Literature DB >> 29162909

Association of serum sodium and risk of all-cause mortality in patients with chronic kidney disease: A meta-analysis and sysematic review.

Liguang Sun1, Yue Hou2, Qingfei Xiao2, Yujun Du3.   

Abstract

Studies on the association of dysnatraemia with all-cause mortality risk in chronic kidney disease (CKD) patients have yielded inconsistent results. This meta-analysis aimed to evaluate the association of hyponatremia or hypernatremia with all-cause mortality risk in CKD patients. An electronic literature search was performed in Web of Science, Pubmed and Embase databases from inception to March 2017 for available observational studies evaluating the association of dysnatraemia with all-cause mortality risk in CKD patients. Pooled hazard risk (HR) with 95% confidence interval (CI) was calculated for hyponatremia or hypernatremia vs. normonatremia. Seven studies that enrolled 742,979 CKD patients were identified. Baseline hyponatremia (HR 1.34; 95% CI: 1.15-1.57), and not hypernatremia (HR 1.12; 95%: CI 0.93-1.34), was independently associated with increased risk of all-cause mortality, when compared than the normonatremia category. In time-dependent analyses, both time-averaged hyponatremia (HR 1.65; 95% CI: 1.27-2.15) and hypernatremia (HR 1.41; 95% CI: 1.20-1.65) had a higher independent risk of all-cause mortality. Furthermore, subgroup analyses by type of patients, study design, sample size and follow-up duration revealed similar results across most of these analyses. Baseline hyponatremia and time-dependent hyponatremia or hypernatremia were independently associated with increased all-cause mortality risk in CKD patients.

Entities:  

Mesh:

Substances:

Year:  2017        PMID: 29162909      PMCID: PMC5698308          DOI: 10.1038/s41598-017-16242-3

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


Introduction

Chronic kidney disease (CKD) is an increasing global public health concern[1]. End-stage renal disease (ESRD) is the chronic and progressive decline in kidney function. More than two million people suffer from ESRD worldwide[2]. Furthermore, the number of patients with ESRD receiving maintenance hemodialysis or peritoneal dialysis continues to increase worldwide[3]. Given that the mortality rate of CKD patients with or without dialysis is unacceptably high[4,5], early risk stratification for mortality is crucial in these populations. Dysnatremia is the most common electrolyte abnormality in clinical practice. Clinically, normal serum sodium level in humans ranges within 135–144 mmol/L[6]. Patients with CKD tend to develop dysnatremia mainly due to their diminished ability to maintain water homeostasis[7]. Hyponatremia and hypernatremia are relatively frequent electrolyte abnormalities in patients with advancing stages of CKD, who are undergoing dialysis[8]. Several[9-17] but not all[18,19] epidemiologic studies reported that hyponatremia is associated with increased all-cause mortality in no-dialysis CKD and maintenance dialysis patients. Similarly, the association of hypernatremia with all-cause mortality risk has also yielded inconsistent results[9,11-14,19]. To the best of our knowledge, no meta-analysis has addressed the association of baseline and time-dependent dysnatremia with subsequent all-cause mortality risk among CKD patients. Given the conflicting findings in these available studies, we conducted a meta-analysis to investigate whether dysnatremia (hyponatremia and hypernatremia) was an independent predictor of all-cause mortality in CKD patients with or without dialysis.

Results

Literature search and study characteristics

The initial literature search yielded 585 articles. Among these retrieved articles, 578 articles were excluded for different reasons (Fig. 1). Thus, seven studies[9-14,19] that comprised of 742,979 CKD patients were included in the meta-analysis. Table 1 presents the baseline characteristics of the included studies. Among the seven articles, four studies[9,11,14,19] were limited to non-dialysis CKD patients, two studies[10,12] enrolled hemodialysis patients, and one study[13] included peritoneal dialysis patients. Furthermore, five studies[9,10,12-14] were conducted in the United States, Korea[19] and the United Kingdom[11], in which each country contributed one study. All seven studies followed an observational design, in which four studies were retrospective cohorts[9,12-14] and three studies were prospective cohorts[11,19]. Furthermore, four studies[9-11,14,19] defined hyponatremia as serum sodium <136 mmol/L and three studies[10,12,13] defined hyponatremia as serum sodium <135 mmol/L. The cut-off definition of hypernatremia was ≥144 mmol/L and ≥145 mmol/L in these included studies. All included studies were deemed as high quality, with a rank of 6-8 stars in the Newcastle–Ottawa Scale (NOS) (Table 2).
Figure 1

Flow chart of the study selection process.

Table 1

Summary of clinical studies included in meta-analysis.

Study/yearCountryDesignType of patientsBaseline eGFRSample size (% male)Age/range Mean ± SDDysnatraemia definition/numberNumber of death/HR (95% CI)Follow-up durationAdjustment for covariates
Kovesdy et al. 2012[9] USARetrospective cohort studyNon-dialysis CKD55.2 ± 19.3 ml/min/1.73 m2 655, 493 (97.2)73.9 ± 9.8Na < 136 mmol/L (85,855); Na > 145 mmol/L (350)Total death: 193,956 Baseline-Na 1.06 (1.03–1.10); L 1.02 (0.93–1.11); H TA-Na 1.32 (1.15–1.51); L 1.20 (1.10–1.31); H5.5 yearsAge, gender, race, geographic location, DM, CVD, CHF, liver disease, malignancy, depression, CCI, SBP, eGFR, serum albumin, AKP, AST, ALT, total bilirubin, hemoglobin, glucose, and WBC
Nigwekar et al.2013[10] USAProspective cohort studyHD10.5 ± 6.2ml/min/1.73 m2 6,053 (54.4)62.5 ± 15.2Na < 135 mmol/L (775)Total death: 965 Baseline-Na 1.42(1.19–1.69)12 monthsAge, race, sex, DM, hypertension, CAD, catheter access, facility mortality statistic, BMI, serum albumin, bicarbonate level.
Han et al. 2015[19] KoreaProspective cohort studyNon-dialysis CKD25.5 ± 10.7 ml/min/1.73 m2 2,141 (55.2)63.5 ± 14.9Na ≤ 135 mmol/L (135); Na ≥ 144 mmol/L (350)Total death: 1821.8 yearsAge, gender, race, eGFR, SBP, hemoglobin, CVD, serum albumin
Baseline-Na
1.74 (0.84–3.60); L
2.01(1.21–3.34); H
TA-Na
0.93 (0.44–1.97); L
1.53 (0.92–2.55); H
Chiu et al. 2016[11] UKProspective cohort studyNon-dialysis CKD32.8 ± 15.9 ml/min/1.73 m2 2,093 (62.6)56–75Na ≤ 135 mmol/L (142);Total death: 68441 monthsAge, gender, smoking, DM, previous MI, heart failure, SBP, eGFR, serum albumin, use of renin-angiotensin blocker or diuretics
Na ≥ 145 mmol/L (134)Baseline-Na
1.35 (1.02–1.78); L
1.15 (0.84–1.57); H
TA-Na
2.15 (1.59–2.91); L
1.47 (0.93–2.38); H
Rhee et al. 2016[12] USARetrospective studyHD27,180 (57)63 ± 15Na < 136 mmol/L (2,501);Total death: 7,5621.4 yearsAge, sex, race/ethnicity, primary insurance, vascular access, comorbidities, IDWG, Kt/V, residual urea clearance, BMI, serum albumin, creatinine, total iron binding capacity, ferritin, iron saturation, bicarbonate, PTH, calcium, phosphorus, hemoglobin, glucose, WBC, BUN, and normalized protein catabolic rate.
Na ≥ 144 mmol/L (549)Baseline-Na
1.39 (1.21–1.59); L
0.84 (0.71–0.99); H
TA-Na
1.64(1.34–2.02); L
1.47 (1.26–1.71); H
Ravel et al. 2016[13] USARetrospective studyPD4,687 (55)58 ± 15Na < 136 mmol/L (399);Total death: 64911.9 monthsAge, sex, race/ethnicity, primary insurance, baseline comorbidities, serum albumin, creatinine, total iron binding capacity, calcium, phosphorus, PTH, ferritin, iron saturation, hemoglobin, WBC, peritoneal Kt/v, renal Kt/V, use of automated PD during the baseline quarter or anytime
Na ≥ 144 mmol/L (170)Baseline-Na
1.48 (1.14–1.92); L
1.03 (0.65–1.65); H
TA-Na
1.52(1.22–1.89); L
1.17 (0.73–1.88); H
Huang et al. 2017[14] USARetrospective studyNon-dialysis CKD48.0 ± 10.2 ml/min/1.73 m2 45,333 (44.6)71.9 ± 11.9Na < 136 mmol/L (3,626);Total death: 11,7153.6 yearsAge, gender, smoking, BMI group, eGFR, DM, hypertension, cerebrovascular disease, CAD, CHF, hyperlipidemia, malignancy, ACEI/ARB, beta blocker, diuretics, albumin, hemoglobin, serum bicarbonate and liver disease
Na > 145 mmol/L (532)Baseline-Na
1.39 (1.32–1.48); L
1.31 (1.14–1.51); H
TA-Na
2.24 (2.14–2.35); L
1.66 (1.44–1.91); H

Abbreviations: HD, hemodialysis; PD, peritoneal dialysis; L, low; H, high; HR, hazard ratio; CI, confidence interval; CVD, cardiovascular disease; CAD, coronary artery disease; CHF, congestive heart failure; DM, diabetes mellitus; BMI, body mass index; CKD, chronic kidney disease; MI, myocardial infarction; eGFR, estimated glomerular filtration rate; WBC, white blood cell count; CCI, Charlson Comorbidity Index; BUN, blood urea nitrogen; PTH, parathyroid hormone; TC, total cholesterol; TG, triglyceride; SBP, systolic blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; AKP, alkaline phosphatase; TA-Na,time-averaged serum sodium; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.

Table 2

quality assessment of studies included in meta-analysis

Study/YearRepresentativeness of the exposed cohortSelection of the non exposed cohortAscertainment of exposureDemonstration that outcome was not present at study startComparability of cohorts on the basis of the design or analysisAssessment of outcomeEnough follow-up periods (≥3 years)Adequacy of follow-up of cohortsOverall NOS
Kovesdy et al. 2012 9 8
Nigwekar et al. 2013[10] 6
Han et al. 2015[19] 6
Chiu et al. 2016[11] 7
Rhee et al. 2016[12] 7
Ravel et al. 2016[13] 7
Huang et al. 2017[14] 8
Flow chart of the study selection process. Summary of clinical studies included in meta-analysis. Abbreviations: HD, hemodialysis; PD, peritoneal dialysis; L, low; H, high; HR, hazard ratio; CI, confidence interval; CVD, cardiovascular disease; CAD, coronary artery disease; CHF, congestive heart failure; DM, diabetes mellitus; BMI, body mass index; CKD, chronic kidney disease; MI, myocardial infarction; eGFR, estimated glomerular filtration rate; WBC, white blood cell count; CCI, Charlson Comorbidity Index; BUN, blood urea nitrogen; PTH, parathyroid hormone; TC, total cholesterol; TG, triglyceride; SBP, systolic blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; AKP, alkaline phosphatase; TA-Na,time-averaged serum sodium; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker. quality assessment of studies included in meta-analysis

The association of hyponatremia with all-cause mortality

All the included studies[9-14,19] evaluated the association of baseline hyponatremia and risk of all-cause mortality. In the time-dependent analyses of hyponatremia, six studies[9,11-14,19] assessed this association. As shown in Fig. 2A, there was significant heterogeneity across these included studies (I 2 = 92.9%, P < 0.001). The meta-analysis revealed that patients with baseline hyponatremia were associated with a higher risk of all-cause mortality (hazard ratio [HR]: 1.34; 95% confidence interval [CI]: 1.15–1.57) compared with patients with normonatremia in a random effects model. Furthermore, there was no evidence of publication bias according to Begg’s test (P = 0.368) and Egger’s test (P = 0.151). The sensitivity analysis, in which an individual study was remove at a time, revealed that pooled HR varied within 1.32–1.40 and low 95% CI varied within 1.11–1.33. Similarly, patients with time-averaged hyponatremia was associated with higher risk of all-cause mortality (HR: 1.65; 95% CI: 1.27–2.15; Fig. 2B) compared with patients with normonatremia in a random effects model, and there was significant heterogeneity across studies (I 2 = 92.8%; P < 0.001). Begg’s test (P = 1.000) and Egger’s test (P = 0.117) did not reveal any evidence of publication bias. The sensitivity analysis indicated that pooled HR varied within 1.54–1.78 and low 95% CI varied within 1.16–1.41.
Figure 2

Forest plots showing HR and 95% CI of all-cause mortality comparing baseline hyponatraemia (A) or time-averaged hyponatraemia (B) to normonatremia in a random effect model.

Forest plots showing HR and 95% CI of all-cause mortality comparing baseline hyponatraemia (A) or time-averaged hyponatraemia (B) to normonatremia in a random effect model.

The association of hypernatremia and all-cause mortality

Six studies[9,11-14,19] reported the association of baseline and time-averaged hypernatremia with all-cause mortality risk. The meta-analysis revealed that patients with baseline hypernatremia were not significantly associated with increased risk of all-cause mortality (HR: 1.12; 95% CI: 0.93–1.34; Fig. 3A) compared with patients with normonatremia in a random effects model. Furthermore, there was significant heterogeneity across these included studies (I 2 = 78.6%; P < 0.001). Moreover, Begg’s test (P = 0.707) and Egger’s test (P = 0.523) suggested no evidence of publication bias. The sensitivity analysis revealed that pooled HR varied within 1.06–1.17 and low 95% CI varied within 0.88–0.99. In the time-dependent analysis of hypernatremia, patients with time-averaged hypernatremia was associated with higher risk of all-cause mortality (HR: 1.41; 95% CI: 1.20–1.65; Fig. 3B) compared with patients with normonatremia in a random effects model, and there was substantial heterogeneity across these studies (I 2 = 70.8%; P = 0.004). Furthermore, there was no evidence of publication bias based on the results of Begg’s test (P = 1.000) and Egger’s test (P = 0.522). The sensitivity analysis indicated that pooled HR varied within 1.32–1.54 and low 95% CI varied within 1.13–1.40.
Figure 3

Forest plots showing HR and 95% CI of all-cause mortality comparing baseline hypernatraemia (A) or time-averaged hypernatraemia (B) to normonatremia in a random effect model.

Forest plots showing HR and 95% CI of all-cause mortality comparing baseline hypernatraemia (A) or time-averaged hypernatraemia (B) to normonatremia in a random effect model.

Subgroup analyses

The stratified analysis revealed similar results across most of the subgroups, indicating a consistent association between dysnatremia and all-cause mortality (Tables 3 and 4).
Table 3

Subgroup analysis of hyponatraemia and all-cause mortality.

SubgroupNo. of studiesPooled HR95%CIHeterogeneity between studies
1. Baseline hyponatraemia
Sample sizes
 ≥10,00031.261.02–1.57p < 0.001; I2 = 97.3%;
 <10,00041.431.26–1.62p = 0.915; I2 = 0.0%
Study design
 Prospective31.411.22–1.63p = 0.810; I2 = 0.0%;
 Retrospective41.31.07–1.58p < 0.001; I2 = 96.1%
Follow-up duration
 ≥2 years31.240.99–1.56p < 0.001; I2 = 97.0%
 <2 years41.421.28–1.56p = 0.921; I2 = 0.0%
Type of patients
 Dialysis31.411.28–1.56p = 0.914; I2 = 0.0%
 Non-dialysis CKD41.271.02–1.59p < 0.001; I2 = 95.6%
2. Time-averaged hyponatraemia
Sample sizes
≥10,00031.71.17–2.48p < 0.001; I2 = 96.5%;
<10,00031.611.14–2.29p = 0.057; I2 = 65.1%
Study design
 Prospective21.520.68–3.42p = 0.042; I2 = 75.8%;
 Retrospective41.661.21–2.26p < 0.001; I2 = 95.4%
Follow-up duration
≥2 years31.841.24–2.73p < 0.001; I2 = 96.1%
<2 years31.551.33–1.80p = 0.349; I2 = 5.0%
Type of patients
 Dialysis21.581.36–1.84p = 0.620; I2 = 0.0%
 Non-dialysis CKD41.681.16–2.44p < 0.001; I2 = 94.7%

Abbreviations: CKD, chronic kidney disease; HR, hazard ratio; CI, confidence interval; NOS, Newcastle-Ottawa Scale.

Table 4

Subgroup analysis of hypernatraemia and all-cause mortality.

SubgroupNo. of studiesPooled HR95%CIHeterogeneity between studies
1. Baseline hypernatraemia
Sample sizes
 ≥10,00031.040.84–1.30p < 0.001; I2 = 88.1%;
 <10,00031.300.91–1.86p = 0.117; I2 = 53.5%
Study design
 Prospective21.460.85–2.52p = 0.066; I2 = 70.3%;
 Retrospective41.040.86–1.86p = 0.001; I2 = 82.2%
Follow-up duration
 ≥2 years31.150.95–1.39p = 0.012; I2 = 77.3%
 <2 years31.150.70–1.89p = 0.008; I2 = 80.9%
Type of patients
 Dialysis20.860.74–1.01p = 0.419; I2 = 0.0%
 Non-dialysis CKD41.230.99–1.52p = 0.003; I2 = 78.8%
2. Time-averaged hypernatraemia
Sample sizes
 ≥10,00031.421.16–1.75p < 0.001; I2 = 87.8%;
 <10,00031.371.04–1.82p = 0.707; I2 = 0.0%
Study design
 Prospective21.51.06–2.12p = 0.910; I2 = 0.0%;
 Retrospective41.391.15–1.68p = 0.001; I2 = 82.0%
Follow-up duration
 ≥2 years31.421.08–1.85p = 0.001; I2 = 86.6%
 <2 years31.451.26–1.66p = 0.650; I2 = 0.0%
Type of patients
 Dialysis21.441.24–1.66p = 0.368; I2 = 0.0%
 Non-dialysis CKD41.431.13–1.81p = 0.002; I2 = 80.3%

Abbreviations: CKD, chronic kidney disease; HR, hazard ratio; CI, confidence interval; NOS, Newcastle-Ottawa Scale.

Subgroup analysis of hyponatraemia and all-cause mortality. Abbreviations: CKD, chronic kidney disease; HR, hazard ratio; CI, confidence interval; NOS, Newcastle-Ottawa Scale. Subgroup analysis of hypernatraemia and all-cause mortality. Abbreviations: CKD, chronic kidney disease; HR, hazard ratio; CI, confidence interval; NOS, Newcastle-Ottawa Scale.

Discussion

This is the first meta-analysis that investigated the association of dysnatremia with all-cause mortality risk. From the seven included observational studies, we found that baseline hyponatremia and time-dependent hyponatremia or hypernatremia were independently associated with increased risk of all-cause mortality in CKD patients. The risk of all-cause mortality for CKD patients who have time-dependent hyponatremia or hypernatremia increased by 41% and 65%, respectively; while the risk of all-cause mortality for CKD patients who had hyponatremia at baseline increased by 34%. However, the association of baseline hypernatremia with all-cause mortality was not statistically significant. Taken together, our results suggest that the association of serum sodium level with all-cause mortality appeared to exhibit a U-shaped trend in CKD patients. Therefore, dysnatremia may have potential in mortality risk stratification among these patients. A previous meta-analysis revealed that moderate hyponatremia was associated with an increased 1.60-fold risk of overall mortality[20]. However, this meta-analysis enrolled patients with myocardial infarction, heart failure, cirrhosis, pulmonary infection, or mixed diseases, and did not concentrate on CKD patients. Our meta-analysis was specially focused on CKD patients. We found that both hyponatremia and hypernatremia were strongly associated with higher risk of all-cause mortality when analyzed in a time-dependent manner. However, the combined risk estimate was not statistically significant when measured as a single baseline sodium level. Particularly, time-averaged hypernatremia was more strongly associated with all-cause mortality than single baseline hypernatremia. Water and sodium removal were almost exclusively determined by the dialysis procedure in ESRD patients. CKD patients undergoing dialysis are prone to develop dysnatremia. Therefore, the severity of the kidney disease may affect the mortality associated with dysnatraemia. In our subgroup analyses, patients undergoing dialysis exhibited a relatively higher all-cause mortality (HR: 1.27 vs. 1.41) associated with baseline hyponatremia, compared with patients with non-dialysis CKD. In contrast, the stage of CKD did not appear to affect the mortality associated with hypernatremia. The prevalence of hyponatremia was significantly higher at all stages of CKD than the prevalence of hypernatremia[6]. A number of studies[15-17,21] that examined the association of serum sodium with all-cause mortality risk have focused largely on hyponatremia. Irrespective of the severity of kidney disease, all studies found that hyponatremia was associated with increased risk of all-cause mortality. Each 4 mmol/L increase in baseline sodium level was associated with 19–28% lower risk of all-cause mortality in maintenance hemodialysis patients[22,23]. As for the noted U-shape association of sodium level with mortality in CKD patients, elevated serum sodium levels also conferred to higher risk of all-cause mortality. The above mentioned studies selected the highest sodium level as the reference control. Thus, the association between hyponatremia and mortality in these studies might have been underestimated. The exact mechanism of dysnatraemia in all-cause mortality risk in CKD patients remains unclear. Kidneys are responsible for maintaining water homeostasis, and CKD could magnify the effect of dysnatremias on their clinical consequences[24,25]. Both hyponatremia and hypernatremia can have direct adverse effects on the function of various organs, including the brain, heart, or musculoskeletal system; and subsequently, increase mortality. For patients with renal disease, hyponatremia at baseline and time-dependent hyponatremia or hypernatremia had higher risk of mortality. Dysnatremia may be a potential target for correction for clinicians. However, whether correct dysnatraemia could improve outcomes should be evaluated in future studies. Several limitations of this meta-analysis should be acknowledged. First, the number of included studies was relatively limited. Thus, the results of these subgroup analyses may not be robust. Second, there was statistical heterogeneity in the quantitative pooling outcome. Potential explanations for heterogeneity included the severity of the disease, follow-up duration and study design. Third, the measurement of serum sodium levels at a single time point could have resulted in the misclassification of dysnatremia due to the fluctuation of sodium values between dialysis sessions. However, time-averaged sodium analysis further confirmed the association of dysnatremia with all-cause mortality. A mean value of several monthly measurements of sodium may have provided more accurate results of dysnatremia. Fourth, the adjusted confounding factors were different across these included studies, and the lack of adjustment for these confounding factors may have slightly overestimated the risk estimate. Fifth, the lack of information on the dialysis regimen of these included studies was a potential limitation. Finally, this meta-analysis could not distinguish the effect of dysnatremia on peritoneal dialysis and hemodialysis. In conclusion, baseline hyponatremia and time-dependent hyponatremia/hypernatremia are independently associated with increased all-cause mortality risk in CKD patients. This meta-analysis suggests the U-shaped association of dysnatremia with all-cause mortality risk in CKD patients. However, whether correcting dysnatremia can reduce mortality risk in CKD patients needs to be investigated in the randomized controlled trials.

Methods

Literature Search

The present meta-analysis was conducted in accordance with the checklists of the Meta-analysis Of Observational Studies in Epidemiology statement[26]. Two authors (LG Sun and Y Hou) independently searched the Web of Science, PubMed and Embase databases for available observational studies from inception to March 2017. The following search terms were used: (chronic kidney disease OR end-stage renal disease OR hemodialysis OR peritoneal dialysis) AND (sodium OR hyponatraemia OR hypernatremia OR dysnatraemia) AND (mortality OR death). In addition, a manual search of the reference lists of all relevant articles was performed to identify any additional eligible publications.

Study Selection

Studies that satisfied the following criteria were eligible: (1) prospective or retrospective observational studies; (2) study populations that comprised of dialysis or non-dialysis CKD patients; (3) the exposure was baseline and time-averaged hyponatremia or hypernatremia; (4) the outcome measure was all-cause mortality; (5) the reported multiple adjusted HR and 95% CI compared hyponatremia or hypernatremia with normonatremia. Exclusion criteria: (1) studies that used the highest serum sodium level as reference controls; (2) serum sodium levels were used as the continuous variable; (3) studies that had a follow-up duration of less than six months.

Data extraction and quality assessment

Two authors independently abstracted the following information into standardized forms: surname of the first author, publication year, country of origin, study design, sample size, percentage of male patients, age at enrollment, baseline estimated glomerular filtration rate (eGFR), hyponatremia or hypernatremia definition and number of patients, number of deaths, the most fully adjusted risk estimate, follow-up duration, and adjustment for potential cofounders. Any differences in opinion in the data extraction were resolved by discussion. In evaluating for methodological quality, the NOS was used for these cohort studies[27]. The NOS was based on the following three aspects: selection, comparability and outcome. When using this scale, a total score of ≥7 stars was deemed of high quality.

Data analysis

The pooled risk estimate was calculated through the category of hyponatremia or hypernatremia vs. normonatremia in these individual studies. Heterogeneity was explored using the Cochrane Q test and I 2 statistic. Statistical heterogeneity was set at an I 2 statistic of ≥50% and/or Q test of P < 0.10. In the presence of statistical heterogeneity, studies were pooled using a random effect model. Otherwise, a fixed-effect model was used. Subgroup analyses were performed according to the type of patients (dialysis vs. non-dialysis CKD), study design (prospective vs. retrospective), sample size (≥10,000 vs. <10,000) and follow-up duration (≥2 years vs. <2 years). A sensitivity analysis was conducted to investigate the impact of these individual studies on the overall results by removing one study at each turn. Potential publication bias was explored using Begg’s test and Egger’s test. All statistical analyses were performed using STATA software 12.0 (Stata Corp, College Station, Texas).
  26 in total

1.  Mortality associated with low serum sodium concentration in maintenance hemodialysis.

Authors:  Sushrut S Waikar; Gary C Curhan; Steven M Brunelli
Journal:  Am J Med       Date:  2011-01       Impact factor: 4.965

2.  Dialysate sodium, serum sodium and mortality in maintenance hemodialysis.

Authors:  Finnian R Mc Causland; Steven M Brunelli; Sushrut S Waikar
Journal:  Nephrol Dial Transplant       Date:  2011-09-02       Impact factor: 5.992

Review 3.  Significance of hypo- and hypernatremia in chronic kidney disease.

Authors:  Csaba P Kovesdy
Journal:  Nephrol Dial Transplant       Date:  2012-03       Impact factor: 5.992

4.  Atherosclerotic cardiovascular disease risks in chronic hemodialysis patients.

Authors:  A K Cheung; M J Sarnak; G Yan; J T Dwyer; R J Heyka; M V Rocco; B P Teehan; A S Levey
Journal:  Kidney Int       Date:  2000-07       Impact factor: 10.612

Review 5.  Maintenance Dialysis throughout the World in Years 1990 and 2010.

Authors:  Bernadette Thomas; Sarah Wulf; Boris Bikbov; Norberto Perico; Monica Cortinovis; Karen Courville de Vaccaro; Abraham Flaxman; Hannah Peterson; Allyne Delossantos; Diana Haring; Rajnish Mehrotra; Jonathan Himmelfarb; Giuseppe Remuzzi; Christopher Murray; Mohsen Naghavi
Journal:  J Am Soc Nephrol       Date:  2015-07-24       Impact factor: 10.121

Review 6.  Dysnatremias in patients with kidney disease.

Authors:  Sara Combs; Tomas Berl
Journal:  Am J Kidney Dis       Date:  2013-11-14       Impact factor: 8.860

7.  Hyponatremia, mineral metabolism, and mortality in incident maintenance hemodialysis patients: a cohort study.

Authors:  Sagar U Nigwekar; Julia Wenger; Ravi Thadhani; Ishir Bhan
Journal:  Am J Kidney Dis       Date:  2013-04-13       Impact factor: 8.860

8.  Pre-dialysis serum sodium and mortality in a national incident hemodialysis cohort.

Authors:  Connie M Rhee; Vanessa A Ravel; Juan Carlos Ayus; John J Sim; Elani Streja; Rajnish Mehrotra; Alpesh N Amin; Danh V Nguyen; Steven M Brunelli; Csaba P Kovesdy; Kamyar Kalantar-Zadeh
Journal:  Nephrol Dial Transplant       Date:  2015-09-25       Impact factor: 5.992

Review 9.  Disorders of body fluids, sodium and potassium in chronic renal failure.

Authors:  W E Mitch; C S Wilcox
Journal:  Am J Med       Date:  1982-03       Impact factor: 4.965

10.  Hyponatremia as a predictor of mortality in peritoneal dialysis patients.

Authors:  Tae Ik Chang; Yung Ly Kim; Hyungwoo Kim; Geun Woo Ryu; Ea Wha Kang; Jung Tak Park; Tae-Hyun Yoo; Sug Kyun Shin; Shin-Wook Kang; Kyu Hun Choi; Dae Suk Han; Seung Hyeok Han
Journal:  PLoS One       Date:  2014-10-29       Impact factor: 3.240

View more
  14 in total

1.  Hypernatremia in Hospitalized Patients: A Large Population-Based Study.

Authors:  Soraya Arzhan; Maria-Eleni Roumelioti; Igor Litvinovich; Cristian G Bologa; Orrin B Myers; Mark L Unruh
Journal:  Kidney360       Date:  2022-04-20

2.  Clinical predictors of hyponatremia in patients with heart failure according to severity of chronic kidney disease.

Authors:  Ivan Velat; Željko Bušić; Viktor Čulić
Journal:  Wien Klin Wochenschr       Date:  2022-05-17       Impact factor: 2.275

3.  Predictors of Mortality in Patients with Chronic Heart Failure: Is Hyponatremia a Useful Clinical Biomarker?

Authors:  Manal M Alem
Journal:  Int J Gen Med       Date:  2020-07-20

4.  Osmotic stress and mortality in elderly patients with kidney failure: a retrospective study.

Authors:  Caroline Grangeon-Chapon; Manuella Dodoi; Vincent Lm Esnault; Guillaume Favre
Journal:  Clin Interv Aging       Date:  2019-01-30       Impact factor: 4.458

5.  Distinct osmoregulatory responses to sodium loading in patients with altered glycosaminoglycan structure: a randomized cross-over trial.

Authors:  Eliane F E Wenstedt; Jetta J Oppelaar; Stijn Besseling; Nienke M G Rorije; Rik H G Olde Engberink; Arie Oosterhof; Toin H van Kuppevelt; Bert-Jan H van den Born; Jan Aten; Liffert Vogt
Journal:  J Transl Med       Date:  2021-01-20       Impact factor: 5.531

6.  Hyponatremia as a prognostic factor in non-small cell lung cancer: a systematic review and meta-analysis.

Authors:  Birgitte Sandfeld-Paulsen; Ninna Aggerholm-Pedersen; Anne Winther-Larsen
Journal:  Transl Lung Cancer Res       Date:  2021-02

7.  Serum Sodium Levels Predict Mortality in Elderly Acute Kidney Injury Patients: A Retrospective Observational Study.

Authors:  Qinglin Li; Yan Wang; Zhi Mao; Hongjun Kang; Feihu Zhou
Journal:  Int J Gen Med       Date:  2021-02-25

8.  Laboratory correlates of SARS-CoV-2 seropositivity in a nationwide sample of patients on dialysis in the U.S.

Authors:  Shuchi Anand; Maria E Montez-Rath; Jialin Han; Pablo Garcia; Julie Bozeman; Russell Kerschmann; Paul Beyer; Julie Parsonnet; Glenn M Chertow
Journal:  PLoS One       Date:  2021-04-15       Impact factor: 3.752

9.  Development and Validation of a Predictive Model for Chronic Kidney Disease After Percutaneous Coronary Intervention in Chinese.

Authors:  Ying Zhang; Jianlong Wang; Guangyao Zhai; Yujie Zhou
Journal:  Clin Appl Thromb Hemost       Date:  2022 Jan-Dec       Impact factor: 2.389

10.  Prevalence and Prognostic Significance of Hyponatremia in Patients With Lung Cancer: Systematic Review and Meta-Analysis.

Authors:  Eszter Bartalis; Marin Gergics; Benedek Tinusz; Mária Földi; Szabolcs Kiss; Dávid Németh; Margit Solymár; Zsolt Szakács; Péter Hegyi; Emese Mezösi; László Bajnok
Journal:  Front Med (Lausanne)       Date:  2021-12-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.