron (μg/dL)	57.8±35.1	60.9±29.4	71.0±27.9	<0.001
TIBC (μg/dL)	246.8±95.6	279.0±71.1	299.4±77.8	<0.001
TSAT (%)	0.29±0.29	0.24±0.17	0.26±0.19	<0.001
TSAT<20%, n (%)	66 (44.3)	196 (48.6)	161 (38.4)	<0.013
Ferritin (ng/mL)	282.3±378.2	147.4±224.8	103.3±107.2	0.001
Ferritin<100 (ng/ml), n (%)	53 (34.9)	248 (59.0)	284 (63.8)	<0.001
Vit B12 (pg/mL)	410.8±307.4	385.3±297.0	339.5±250.4	0.002
Folate (ng/mL)	8.7±6.4	9.0±6.1	8.2±4.3	0.329
Vit B12 use, n (%)	27 (17.0)	90 (20.6)	74 (15.7)	0.155
Folate use, n (%)	11 (6.9)	38 (8.7)	26 (5.5)	0.176
Iron use, n (%)	67 (42.1)	145 (33.2)	66 (14.0)	<0.001
ESA use, n (%)	56 (35.2)	45 (10.3)	16 (3.4)	<0.001

Values are presented as mean±standard deviation for the continuous variables and frequency (percentage) for the categorical variables.

CRP: C-reactive protein, DM: diabetes mellitus, eGFR: estimated glomerular filtration rate, ESA: erythropoiesis- stimulating agent, Hgb: hemoglobin, Htc: hematocrit, iPTH: intact parathyroid hormone, MCV: mean corpuscular volume, PHD: primary hematological disease, TIBC: total iron binding capacity, TSAT: transferrin saturation ratio, Vit B12: vitamin B12.

According to multi-group comparisons, anemia was associated with the female gender, the presence of diabetes, malignancy and PHD. We also showed the well-known association between decreasing eGFR and the presence of anemia. Fig 1 demonstrates the negative association between the prevalence of anemia and eGFR, indicating that the percentage of the patients with anemia increases while kidney function decreases. The percentage of the patients with Hgb greater than 12 g/dL was significantly higher in Stage 3 than Stage 4 and 5 CKD patients (55.1% vs 26.2% and 16.1%, respectively, p<0.001). Conversely, the percentage of the patients with Hgb<10 g/dL was significantly lower in Stage 3 than Stage 4 and 5 CKD (6.9%vs 26.5% and 39.8%, respectively, p<0.001).

Fig 1

Prevalence of anemia based on staging of CKD.

Regarding iron-related parameters, there was a trend toward higher iron use in patients with anemia. In line with this finding, ferritin levels tended to be higher in patients with anemia, possibly at least partially reflecting the effect of iron treatment. Distribution of the patients with TSAT below 20% grouped according to Hgb levels was also not homogenous.

The use of ESA was also associated with the presence of anemia. We want to point out that ESA was used only by 56 of the 159 patients with severe anemia. Malignancy or PHD was present in 26 of these 159 patients. Additionally, iron deficiency defined as a ferritin level <100 ng/ml and a TSAT level <%20 were present in 28 patients. Therefore, the remaining 53 patients were candidates for ESA treatment.

Binary logistic regression

We used binary logistic regression analysis to examine the independent variables associated with severe renal anemia. According to multivariate forward-stepwise binary logistic regression analysis, gender (p = 0.027; OR: 1.637; 95% CI: 1.058–2.533), eGFR (p<0.001; OR: 0.951; 95% CI: 0.935–0.967), ferritin ≥ 100 ng/ml (p = 0.001; OR: 2.144; 95% CI: 1.368–3.362), iron (p = 0.041; OR: 0.991; 95% CI: 0.983–0.999), and the center (p = 0.005) were independent determinants of severe anemia (Table 3). It should be noted that the CRP level was also associated with severe anemia in logistic regression, with a borderline statistical significance (p = 0.064; OR: 1.008; 95% CI: 1.000–1.016).

Table 3

Covariates associated with severe anemia.

	Estimate	SE	p	OR	95% CI for OR
Center			0,005
A	Reference
B	-0.834	0.262	0.001	0.434	0.260–0.725
C	0.020	0.312	0.950	1.020	0.553–1.880
Gender, Male	Reference
Female	0.493	0.223	0.027	1.637	1.058–2.533
Ferritin < 100	Reference
Ferritin ≥ 100	0,763	0.229	0.001	2.144	1.368–3.362
CRP	0,008	0,004	0,064	1,008	1,000–1,016
Iron	-0.009	0.004	0.041	0.991	0.983–0.999
e-GFR	-0,050	0.009	0.000	0.951	0.935–0.967
Constant	0.142	0.365	0.697	1.153

CI: confidence interval, eGFR: estimated glomerular filtration rate, OR: odds ratio, SE: standard error

Discussion

In this multicenter, cross-sectional study, we evaluated the prevalence, severity, risk factors and treatment of anemia in CKD patients who were not given renal replacement therapy. Anemia and severe anemia were present in 55.9% and 14.9% of the patients, respectively. These data are consistent with previous studies. According to McClellan et al., in CKD patients with an eGFR level below 60 ml/min, the prevalence of anemia and severe anemia was 47.7% and 8.9%, respectively [2]. In another multicenter study, the prevalence of anemia and severe anemia in CKD patients with an eGFR level below 60 ml/min were found to be 38% and 7.5% respectively [12]. In line with previous studies, we observed an association between anemia and decreasing kidney function [2,15].

Additionally, we showed that the female gender, eGFR, serum levels of iron, and ferritin were independent risk factors for severe anemia. Furthermore, the center was an additional independent risk factor for severe anemia. The presence of diabetes mellitus was found to be a risk factor for severe anemia in univariate analysis, but not in multivariate analysis. Previous studies showed that female gender, history of diabetes mellitus, CKD stage, serum transferrin saturation, serum levels of ferritin and iPTH and angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blocking agent (ARB) were risk factors for severe anemia in CKD patients [15-19].

We have found different risk profiles of anemia for different medical centers. We want to point out that the specific nephrology center was found as an independent determinant of anemia according to our logistic regression model. Our study is one of the few studies investigating the effects of the medical center on anemia in CKD. Country-specific differences were previously reported [12]. However, to the best of our knowledge, center effect was not investigated for medical centers located in a single country. We think that the center effect on severe anemia may be due to the socioeconomic differences, along with nutritional characteristics, environmental factors, drug intake (such as ACEI or ARB), and self-care characteristics of the patients. Low socioeconomic background, different nutritional intakes, genetic, and environmental factors have been previously identified as risk factors for anemia [20-23]. These data from the literature suggest that interventions and iron intake guidelines should be tailored to regional, nutritional, and socioeconomic subgroups.

We also revealed that a substantial proportion of the potential candidates for ESA treatment were not using ESA. We suggest two potential explanations. First, our cross-sectional study might not capture forthcoming treatments. Second, “therapeutic inertia” might have a role. The therapeutic inertia is a well described concept in patients with CKD and this phenomenon might also be in charge in our cases [24].

There were several limitations to our study. First, a cross-sectional design is unable to capture the treatment effect as efficient as a longitudinal study. Another limitation was the use of three different laboratories and the lack of standardization between laboratories. Finally, we did not assess the rates of blood transfusions and ACEI and ARB treatment. Additionally, generalizability of our results regarding anemia prevalence to a larger scale might be limited because of center-based differences.

In conclusion, we found a large prevalence of anemia among CKD patients who were not given RRT, and the burden of patients who require treatment with erythropoietin is considerably large. We found that some of these patients did not receive ESA treatment. Thus, there is a need to improve the timing of anemia intervention and the quality of care for these patients. Clinicians should be aware of this risk, identify and work up the anemic patients, and implement appropriate therapy. According to our results, center specific management of anemia seems to be important.

Baseline characteristics of the patients grouped according to the presence of severe anemia.

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24 Jan 2020

PONE-D-19-32342

Prevalence of anemia in predialysis chronic kidney disease: is the study center significant?

PLOS ONE

Dear Dr Seyahi,

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There are conflicts between the reviews, I decided to ask you for  major revisions.

In my opinion the article is interest but there are many papers already published on this topic. So authors should strengthen the manuscript providing more significant references, in order to reinforce the methodology part. Also provide references for "Anemia definition". The population among different centers is quite heterogeneous, authors should address this in the statistical analysis.

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Reviewer #1: This work is very good to look for the anemia in CKD Patients in different stages improving of the anemia for sure will have a benefit to the health service. By improving the anemia many chronic illness will be avoided.

Reviewer #2: Article is interested but need to clarify few points;

1. [ABSTRACT]; Title and even apart of objective indicated that centre and or centres based treatment / services will affect the treatment, but abstract results didn’t indicated anything like that. I will suggest to incorporate related findings, if possible. Otherwise, rephrase title or objective part………………………………………………………………………………………………………………..

2. [INTRODUCTION]; Introduction last sentence is different than title and even with abstract objective…………………………………………………………………………………………………………………………...

3. This section is not sufficient to provide enough background of the objectives, it is suggested to elaborate……………………………………………………………………………………………………………………….

4. What was the inclusion criteria for this current project………………………………………………..…….

5. To determine the incidence by getting this 6-7 months data from THREE centres are sufficient..? Shall we generalized this Incidence at country level…?........................................

6. Baseline data included: sex, age, causes of kidney disease, presence of menopause, diabetes mellitus, primary hematologic disease, and malignancy… Why only these factors, how about hypertension?.............................................................................................................................

7. Provide reference for the statement of “Anemia was defined by hemoglobin (Hgb) level below 12g/dL and severe anemia was defined by Hgb level below 10g/dl”………………………….

8. MDRD formula is practicing in the centre or any other reason to select this formula……………

9. Table 1; It would be looking more better than existing , if author convert this into descriptive statistics, as three centres comparison might not be that much fruitful sound. But, Still authors like to keep as it is then they have to provide all three centres background. I mean all three centres are associated with tertiary level referral hospitals and how much bed size and CKD population are there. Further, where these centres are located, and the general population….?.......................................................................................................................

10. It is suggested to avoid abbreviations in the heading/sub-heading, like “ Anemia Parameters and the use of ESA and Iron”………………………………………………………………………………..

11. Table 2; P supposed to be p-value…………………………………………………………………………

12. Conclusion should be more specific based on study findings………………………………..

Reviewer #3: This multicenter cross-sectional study is presented in an intelligible fashion, although it needs some minor English revision.

The authors state that the center is an independent risk factor for anemia in patients affected by predialysis chronic kidney disease. Anyway it is clear from the manuscript that the population among the centers is very dishomogeneus, with statistical significant differences in relevant characteristics such as gender distribution, eGFR, ferritin and iron blood level.

How the authors address this in their statistical analysis?

The following are minor comments, based on STROBE checklist:

9) BIAS, in the methods section, potential sources of bias are not addressed

10) Explain how you arrived at this study size

12.a) you should be more precise to describe the logistic regression: you decide to exclude patients with malignacy and PHD form the analysis. this should be stated in the methods section and the choice must be justified. the criteria used to select the variable for the multivariate analysis must be specified in the methods.

12.c) how do you handle missing data?

16) the presentation of the results of the binary logistic regression is not clear: OR (and not just p value should be reported in the text). You specified a reference level of ferritin < 100, but no other reference level for other continuous variable such as CRP e GFR and Iron are present.

18) your results shows that ESA is underused, this is interesting and should be cited in the discussion. Moreover, is there any significant difference among centers on this point?

20) the authors interpretate the impact of treatment center on anemia as due to the socio-economical differences in the population. it sounds strange if we think that the three center are located in the same area. Please clarify.

Reviewer #4: The authors have conducted an observational study to study prevalence of and management of anemia in non-dialysis dependent CKD patients. First the title of the study is very vague. Methodology section is very weak, there is no explanation of study design, study instrument, recruitment of patients, inclusion exclusion criteria, methodological flowchart etc. Statistical methods used in current study are very basic and gives very basic information. Authors could have performed more analysis especially for comparison of management in different centers with respect to patient outcomes. There are hundreds of articles on this topic online, i feel this authors literature review is bit weak. I would suggest them to read relevant articles and modify their paper accordingly.

**********

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Reviewer #1: Yes: faissal a. m. shaheen

Reviewer #2: Yes: AMER HAYAT KHAN

Reviewer #3: No

Reviewer #4: No

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Submitted filename: Plos 1 , Reviewer 2020-1, Anemia among Pre CKD patients.docx

Click here for additional data file.

15 Feb 2020

February 13, 2020

Martina Crivellari

Academic Editor

PLOS ONE

Dear Editor,

We would like to thank you very much for your valuable review, which gave us the opportunity to improve our manuscript, titled “Prevalence of anemia in predialysis chronic kidney disease: is the study center a significant factor?”, MS number PONE-D-19-32342. In line with your suggestions, we made an extensive literature search to locate relevant and new references. We incorporated those literature to our revised manuscript and specifically provided references for “the definition of anemia”. Finally, we tried to present our statistical analysis more clearly and extensively in order to explain how we addressed the heterogenous population among different centers.

We sincerely appreciate the useful and expert suggestions that helped us to prepare the revised manuscript. We thank you for your consideration and remain.

Yours truly,

Nurhan Seyahi, MD

Selma Alagoz, MD

PONE-D-19-32342

Prevalence of anemia in predialysis chronic kidney disease: is the study center a significant factor?

PLOS ONE

RESPONSE TO REVIEWERS

REVIEWER # 1

This work is very good to look for the anemia in CKD Patients in different stages improving of the anemia for sure will have a benefit to the health service. By improving the anemia many chronic illness will be avoided.

Response : We thank the reviewer for those appreciative comments.

REVIEWER # 2

Article is interested but need to clarify few points;

1. [ABSTRACT]; Title and even apart of objective indicated that centre and or centres based treatment / services will affect the treatment, but abstract results didn’t indicated anything like that. I will suggest to incorporate related findings, if possible. Otherwise, rephrase title or objective part…….

Response: In the results section of the original abstract, we mentioned that nephrology center was an independent determinant of severe anemia (page 1, paragraph 3, line 6-7). Following the comments of the reviewer, we incorporated additional information to underline this subject in the revised manuscript (page 1, paragraph 3, line 3-5).

2. [INTRODUCTION]; Introduction last sentence is different than title and even with abstract objective ……..

Response: Following the comments of the reviewer, we reedited the last part of the introduction to clarify the objective (page 2, paragraph 3, line 5-6).

3. This section is not sufficient to provide enough background of the objectives, it is suggested to elaborate……

Response: In line with the comments of the reviewer, we provided additional information for the background of the objectives (page 2, paragraph 2, line 5-13; page 2, paragraph 3, line 1-3). We also included additional references to reinforce this section (reference no: 9,10,11).

4. What was the inclusion criteria for this current project…………

Response: As we wanted to reveal the frequency of anemia in a center, we aimed to recruit all adult consecutive stage G3-G5 CKD patients.

5. To determine the incidence by getting this 6-7 months data from THREE centres are sufficient..? Shall we generalized this Incidence at country level…?....

Response: We agreed with the reviewer on his/her remark. In line with his/her comments, we did not generalize our incidence data to our country in the original manuscript. On the other hand, we recruited all patients to reveal the incidence of anemia in those centers.

Following the comments of the reviewer, we added information about the limitation of generalizability of our results (page 11, paragraph 4, line 5-6).

6. Baseline data included: sex, age, causes of kidney disease, presence of menopause, diabetes mellitus, primary hematologic disease, and malignancy… Why only these factors, how about hypertension?..

Response: We focused to collect data on the factors related to anemia. Unfortunately, we did not collect data about the prevalence of hypertension. However, we mentioned about the frequency of hypertension as a case of CKD.

7. Provide reference for the statement of “Anemia was defined by hemoglobin (Hgb) level below 12g/dL and severe anemia was defined by Hgb level below 10g/dl”…

Response: We added relevant paper to the reference list in the revised manuscript (reference no: 6).

8. MDRD formula is practicing in the centre or any other reason to select this formula………

Response: We used the MDRD formula to estimate GFR, because, according to a previous study performed in our unit, the MDRD formula performed better than the other estimation equations, including CKD-EPI (Altiparmak MR, Ren Fail. 2013;35(8):1116-23. doi: 10.3109/0886022X.2013.817278).

9. Table 1; It would be looking more better than existing , if author convert this into descriptive statistics, as three centres comparison might not be that much fruitful sound. But, Still authors like to keep as it is then they have to provide all three centres background. I mean all three centres are associated with tertiary level referral hospitals and how much bed size and CKD population are there. Further, where these centres are located, and the general population….?..................

Response: Following the comments of the reviewer, we wanted to keep the Table 1 as it is, and then we provided background information for all tree centers in the relevant sections of the revised manuscript (page 3, paragraph 1, line 2-7).

10. It is suggested to avoid abbreviations in the heading/sub-heading, like “Anemia Parameters and the use of ESA and Iron”……

Response: We are sorry for this inconvenience; we corrected the abbreviations.

11. Table 2; P supposed to be p-value………………………………………

Response: The reviewer is correct in his/her assumption and we made the necessary change in the revised manuscript.

12. Conclusion should be more specific based on study findings………

Response: In our original manuscript, we made specific recommendations in the conclusion section regarding anemia management, such as improving the timing of anemia intervention and increasing the awareness of the clinicians.

We think that our results are not robust enough to make more specific recommendations. However, as suggested by the reviewer, there is a need for more specific recommendations regarding the management of CKD-related anemia in predialysis patients. We hope the results of our study will address this unmet need.

REVIEWER # 3

This multicenter cross-sectional study is presented in an intelligible fashion, although it needs some minor English revision.

Response: In the revised manuscript, the whole text was reedited by a native English speaker.

The authors state that the center is an independent risk factor for anemia in patients affected by predialysis chronic kidney disease. Anyway, it is clear from the manuscript that the population among the centers is very dishomogeneous, with statistically significant differences in the relevant characteristics such as gender distribution, eGFR, ferritin and iron blood levels.

How the authors address this in their statistical analysis?

Response: The reviewer correctly stated that there are statistical differences between the populations in different centers regarding the variables that were clinically associated with the presence of anemia. Therefore, in our original manuscript, we constructed a multivariate model that used those variables as the determinants of anemia (page 9, paragraph 4, line 1-7) (Table 3). After appropriate statistical correction, ‘center’ was defined as an independent risk factor for the presence of anemia. Following the comments of the reviewer, we included additional information to clarify this issue in the revised manuscript (page 4, paragraph 1, line 8-17).

The following are minor comments, based on STROBE checklist:

9) BIAS, in the methods section, potential sources of bias are not addressed

Response: Following the suggestion of the reviewer, we added bias part in the original manuscript (page 3, paragraph 3, line 1-5).

10) Explain how you arrived at this study size

Response: We did not perform a formal sample size calculation. We intended to recruit all consecutive eligible patients for a duration of seven months.

12.a) You should be more precise to describe the logistic regression: you decide to exclude patients with malignacy and PHD form the analysis. this should be stated in the methods section and the choice must be justified. the criteria used to select the variable for the multivariate analysis must be specified in the methods.

Response: We wanted to examine the factors associated with anemia of CKD and we excluded the patients with malignancy and primary hematological disease, since both conditions were known to be associated with anemia (reference no: 14). Following the comment of the reviewer, we moved the relevant information to the Methods section in the revised manuscript (page 4, paragraph 1, line 8-17).

12.c) How do you handle missing data?

Response: We didn’t fill-in or impute the missing values in the statistical methods, but rather omitted it. The frequency of missing data was <10% (ferritin 5%; iron 8%; CRP 2%) in the clinical parameters regarding anemia.

16) the presentation of the results of the binary logistic regression is not clear: OR (and not just p value should be reported in the text). You specified a reference level of ferritin < 100, but no other reference level for other continuous variable such as CRP e GFR and Iron are present.

Response: According to the current guidelines, a ferritin level of above 100 ng/mL reflects the presence of appropriate iron stores (National Kidney Foundation. K/DOQI clinical practice guidelines for anemia of chronic kidney disease. 2000. Am J Kidney Dis 2001;37(Suppl 1):182-238). Therefore, we used a cut-off value for ferritin. However, to the best of our knowledge, there is no definite cut-off value for CRP and eGFR that indicates renal anemia. Following the comments of the reviewer, OR data was added to the manuscript.

18) your results shows that ESA is underused, this is interesting and should be cited in the discussion. Moreover, is there any significant difference among centers on this point?

Response: We appreciate this comment. In line with this suggestion, we discussed about underused ESA treatment and the possible causes (page 11, paragraph 3, line 1-5).

20) the authors interpretate the impact of treatment center on anemia as due to the socio-economical differences in the population. it sounds strange if we think that the three center are located in the same area. Please clarify.

Response: The three centers are located in the same geographical region, namely Marmara. However, one of them is located in a different city called Kocaeli. The remaining two centers are located in different districts of Istanbul which is a very large city with a population of nearly 16 million inhabitants. There are well defined socioeconomical differences between different districts of Istanbul.

REVIEWER #4

The authors have conducted an observational study to study prevalence of and management of anemia in non-dialysis dependent CKD patients. First the title of the study is very vague. Methodology section is very weak, there is no explanation of study design, study instrument, recruitment of patients, inclusion exclusion criteria, methodological flowchart etc. Statistical methods used in current study are very basic and gives very basic information. Authors could have performed more analysis especially for comparison of management in different centers with respect to patient outcomes. There are hundreds of articles on this topic online, i feel this authors literature review is bit weak. I would suggest them to read relevant articles and modify their paper accordingly.

Response: In our revised manuscript, we made changes according to the specific comments of the academic editor and other reviewers. We added additional information to reinforce methodology and clarify study design (page 3, paragraph 1, line 2-7; page 3, paragraph 3, line 1-5; page 4, paragraph 1, line 8-17). We used the appropriate statistical methods to analyze our data including multivariate logistic regression and multigroup comparison with ANOVA test, which are well beyond the basic level. If there is any further specific statistical method that the reviewer suggests, we will be happy to incorporate them to our study in order to make a more sound analysis. Following the comments of the reviewer, we added additional references to reinforce our literature review (reference no: 9,10,11,14,24) and made appropriate modifications.

Submitted filename: Response the reviewers .docx

Click here for additional data file.

13 Mar 2020

Prevalence of anemia in predialysis chronic kidney disease: is the study center a significant factor?

PONE-D-19-32342R1

Dear Dr. Seyahi,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Martina Crivellari

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

18 Mar 2020

PONE-D-19-32342R1

Prevalence of anemia in predialysis chronic kidney disease: is the study center a significant factor?

Dear Dr. Seyahi:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

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on behalf of

Dr. Martina Crivellari

Academic Editor

PLOS ONE