Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis

Abstract

Objective

To evaluate the role of bioimpedance-defined overhydration (BI-OH) parameters in predicting the risk of mortality and cardiovascular (CV) events in patients undergoing dialysis.

Methods

We searched multiple electronic databases for studies investigating BI-OH indicators in the prediction of mortality and CV events through 23 May 2020. We assessed the effect of BI-OH indexes using unadjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Sensitivity analysis was used for each outcome.

Results

We included 55 studies with 104,758 patients in the meta-analysis. Extracellular water/total body water (ECW/TBW) >0.4 (HR 5.912, 95% CI: 2.016–17.342), ECW/intracellular water (ICW) for every 0.01 increase (HR 1.041, 95% CI: 1.031–1.051), and OH/ECW >15% (HR 2.722, 95% CI: 2.005–3.439) increased the risk of mortality in patients receiving dialysis. ECW/TBW >0.4 (HR 2.679, 95% CI: 1.345–5.339) and ECW/ICW per increment of 10% (HR 1.032, 95% CI: 1.017–1.047) were associated with an increased risk of CV events in patients undergoing dialysis. A 1-degree increase in phase angle was a protective factor for both mortality (HR 0.676, 95% CI: 0.474–0.879) and CV events (HR 0.736, 95% CI: 0.589–0.920).

Conclusions

BI-OH parameters might be independent predictors for mortality and CV events in patients undergoing dialysis.

Keywords

Bioimpedance-defined overhydration mortality cardiovascular event dialysis meta-analysis outcome

Introduction

As a renal replacement therapy, renal dialysis is, in principle, a selective treatment for renal dysfunction or renal diseases that includes peritoneal dialysis (PD) or hemodialysis (HD).¹ Following a rapid increase in dialysis use over a period of approximately two decades, the incidence of dialysis initiation in most high-income countries reached a peak in the early 2000s and has remained stable or has decreased slightly since then.² However, mortality remains unacceptably high among patients on dialysis, especially in the first 3 months following initiation of HD treatment. According to the 2019 Annual Data Report from the U.S. Renal Data System, the annual mortality was 156 per 1000 patient-years for patients undergoing PD and 167 patients for those receiving HD in the United States.³

Overhydration (OH) is relatively common among patients receiving dialysis, with an incidence of 56.5% to 73.1%.^4–6 Observational studies have shown an association between OH and mortality in patients receiving dialysis.^7,8 Therefore, it is essential to objectively measure patients’ hydration status to obtain a more clearly defined assessment of prognosis in patients on dialysis. Common clinical approaches, such as measuring weight changes and the isotope dilution method, have certain limitations, which have led to the development of bioimpedance analysis (BIA).^9–11 Bioimpedance-defined overhydration (BI-OH) indicators have been suggested to predict mortality risk and cardiovascular (CV) events in patients receiving dialysis.^9,12 Previous studies have indicated that phase angle (PA) level is linked to a decreased risk of death among patients undergoing PD or HD.^13,14 Other evidence suggests that a higher extracellular water (ECW)/intracellular water (ICW) ratio, ECW/total body water (TBW) ratio >0.4, and overhydration (OH)/ECW ratio >15% are independent risk factors for mortality and CV events in patients undergoing HD or PD treatment.^15–18 However, Rhee et al. and Shin et al. demonstrated that a 1-degree increase in PA was not associated with increased risk of mortality and CV events in patients undergoing dialysis, without statistical significance.^14,19 A post-hoc study from a cross-sectional survey by Guo et al. found that ECW/TBW >0.4 had no effect on CV events, with P>0.05.²⁰

A newly published meta-analysis predicted the risk of death in patients with renal and heart failure using a 1-degree decrease in PA and OH/ECW >15%.¹² Nevertheless, the role of specific OH parameters in predicting the risk of death and CV events in patients receiving dialysis remains unclear. To further clarify the correlation between OH parameters measured using BIA and the above clinical outcomes in patients on dialysis, we conducted a meta-analysis adding measures such as ECW/TBW >0.4 and ECW/ICW per every 0.01 increase, as well as subgroup analysis of dialysis methods and literature quality.

Methods

In this meta-analysis, approval of the Institutional Review Board and informed consent were not required. Our study was performed and documented according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines (Supplementary Material). The supplementary material describes the methods of this study in detail. The present study was approved by the Open Science Framework Registries (https://osf.io/registries), registration number 10.17605/OSF.IO/H2KJ4.

Literature search strategy

We performed a search of the published literature in the PubMed, Embase, Cochrane Library, and Web of Science databases up to 23 May 2020. The keywords in the search strategy were as follows: dialysis, renal dialysis, renal insufficiency, chronic kidney failure, and electric impedance. The search strategy is presented in Supplementary Table 1.

Inclusion and exclusion criteria

The inclusion criteria were as follows. i) Patients with renal diseases including chronic renal insufficiency, end-stage renal disease, renal failure, and other renal diseases, and were undergoing PD or HD. ii) BIA and its parameter indexes were ECW/TBW >0.4,⁹ ECW/ICW, OH/ECW>15%,²¹ and PA. iii) The outcomes were unadjusted hazard ratio (HR) of mortality (main outcome) and unadjusted HR of CV events (secondary outcome). When multiple follow-up time points of the outcome event were reported, the final follow-up time point at which the outcome event occurred was included in the analysis. iv) Cohort studies.

Exclusion criteria were: i) animal studies; ii) non-English language international publications; iii) studies with unavailable data; and iv) case reports, meeting abstracts, meta-analyses, reviews, or editorials.

Data extraction and quality assessment

The literature was reviewed and the research data were extracted by two researchers (Yajie Wang and Zejuan Gu) according to the inclusion and exclusion criteria. In the case of a conflict, the point of disagreement was discussed between the two parties until agreement was reached. The following information was collected from the studies: first author, year of publication, country, method of renal replacement therapy, number of patients, age, body mass index, sex, follow-up, primary outcome, secondary outcome, mortality, BIA method, Newcastle–Ottawa scale (NOS) score, and quality assessment score. The quality of the articles was evaluated using the NOS, with scores ranging from 0 to 10. “Low quality” studies were defined as those with scores <5 and those with scores ≥5 were considered “high quality” studies.

Statistical analysis

The data were evaluated using unadjusted HRs and 95% confidence intervals (CIs) to determine effect sizes. To evaluate heterogeneity for each outcome, random-effects (I²≥50%) and fixed-effects (I²<50%) models were used. When I²≥50% and P<0.05, subgroup analysis was carried out for the dialysis method and literature quality. We performed sensitivity analysis for all outcomes. P<0.05 was considered statistically significant. All analyses were conducted using R studio 4.0.3 (www.r-project.org; The R Foundation for Statistical Computing, Vienna, Austria).

Results

Literature search and study characteristics

In a search of the selected electronic databases, 12,076 studies were initially identified in total. After removing duplicates, 7839 studies for subsequently screened. Finally, 55 studies were included.^5,7,13–65 A flow diagram of the complete search strategy, article screening, and exclusion and inclusion processes in this review are shown in Figure 1.

Figure 1.

Flow chart of the literature search.

There was a total of 104,758 participants in the 55 studies, with follow-up times ranging from 1 to 15 years. A total of 14,624 patients died, with up to 15 years of follow-up. Among the 55 studies, 32 were assessed as high quality and 23 as low quality. The baseline characteristics of the study participants are summarized in Table 1.

Table 1.

Baseline characteristics of included studies.

Author	Year	Country	Dialysis method	N	Age, years	BMI, kg/m²	Male/Female	Follow-up (years)	Primary outcome	Secondary outcome	Mortality	BIA method	NOS score	Quality
Abad²²	2011	Spain	127 HD, 37 PD	164	61.1±14.5	25.3±5.0	99/65	6	Mortality	NA	100	PA	5	HQ
Avram²³	2006	USA	PD	177	54±16	25.4±4.94	73/104	15	Mortality	NA	89	PA	2	LQ
Beberashvili²⁴	2014	Israel	HD	91	64.0±11.5	28.1±5.5	57/34	3	Mortality	NA	38	PA	6	HQ
Beberashvili (1)²⁵	2014	Israel	HD	250	68.7±13.6	26.6± 4.5	158/92	1.4	Mortality	NA	64	PA	5	HQ
Caetano²⁶	2016	Portugal	HD	697	67 (55.5–76)*	(25–29.9)*	394/303	1	Mortality	NA	66	OHI	5	HQ
Chazot⁷	2012	France	HD	158	64.7±13.8	26.9±5.1	78/80	6.5	Mortality	Hypertension	unclear	OHI	4	LQ
Chen²⁷	2007	China	PD	227	59.53±14.37	23.27±3.57	100/127	3	Mortality	NA	58	ECWR	5	HQ
de Araujo²⁸	2012	Brazil	109 HD, 36 PD	145	54.9±15.4	24.7 (21.9–28.7)*	72/73	1.3	CV events	Mortality	13	PA	5	HQ
Dekker²⁹	2017	International	HD	8883	63±14.8	NA	5081/3802	1	Mortality	NA	unclear	OHI	4	LQ
Demirci³⁰	2016	Turkey	HD	493	57.7±13.9	26.1±4.8	253/240	2.3	Mortality	CV mortality	93	BIVA	5	HQ
Di Iorio⁵²	2004	Italy	HD	515	63.62±15.35	24.56±4.45	316/199	1.25	Mortality	NA	75	PA	4	LQ
Fan³¹	2015	UK	PD	183	54.9±15.6	NA	95/88	1.7	Mortality	Technique failure	37	ECWR	4	LQ
Fein⁵³	2002	USA	PD	53	53	NA	17/36	8	Mortality	NA	21	ECWR	3	LQ
Fiedler³²	2009	Germany	HD	90	61±14	NA	53/37	3	Mortality	Hospital admission events	36	PA	4	LQ
Guo⁵	2015	China	PD	307	47.8±15.3	22.7±3.9	132/175	3.2	Mortality	CV mortality	52	ECWR	6	HQ
Hoppe³³	2015	Poland	HD	241	61.9±12.5	26.1±3.9	160/81	2.5	Mortality	NA	42	OHI	1	LQ
Jotterand Drepper³⁴	2016	Germany	PD	54	56.1± 15.5	25.5±3.6	33/21	6.5	Mortality	NA	19	OHI	4	LQ
Kim³⁶	2015	South Korea	HD	240	65.6±12.8	NA	147/93	2	Mortality	Hospital admission events	50	OHI	4	LQ
Kim³⁵	2017	South Korea	HD	77	52.6±12.5	NA	40/37	5	Mortality	CV events	24	ECWR	4	LQ
Koh³⁷	2011	Malaysia	PD	128	48.0±1.2	24.3±0.4	59/69	2.2-2.3	Mortality	NA	35	PA	5	HQ
Maggiore⁵⁴	1996	Italy	HD	131	62.5±13.6	NA	66/65	2.2	Mortality	NA	23	PA	4	LQ
Mathew³⁸	2015	India	85 HD, 14 PD	99	55.26±12.5	22.23±4.2	78/21	2	Mortality	NA	33	OHI	5	HQ
O’Lone⁴⁰	2014	UK	PD	529	57.0 (46.7–68.8)*	NA	329/200	4	Mortality	NA	95	OHI+ECWR	4	LQ
Oei³⁹	2016	UK	PD	336	57.9 (48.1–69.0)*	NA	207/129	2	Mortality	NA	48	OHI	4	LQ
Onofriescu⁴¹	2015	Romania	HD	221	53.8±13.9	25.5±5.0	116/105	5.5	Mortality	CV events	66	OHI	6	HQ
Paniagua⁴²	2010	Mexico	388 HD, 365 PD	753	48.64±17.55	25.22±5.15	415/338	1.4	Mortality	CV mortality	182	ECWR	5	HQ
Paudel⁴³	2015	UK	PD	455	56.1± 0.7	26.8±0.3	278/177	2	Mortality	NA	72	OHI	3	LQ
Pillon⁵⁵	2004	USA	HD	3009	60.5±15.4	NA	1589/1420	1.5	Mortality	NA	361	BIVA	4	LQ
Pupim⁵⁶	2004	USA	HD	194	55.7±15.4	NA	102/92	3	Mortality	CV mortality	50	PA	4	LQ
Rhee⁶⁴	2015	South Korea	PD	129	49.74±10.01	23.59±3.31	80/49	2.1	residual renal function	Mortality	15	ECWR	3	LQ
Segall⁴⁵	2014	Romania	HD	149	53.9±13.7	22.8±8.1	82/67	1.1	Mortality	NA	43	PA	5	HQ
Shin¹⁴	2017	South Korea	HD	142	64±13	22.5 (20.4, 24.9)*	75/67	2.4	Mortality	CV mortality	15	PA	3	LQ
Siriopol⁴⁷	2015	Romania	HD	173	57.9±14.0	NA	85/88	1.8	Mortality	NA	31	OHI	3	LQ
Siriopol (1)⁴⁸	2017	Romania	HD	285	58.9 ±14.1	NA	136/149	3.4	Mortality	NA	89	OHI	5	HQ
Tangvoraphonchai⁴⁹	2016	UK	HD	362	63 (50–76)*	NA	216/146	4.1	Mortality	NA	110	OHI	6	HQ
Tian⁵⁰	2016	China	PD	152	60.5±12.8	24.0±3.8	62/90	5	Mortality	NA	44	ECWR	4	LQ
Wizemann²¹	2009	Poland	HD	269	65±15	25.6±4.7	NA	3.5	Mortality	NA	86	OHI	5	HQ
Zoccali¹⁸	2017	International	HD	39,566	60.9±15.7	27.1±18.5	23593/15973	1.4	Mortality	NA	5866	OHI	5	HQ
Arrigo⁵⁷	2018	Switzerland	HD	144	73 (59–81)*	NA	88/56	1	Mortality	NA	27	OHI	5	HQ
Avram⁵⁸	2010	USA	PD	62	54±16	NA	28/34	8	Mortality	NA	21	ECWR	6	HQ
Bansal⁵⁹	2018	USA	HD	3751	58.1±11.6	29.0±5.9	2047/1704	7	Mortality	CV events	776	PA	5	HQ
Beberashvili⁶⁰	2010	Israel	HD	81	64.3±11.9	28.3±5.6	53/28	2.2	Mortality	NA	22	PA	5	HQ
Ng¹⁵	2018	China	PD	311	58.8±12.2	24.9±4.3	172/139	2.2	Mortality	CV events	81	OHI+ECWR	4	LQ
Hecking⁶¹	2018	Germany	HD	38,614	60.9±15.7	25.9±5.3	23011/15603	1	Mortality	NA	5640	OHI	7	HQ
Huang⁶²	2018	China	HD	178	60.9±11.6	23.9±3.8	87/91	2.7	Mortality	CV mortality	24	OHI	5	HQ
Huang¹³	2019	China	PD	760	45.2±14.5	22.5±3.2	465/295	2	Mortality	CV events	125	PA	5	HQ
Kim¹⁷	2018	South Korea	HD	142	64±13	23.4±8.5	75/67	2.4	Mortality	CV events	15	ECWR	6	HQ
Mushbick⁶³	2003	USA	PD	48	51±15	25.7±5.0	25/23	2	Mortality	NA	8	PA	3	LQ
Rhee¹⁹	2018	South Korea	HD	208	65.19±12.9	22.62±3.21	208/0	1	Mortality	NA	84	ECWR+PA	6	HQ
Voroneanu⁶⁵	2014	Romania	HD	98	55.4±13.2	NA	49/49	2	Mortality	CV mortality	16	N- proBNP and hs–cTnT	5	HQ
Yajima¹⁶	2019	Japan	HD	234	65.1±12.6	22.0±3.8	162/72	2.8	Mortality	CV mortality	72	ECWR	6	HQ

*Indicates extreme value.

Notes: References⁵ and²⁰ had the same study population, but the follow-up time was different; thus the basic data in reference²⁰ were not included in Table 1. This was similar for references⁴⁴ and⁴⁵, and references^47,48, and⁴⁶.

Values are mean±standard deviation or (range).

PD, peritoneal dialysis; HD, hemodialysis; N, number of samples; BMI, body mass index; CV, cardiovascular; BIA, bioelectrical impedance analysis; BIVA, bioelectrical impedance vector analysis; ECWR: extracellular water ratio; PA, phase angle; hs-cTnT, high-sensitivity cardiac T troponin; NT–pro-BNP, N-terminal pro–B-type natriuretic peptide; OHI, overhydration index; NOS, Newcastle–Ottawa scale; LQ: low quality; HQ, high quality; TBW, total body water; ICW, intracellular water; ECW, extracellular water; NA, not available.

Qualitative analysis of included studies

Regarding dialysis methods, HD treatment was used in 31 of 55 studies and PD treatment in 16 studies; 4 studies used a combination of these two treatments. For the bioimpedance method, 18 of 55 studies reported the overhydration index (OHI) method, 16 studies the PA method, 11 studies the extracellular water expressed as a ratio (ECWR) method, 2 studies the bioimpedance vector analysis method, and 2 studies reported the OHI+ECWR method; only 1 study reported the PA+ECWR method and 1 study the N-terminal pro–B-type natriuretic peptide+high-sensitivity cardiac T troponin method.

Studies were divided into primary outcomes and secondary outcomes. Death was the primary outcome in 49 studies, CV events was the primary outcome in 1 study, and residual renal function was the primary outcome in 1 study. Secondary outcome events were reported in 20 articles, mainly CV death (8 articles), CV disease (6 articles), hospitalization events (2 articles), death (2 articles), hypertension (1 article), and technical failure (1 article).

Risk of mortality

There was no heterogeneity among two included studies;^5,17 therefore, we used a fixed-effects model for the analysis (I²=0.0%). We found that an ECW/TBW ratio >0.4 was a significant risk factor for mortality, with HR (95% CI) 5.912 (2.016–17.342), P=0.001 (Table 2).

Table 2.

Results of overall meta-analysis.

Characteristics	HR (95% CI)	P	I²
Mortality
ECW/TBW >0.4
Overall	5.912 (2.016–17.342)	0.001	0.0
Sensitivity analysis	5.912 (2.016–17.342)
ECW/ICW for every increase by 0.01
Overall	1.041 (1.031–1.051)	<0.001	45.7
Sensitivity analysis	1.041 (1.031–1.051)
1-degree increase in PA
Overall	0.676 (0.474–0.879)	<0.01	73.6
Sensitivity analysis	0.676 (0.474–0.879)
Dialysis method
HD	0.749 (0.511–0.986)	<0.05	78.5
PD	0.488 (0.225–0.751)	<0.05	0.0
Quality assessment
High quality	0.686 (0.467–0.905)	<0.05	78.3
Low quality	0.560 (0.021–1.099)	>0.05	NA
OH/ECW >15%
Overall	2.722 (2.005–3.439)	<0.001	97.3
Sensitivity analysis	2.722 (2.005–3.439)
Dialysis method
HD	2.265 (1.602–2.929)	<0.05	96.9
PD	7.820 (6.183–9.457)	<0.05	NA
Quality assessment
High quality	1.833 (1.259–2.407)	<0.05	90.7
Low quality	3.835 (2.548–5.122)	<0.05	92.7
Cardiovascular events
ECW/TBW >0.4
Overall	2.679 (1.345–5.339)	0.005	0.0
Sensitivity analysis	2.679 (1.345–5.339)
ECW/ICW for every increase by 0.01
Overall	1.032 (1.017–1.047)	<0.001	49.2
Sensitivity analysis	1.032 (1.017–1.047)
1-degree increase in PA
Overall	0.736 (0.589–0.920)	0.007	0.0
Sensitivity analysis	0.736 (0.589–0.920)

OH, overhydration; TBW, total body water; ICW, intracellular water; ECW, extracellular water; PA, phase angle; PD, peritoneal dialysis; HD, hemodialysis; HR, hazard ratio; NA, not applicable; CI, confidence interval.

Two studies^16,35 on ECW/ICW (per increment of 0.01) showed no remarkable heterogeneity (I²=45.7%). The result indicated that each 0.1-unit increase in the ECW/ICW ratio could independently predict the mortality risk: HR (95% CI) 1.041 (1.031–1.051), P<0.001 (Table 2).

Considerable heterogeneity was present after combining six studies^{13,14,19,24,25,37} (I²=73.6%); therefore, a random-effects model was used for the analysis. A 1-degree increase in PA was found to be a protective factor against death: HR (95% CI) 0.676 (0.474–0.879), P<0.01 (Table 2; Figure 2a). However, owing to the significant heterogeneity among the six studies, subgroup analysis was conducted for the dialysis method and quality assessment. In terms of dialysis method, a 1-degree increase in PA was associated with a reduced risk of death in patients receiving PD (HR 0.488, 95% CI: 0.225–0.751, P<0.05) and HD (HR 0.749, 95% CI: 0.511–0.986, P<0.05) treatment (Table 2; Figure 2b). The same result was observed in both the high-quality articles (HR 0.686, 95% CI: 0.467–0.905, P<0.05) and low-quality articles (HR 0.560, 95% CI: 0.021–1.099) (Table 2; Figure 2c).

Figure 2.

Forest plots of mortality among patients receiving dialysis with a 1-degree increase in phase angle. (a) Overall analysis; (b) subgroup analysis for dialysis method; (c) subgroup analysis for quality assessment.

In eight studies,^{7,18,21,26,29,34,36,41} OH/ECW >15% was an independent risk factor for death: HR (95% CI): 2.722 (2.005–3.439), P<0.001 (Table 2; Figure 3a). A subgroup analysis was conducted for the dialysis method and quality assessment with large heterogeneity (I²=97.3%). Results of the subgroup analysis showed that OH/ECW >15% was closely related to the risk of death in patients undergoing HD (HR 2.265, 95% CI: 1.602–2.929, P<0.01) and PD (HR 7.820, 95% CI: 6.183–9.457, P<0.05) treatment (Table 2; Figure 3b). The same result was found for high-quality studies (HR 1.833, 95% CI: 1.259–2.407, P<0.05) and low-quality studies (HR 3.835, 95% CI: 2.548–5.122, P<0.05) (Table 2; Figure 3c).

Figure 3.

Forest plots of mortality among patients receiving dialysis with overhydration/extracellular water ratio >15% (a) overall analysis; (b) subgroup analysis for dialysis method; (c) subgroup analysis for quality assessment.

Risk of CV events

There were two studies on ECW/TBW ratio >0.4,^5,17 two studies on ECW/ICW (per increment of 0.01),^16,35 and three studies on PA.^13,14,31 Among them, ECW/TBW >0.4 (HR 2.679, 95% CI: 1.345–5.339, P=0.005) and every 0.01 unit increment in ECW/ICW ratio (HR 1.032, 95% CI: 1.017–1.047, P<0.001) were considered risk factors for CV events whereas a 1-degree increase in PA (HR 0.736, 95% CI: 0.589–0.920, P=0.007) emerged as a protective factor against CV events (Table 2).

Sensitivity analysis and publication bias assessment

To determine the effect of individual studies on HRs, we carried out sensitivity analysis for each outcome. The results revealed that removing each study did not remarkably affect the overall HR, and the results of this meta-analysis were reliable and robust (Table 2). Additionally, there were fewer than nine studies included for each indicator in our study, which did not conform to the standard of publication bias.

Discussion

Recently, there has been increasing evidence that fluid overload is frequently present in a substantial number of patients receiving dialysis.⁶ More than one-third of incident patients undergoing dialysis, who are considered euvolemic or dehydrated on clinical assessment, have fluid overload using BIA measurement.⁴ Therefore, it is critical to make an accurate assessment of hydration status in this patient population. In our meta-analysis, four electronic databases were comprehensively searched to clarify the role of BI-OH markers in predicting the risk of mortality and CV events for patients receiving HD and PD. A total of 55 studies including 104,758 participants were identified. Among the BI-OH indices, ECW/TBW >0.4 and ECW/ICW (per increment of 10%) were found to be risk factors for mortality and CV events. Moreover, OH/ECW >15% was related to a reduced risk of death. Additionally, a 1-degree increase in PA emerged as a protective factor against mortality and CV events. All results suggested that multiple BI-OH parameters are associated with the risk of mortality and CV events, which may provide practical information to predict clinical outcomes among patients receiving dialysis.

Of note, there were various indices used to evaluate hydration status when using BIA to measure the risk of mortality. The ECW/TBW ratio was frequently used whereas OH/ECW and ECW/ICW were less frequently adopted. ECW/TBW ratios among patients were consistent, although the absolute values of ECW and TBW were different,⁹ thus leading to the wide use of ECW/TBW. Multiple studies showed that the ECW/TBW ratio as a risk factor independently predicted mortality.^5,15,17 In 529 patients undergoing PD, O'Lone et al. found that this ratio as a continuous variable was not associated with increased mortality.⁴⁰ Kang et al. conducted a retrospective study of 631 unselected incident patients on PD and concluded that a higher overload index (ECW/TBW >0.37) was associated with an increase in mortality;⁶⁶ the ECW/TBW ratio showed a slightly significant difference. According to deviations from the normal value, a possible explanation may be patients’ nutritional status (including age and sex). The study by Shu et al.⁶⁷ showed that ECW/ICW (per increment of 10%) remained a risk factor after adjusting obesity, age, sex, ethnicity, and other confounders, which would make the results more reliable and consistent with our results. PA level is calculated using BIA measurements as the arc tangent of the reactance-to-resistance ratio. Our results have been further confirmed in a previous study.¹² A meta-analysis also indicated that a 1-degree decrease in PA level is considered a risk factor of mortality,¹² indirectly suggesting results that are consistent with our results. In a single-center, retrospective study by Rhee et al. including 208 patients with acute kidney injury, a 1-degree increase in PA did not show any statistical significance in the prediction of in-hospital mortality (P>0.05).⁶⁴ The reason may be related to the large number of studies included in our study and different study populations.

Another clinical outcome, CV events, can also occur in patients on dialysis. BI-OH indices are important predictors of their occurrence and development, which can serve to predict the risk of CV events. Prior studies have confirmed that every 0.1-unit increase in the ECW/ICW ratio and ECW/TBW >0.4 are risk factors for CV events, which is supported by Ng et al.¹⁵ In contrast to our results, CV events were found to be associated with PA in one study, mainly owing to a small number of patients and other confounding factors.¹⁴ PA cutoff values should be obtained for routine assessment and improving outcomes in patients receiving dialysis, but these outcomes were rarely determined in the included studies.

Our study has several strengths as follows. First, this meta-analysis included a considerable number of studies with large sample sizes (from 48 to 39,566), which may make our results more generalizable and reliable. Second, identical criteria were applied between studies in classifying BI-OH methods, which did not limit further meta-analyses and subgroup analyses for each hydration indicator. The main limitation of our meta-analysis was the absence of publication bias. The reason may be the inclusion of fewer than 9 studies for each indicator in our study, which does not conform to the standard of publication bias. Unadjusted HRs were used in our cohort studies, which may be a source of bias.

Conclusions

Our results showed that ECW/TBW >0.4, ECW/ICW (per each 0.1-unit increase), and OH/ECW >15% were risk factors for mortality in patients receiving dialysis. ECW/TBW >0.4 and ECW/ICW (per increment of 10%) were associated with an increased risk of CV events; a 1-degree increase in PA was a protective factor against mortality and CV events.

Supplemental Material

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Supplemental material, sj-pdf-1-imr-10.1177_03000605211031063 for Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis by Yajie Wang and Zejuan Gu in Journal of International Medical Research

Supplemental Material

sj-pdf-2-imr-10.1177_03000605211031063 - Supplemental material for Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis

Supplemental material, sj-pdf-2-imr-10.1177_03000605211031063 for Effect of bioimpedance-defined overhydration parameters on mortality and cardiovascular events in patients undergoing dialysis: a systematic review and meta-analysis by Yajie Wang and Zejuan Gu in Journal of International Medical Research

Footnotes

Declaration of conflicting interest

The authors declare that there is no conflict of interest.

Funding

This work was supported by the Project of “Nursing Science” Funded by the Priority Discipline Development Program of Jiangsu Higher Education Institutions (General Office, the People’s Government of Jiangsu Province [2018] No. 87), and grants from the Jiangsu Provincial Medical Innovation Team (CXTDA2017019).

ORCID iD

Zejuan Gu

References

Himmelfarb

Vanholder

Mehrotra

, et al. The current and future landscape of dialysis. Nat Rev Nephrol 2020; 16: 573–585.

Sanderson

Harshman

LA.

Renal replacement therapies for infants and children in the ICU. Curr Opin Pediatr 2020; 32: 360–366.

US Renal Data System 2019 Annual Data Report: Epidemiology of Kidney Disease in the United States. Am J Kidney Dis 2019: S0272-6386(19)31008-X. doi: 10.1053/j.ajkd.2019.09.002.

Ronco

Verger

Crepaldi

, et al. Baseline hydration status in incident peritoneal dialysis patients: the initiative of patient outcomes in dialysis (IPOD-PD study)†. Nephrol Dial Transplant 2015; 30: 849–858.

Guo

Lin

, et al. The Effect of Fluid Overload on Clinical Outcome in Southern Chinese Patients Undergoing Continuous Ambulatory Peritoneal Dialysis. Perit Dial Int 2015; 35: 691–702.

Kwan

Szeto

Chow

, et al. Bioimpedance spectroscopy for the detection of fluid overload in Chinese peritoneal dialysis patients. Perit Dial Int 2014; 34: 409–416.

Chazot

Wabel

Chamney

, et al. Importance of normohydration for the long-term survival of haemodialysis patients. Nephrol Dial Transplant 2012; 27: 2404–2410.

Van Der Sande

Van De Wal-Visscher

Stuard

, et al. Using Bioimpedance Spectroscopy to Assess Volume Status in Dialysis Patients. Blood Purif 2020; 49: 178–184.

Davies

Davenport

The role of bioimpedance and biomarkers in helping to aid clinical decision-making of volume assessments in dialysis patients.

Kidney Int 2014; 86: 489–496.

10.

Cooper

Aslani

Ryan

, et al. Comparing different methods of assessing body composition in end-stage renal failure. Kidney Int 2000; 58: 408–416.

11.

Jaffrin

Morel

Body fluid volumes measurements by impedance: A review of bioimpedance spectroscopy (BIS) and bioimpedance analysis (BIA) methods.

Med Eng Phys 2008; 30: 1257–1269.

12.

Tabinor

Elphick

Dudson

, et al. Bioimpedance-defined overhydration predicts survival in end stage kidney failure (ESKF): systematic review and subgroup meta-analysis. Sci Rep 2018; 8: 4441.

13.

Huang

, et al. Lower Phase Angle Measured by Bioelectrical Impedance Analysis Is a Marker for Increased Mortality in Incident Continuous Ambulatory Peritoneal Dialysis Patients. J Ren Nutr 2020; 30: 119–125.

14.

Shin

Kim

Park

, et al. Predicting clinical outcomes using phase angle as assessed by bioelectrical impedance analysis in maintenance hemodialysis patients. Nutrition 2017; 41: 7–13.

15.

Kwan

Chow

, et al. Asymptomatic fluid overload predicts survival and cardiovascular event in incident Chinese peritoneal dialysis patients. PLoS One 2018; 13: e0202203.

16.

Yajima

Takahashi

, et al. Combined Predictive Value of Extracellular Fluid/Intracellular Fluid Ratio and the Geriatric Nutritional Risk Index for Mortality in Patients Undergoing Hemodialysis. Nutrients 2019; 11: 2659.

17.

Kim

Shin

Hwang

, et al. Monitoring Volume Status Using Bioelectrical Impedance Analysis in Chronic Hemodialysis Patients. ASAIO J 2018; 64: 245–252.

18.

Zoccali

Moissl

Chazot

, et al. Chronic Fluid Overload and Mortality in ESRD. J Am Soc Nephrol 2017; 28: 2491–2497.

19.

Rhee

Baek

Chung

, et al. Extracellular volume expansion and the preservation of residual renal function in Korean peritoneal dialysis patients: a long-term follow up study. Clin Exp Nephrol 2016; 20: 778–786.

20.

Guo

, et al. Prevalence and risk factors of fluid overload in Southern Chinese continuous ambulatory peritoneal dialysis patients. PLoS One 2013; 8: e53294.

21.

Wizemann

Wabel

Chamney

, et al. The mortality risk of overhydration in haemodialysis patients. Nephrol Dial Transplant 2009; 24: 1574–1579.

22.

Abad

Sotomayor

Vega

, et al. The phase angle of the electrical impedance is a predictor of long-term survival in dialysis patients. Nefrologia 2011; 31: 670–676.

23.

Avram

Fein

Rafiq

, et al. Malnutrition and inflammation as predictors of mortality in peritoneal dialysis patients. Kidney Int 2006; 70: S4–S7.

24.

Beberashvili

Azar

Sinuani

, et al. Longitudinal changes in bioimpedance phase angle reflect inverse changes in serum IL-6 levels in maintenance hemodialysis patients. Nutrition 2014; 30: 297–304.

25.

Beberashvili

Azar

Sinuani

, et al. Bioimpedance phase angle predicts muscle function, quality of life and clinical outcome in maintenance hemodialysis patients. Eur J Clin Nutr 2014; 68: 683–689.

26.

Caetano

Valente

Oliveira

, et al. Body Composition and Mortality Predictors in Hemodialysis Patients. J Ren Nutr 2016; 26: 81–86.

27.

Chen

Guo

Wang

Extracellular water/intracellular water is a strong predictor of patient survival in incident peritoneal dialysis patients.

Blood Purif 2007; 25: 260–266.

28.

De Araujo Antunes

Vannini

De Arruda Silveira

, et al. Associations between bioelectrical impedance parameters and cardiovascular events in chronic dialysis patients. Int Urol Nephrol 2013; 45: 1397–1403.

29.

Dekker

MJE

Marcelli

Canaud

, et al. Impact of fluid status and inflammation and their interaction on survival: a study in an international hemodialysis patient cohort. Kidney Int 2017; 91: 1214–1223.

30.

Demirci

Asci

Demirci

, et al. Impedance ratio: a novel marker and a powerful predictor of mortality in hemodialysis patients. Int Urol Nephrol 2016; 48: 1155–1162.

31.

Fan

Davenport

The importance of overhydration in determining peritoneal dialysis technique failure and patient survival in anuric patients.

Int J Artif Organs 2015; 38: 575–579.

32.

Fiedler

Jehle

Osten

, et al. Clinical nutrition scores are superior for the prognosis of haemodialysis patients compared to lab markers and bioelectrical impedance. Nephrol Dial Transplant 2009; 24: 3812–3817.

33.

Hoppe

Schwermer

Klysz

, et al. Cardiac Troponin T and Hydration Status as Prognostic Markers in Hemodialysis Patients. Blood Purif 2015; 40: 139–145.

34.

Jotterand Drepper

Kihm

Kalble

, et al. Overhydration Is a Strong Predictor of Mortality in Peritoneal Dialysis Patients - Independently of Cardiac Failure. PLoS One 2016; 11: e0158741.

35.

Kim

Choi

Lee

, et al. Extracellular Fluid/Intracellular Fluid Volume Ratio as a Novel Risk Indicator for All-Cause Mortality and Cardiovascular Disease in Hemodialysis Patients. PLoS One 2017; 12: e0170272.

36.

Kim

Jeon

Kim

, et al. Overhydration measured by bioimpedance analysis and the survival of patients on maintenance hemodialysis: a single-center study. Kidney Res Clin Pract 2015; 34: 212–218.

37.

Koh

Wong

, et al. Normalized bioimpedance indices are better predictors of outcome in peritoneal dialysis patients. Perit Dial Int 2011; 31: 574–582.

38.

Mathew

Abraham

Vijayan

, et al. Body composition monitoring and nutrition in maintenance hemodialysis and CAPD patients–a multicenter longitudinal study. Ren Fail 2015; 37: 66–72.

39.

Oei

Paudel

Visser

, et al. Is overhydration in peritoneal dialysis patients associated with cardiac mortality that might be reversible? World J Nephrol 2016; 5: 448–454.

40.

O'Lone

Visser

Finney

, et al. Clinical significance of multi-frequency bioimpedance spectroscopy in peritoneal dialysis patients: independent predictor of patient survival. Nephrol Dial Transplant 2014; 29: 1430–1437.

41.

Onofriescu

Siriopol

Voroneanu

, et al. Overhydration, Cardiac Function and Survival in Hemodialysis Patients. PLoS One 2015; 10: e0135691.

42.

Paniagua

Ventura

Avila-Diaz

, et al. NT-proBNP, fluid volume overload and dialysis modality are independent predictors of mortality in ESRD patients. Nephrol Dial Transplant 2010; 25: 551–557.

43.

Paudel

Visser

Burke

, et al. Can Bioimpedance Measurements of Lean and Fat Tissue Mass Replace Subjective Global Assessments in Peritoneal Dialysis Patients? J Ren Nutr 2015; 25: 480–487.

44.

Segall

Mardare

Ungureanu

, et al. Nutritional status evaluation and survival in haemodialysis patients in one centre from Romania. Nephrol Dial Transplant 2009; 24: 2536–2540.

45.

Segall

Moscalu

Hogas

, et al. Protein-energy wasting, as well as overweight and obesity, is a long-term risk factor for mortality in chronic hemodialysis patients. Int Urol Nephrol 2014; 46: 615–621.

46.

Siriopol

Hogas

Voroneanu

, et al. Predicting mortality in haemodialysis patients: a comparison between lung ultrasonography, bioimpedance data and echocardiography parameters. Nephrol Dial Transplant 2013; 28: 2851–2859.

47.

Siriopol

Voroneanu

Hogas

, et al. Bioimpedance analysis versus lung ultrasonography for optimal risk prediction in hemodialysis patients. Int J Cardiovasc Imaging 2016; 32: 263–270.

48.

Siriopol

Voroneanu

, et al. Predictive abilities of baseline measurements of fluid overload, assessed by bioimpedance spectroscopy and serum N-terminal pro-B-type natriuretic peptide, for mortality in hemodialysis patients. Arch Med Sci 2017; 13: 1121–1129.

49.

Tangvoraphonkchai

Davenport

Pre-dialysis and post-dialysis hydration status and N-terminal pro-brain natriuretic peptide and survival in haemodialysis patients.

Int J Artif Organs 2016; 39: 282–287.

50.

Tian

Wang

, et al. The standard deviation of extracellular water/intracellular water is associated with all-cause mortality and technique failure in peritoneal dialysis patients. Int Urol Nephrol 2016; 48: 1547–1554.

51.

Chertow

Johansen

Lew

, et al. Vintage, nutritional status, and survival in hemodialysis patients. Kidney Int 2000; 57: 1176–1181.

52.

Di Iorio

Cillo

Cirillo

, et al. Charlson Comorbidity Index is a predictor of outcomes in incident hemodialysis patients and correlates with phase angle and hospitalization. Int J Artif Organs 2004; 27: 330–336.

53.

Fein

Chattopadhyay

Paluch

, et al . Enrollment fluid status is independently associated with long-term survival of peritoneal dialysis patients. Adv Perit Dial 2008; 24: 79–83.

54.

Maggiore

Nigrelli

Ciccarelli

, et al. Nutritional and prognostic correlates of bioimpedance indexes in hemodialysis patients. Kidney Int 1996; 50: 2103–2108.

55.

Pillon

Piccoli

Lowrie

, et al. Vector length as a proxy for the adequacy of ultrafiltration in hemodialysis. Kidney Int 2004; 66: 1266–1271.

56.

Pupim

Caglar

Hakim

, et al. Uremic malnutrition is a predictor of death independent of inflammatory status. Kidney Int 2004; 66: 2054–2060.

57.

Arrigo

Von Moos

Gerritsen

, et al. Soluble CD146 and B-type natriuretic peptide dissect overhydration into functional components of prognostic relevance in haemodialysis patients. Nephrol Dial Transplant 2018; 33: 2035–2042.

58.

Avram

Fein

Borawski

, et al. Extracellular mass/body cell mass ratio is an independent predictor of survival in peritoneal dialysis patients. Kidney Int Suppl 2010; 117: S37–S40. PMID: 20671743.

59.

Bansal

Zelnick

Himmelfarb

, et al. Bioelectrical Impedance Analysis Measures and Clinical Outcomes in CKD. Am J Kidney Dis 2018; 72: 662–672.

60.

Beberashvili

Azar

Sinuani

, et al. Objective Score of Nutrition on Dialysis (OSND) as an alternative for the malnutrition-inflammation score in assessment of nutritional risk of haemodialysis patients. Nephrol Dial Transplant 2010; 25: 2662–2671.

61.

Hecking

Moissl

Genser

, et al. Greater fluid overload and lower interdialytic weight gain are independently associated with mortality in a large international hemodialysis population. Nephrol Dial Transplant 2018; 33: 1832–1842.

62.

Huang

Tsai

, et al. Independent Association of Overhydration with All-Cause and Cardiovascular Mortality Adjusted for Global Left Ventricular Longitudinal Systolic Strain and E/E' Ratio in Maintenance Hemodialysis Patients. Kidney Blood Press Res 2018; 43: 1322–1332.

63.

Mushnick

Fein

Mittman

, et al. Relationship of bioelectrical impedance parameters to nutrition and survival in peritoneal dialysis patients. Kidney Int Suppl 2003; 87: S53–S56. PMID: 14531774.

64.

Rhee

Jang

Shin

, et al. Use of Multifrequency Bioimpedance Analysis in Male Patients with Acute Kidney Injury Who Are Undergoing Continuous Veno-Venous Hemodiafiltration. PLoS One 2015; 10: e0133199.

65.

Voroneanu

Siriopol

Nistor

, et al. Superior predictive value for NTproBNP compared with high sensitivity cTnT in dialysis patients: a pilot prospective observational study. Kidney Blood Press Res 2014; 39: 636–647.

66.

Kang

Choi

Park

, et al. Clinical Significance of the Edema Index in Incident Peritoneal Dialysis Patients. PLoS One 2016; 11: e0147070.

67.

Shu

Liu

Zeng

, et al. The Effect of Overhydration on Mortality and Technique Failure Among Peritoneal Dialysis Patients: A Systematic Review and Meta-Analysis. Blood Purif 2018; 46: 350–358.

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